Behavioral Financ 101

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BEHAVIORAL FINANCE 101

By John Kihn



Kihn / Behavioral Finance 101 / 2

Table of Contents

Behavioral Finance 101 1

By John Kihn 1

Preface: An introduction to behavioral finance as a paradigm shift vs. just another

module within financial economics 6

Why not approach behavioral finance as an additional module or add-on to ‗modern finance‘? 10

References 10

Introduction & setting the stage: discussion and some definitions 13

What is finance? 13

What is, and why, behavioral finance? 15

Three theoretical approaches, one that fits finance (and economics), but it‘s not the one currentlyused (and it should be) 17

References 22

Chapter 1: Are the markets „efficient‟? 23

A few reminders and some themes 27

I leave you with a graph, a few questions, and a piece of advice 31

References 35

Chapter 2: The „rational‟ economic agent 37

References 42

Chapter 3: Efficient Markets and Efficient Market Theory (“EMT”) 45

The assumptions behind ‗market efficiency‘ and the EMH/EMT 46

A brief history and review of ‗market efficiency‘ and the EMH/EMT 54

A few key articles on the EMH/EMT 55

Where do we stand today? 67

The issue with predictability 68

References 80

Appendix A: Two basic logical fallacies in finance and economics 86

Appendix B: Some commonly used excess return measures 92

Chapter 4: Limits to arbitrage or the first „pillar‟ of behavioral finance 98

‗Twin shares‘ or ‗dual-listed companies‘ 109

‗Carve-outs‘ 113




Closed-end funds (―CEFs‖) 120

The issue of absolute vs. relative prices and arbitrage 130

‘Index inclusions‘ or index adds and drops 132

References 136

Chapter 5: Psychology or the second „pillar‟ of behavioral finance 141

The short list of known offenses 143

The relatively long list of known decision making offenses 148

Neuroeconomics – linking the human mind to the market choice/action 153

A SAD example of psychology affecting security pricing 162

References 180

Chapter 6: What do we know about the individuals, agents and institutions who push

financial market prices around (or: Who buys and sells this stuff anyways?)? 186

The ‗smart money‘ – the ‗analysts‘ and Portfolio Managers (―PMs‖) 191

The ‗smart money‘ – the analysts 193

The ‗smart‘ anlaysts – the earnings analysts 198

The ‗smart money‘ – the PMs 203

The personnel filter – a tendency for the adverse selection of PMs – or why aren‘t only rationalarbitrageurs selected to be PMs? 213

Individual investors – the most maligned group 218

References 230 Chapter 7: Bubbles 242

What is a bubble? 245

What does a bubble look like? 253

What is/are the likely cause(s) of financial bubbles? 276

Bursting effects of financial bubbles (especially macroeconomic ones) 285

When is it ‗rational‘ to be ‗irrational‘? 293

Our last best hope – Hedge Funds (―HFs‖) 295

References 302

Chapter 8: When does EMT seem to apply? – The Iceberg 309

References 316

Chapter 9: What could go wrong with financial market prices? 317

The pricing model – discounted present value 317

References 334




Appendix A: Some useful terms to know, especially for this chapter 338

Appendix B: What happened to the U.S. CPI? 347

Chapter 10: Overreaction and Underreaction (overshooting and undershooting) 360

Overreaction – mostly in the medium- to long-run 362

Underreaction – the example of earnings announcements – mostly in the short-run 370

Overreaction and underreaction in the same market at the same time – an EMH/EMT proponent‘s worst nightmare 377

References 381

Chapter 11: Chapter 11 391

The event of bankruptcy or restructuring 392

Bankruptcy prediction and market efficiency 396

Before, during and after bankruptcy 397

Periods of economic distress – when many firms hit the skids (recessionary and depressionaryperiods) 401

Final two comments for Chapter 11 410

References 412

Chapter 12: Illusions 416

‘Money illusion‘ – a brief explanation of the bias or nominal vs. real evaluations 418

Inflation illusion – stocks (accepting the Modigliani-Cohn hypothesis) & real estate 420

Biased interest rate expectations or not – bonds 424 Biased exchange rate expectations or not – exchange rates & the forward discount bias (or twowrongs don‘t make it right) 431

A few final thoughts on inflation and finance illusions 435

References 437

Chapter 13: Descriptive theories in finance 442

A template for descriptive financial markets hypotheses 443

Modigliani-Cohn theory & related hypotheses 444

Closed-end funds (―CEFs‖), and IPO similarities 449

Prospect theory 460

SAD and stock exchange distance from the equator 473

References 474

Chapter 14: Volatility & volume (V & V) – or why so much trading? 480

Volatility 481

Volume 486




References 494

Chapter 15: Corporate events 500

A list of corporate events 502

Concerning the ―power‖ of the event study tests and related issues 506

So, what can we say about corporate event studies? 511

References 512

Chapter 16: Can we learn our way to normative market efficiency? 514

The ‗dual burden‘ 518

How do we learn? 530

Psychological issues and cancellation 534

Correcting cognitive biases and learning – the cases of overconfidence and hindsight bias 535

References 542

Chapter 17: Conclusion 545

References 548




Preface: An introduction to behavioral finance as a paradigm shift vs. just

another module within financial economics

Sometimes ―what you don‘t know can‘t hurt you‖, sometimes it can. I am of the opinion that,

especially during this ‗financial crisis‘, not knowing about what drives financial markets can hurt

you, at least financially. For example, going long the stock market during the up move in a stock

market craze or ‗bubble‘ and not knowing how stocks are valued might be financially beneficial;

but after the peak and subsequent bust it could hurt you. Wouldn‘t it be better just to have some

understanding of what likely causes extreme movements in the prices of stocks, or any other

financial instrument?

If you are of the opinion that true knowledge can be helpful, note that all academically accepted

financial models are wrong1; but that doesn‘t mean they are not possibly useful on some level.

First, an explanation for the first part of that statement: minimally, modern finance is literally

wrong because it has relied almost entirely on financial theories that make explicit and implicit

1 As Fama (1991, p. 1596) noted that: ―we know all models are false.‖ Actually, this seems to be an unattributedquote from the famous statistician (of Box-Jenkins, etc. fame) George E.P. Box: ―Essentially, all models are wrong, but some are useful.‖ My use of this observation is not exactly as Box meant it. He was referring to econometricmodels, I generally am not. My reference is primarily normative financial market models. To the extenteconometrics is always wrong is a matter of degree, whereas, financial models are wrong by construction and/ordesign.




assumptions that are incorrect, if not silly. For example, the Capital Asset Pricing Model (the

―CAPM‖)2 assumes the following3 (among other assumptions):

1. ―Investors are risk-averse individuals who maximize the expected utility of their end-of-

period wealth.

2. Investors are price takers and have homogeneous expectations about asset returns that

have joint normal distributions.

3. There exists a risk-free asset such that investors may borrow or lend unlimited amounts at

the risk-free rate.

4. The quantities of assets are fixed. Also, all assets are marketable and perfectly divisible.

5. Asset markets are frictionless and information is costless and simultaneously available to

all investors.

6. There are no market imperfections such as taxes, regulations, or restrictions on short

selling.‖

Let‘s look at the last assumption first (i.e., #6), are there no taxes or regulations? Clearly there

are. In fact, every assumption listed is incorrect. Yet the aforementioned list of assumptions is

standard. Also, although not listed, it is assumed that all investors are always rational4, which

again is not the case (i.e., in the relatively strict economics sense of the term). Therefore, the

derivation may be mathematically correct, but the proof and results must be wrong. Then why

make such patently absurd assumptions that we know are empirically and/or logically

contradicted? The likely answer is mathematical tractability (i.e., to get an answer). The short

2 See Sharpe, W., ―Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk‖, Journal of Finance, Volume 19, Issue 3 (Sep., 1964), 425-442.3 See, for example, Copeland and Weston (1988, p. 194).4 Patel et al. (1991, p. 232) call it ―an article of faith‖, that economists insist that in the aggregate individuals willreach ‗rational‘ outcomes.




answer, and suspicion, is that getting an answer and using math has outweighed the importance

of dovetailing with empirical reality.5 This book approaches the subject matter of finance from

exactly the opposite direction; that is, it is of the most critical importance to reflect reality not

theory.

Regarding the second part of the statement concerning the usefulness of theories, such as the

CAPM, it is not as clear as the logic and math would suggest. That is, while the CAPM is clearly

wrong, I would submit that it is still useful for equities in particular. In fact, I have found that

some of the more useful financial concepts may be those the furthest from actual market reality.

In addition, at the time of writing this book, the future of finance as an academic discipline, and

even as an industry, has been somewhat clouded by the current ‗financial crisis‘. In fact, some of

the current blame has been placed on financial models that were ‗wrong‘ and professional

economists (both academic and more practitioner oriented) that didn‘t see the crisis coming, have

no idea when it will end, and offer no insightful views as to the conditions under which it will

end.

A rather long, somewhat disjointed quote might be of help:

―We trace the deeper roots of this failure to the profession‟s insistence on constructing models

that, by design, disregard key elements driving outcomes in real-world markets . …

5 Along these lines, Krugman (2009) notes: ―As I see it, the economics profession went astray because economists,as a group, mistook beauty, clad in impressive-looking mathematics, for truth.‖ Of course, ‗beauty is in the eye of the beholder‘. To me, being systematically incorrect is hardly beautiful.




Many economic models are built upon the twin assumptions of ‗rational expectations‘ and a

representative agent. …

The major problem is that despite its many refinements, this is not at all an approach based on,

and confirmed by, empirical research. In fact, it stands in stark contrast to a broad set of

regularities in human behavior discovered in both psychology and what is called behavioral and

experimental economics. … despite all the contradictory evidence …

It is highly problematic to insist on a specific view of humans in economic settings that is

irreconcilable with evidence.‖

Colander et al. (2009, p. 1, 7-8) – ―The Financial Crisis and the Systemic Failure of Academic

Economics‖

In other words, economists, and especially financial economists, have known that their models

are often very wrong, by design, yet insist on their application anyway, with often, and especially

recently, disastrous results. Clearly, something more in line with reality might be more useful.6

6 As Statman (1999, p. 26) points out, essentially ―standard finance‖ expects ―perfect self -control‖ that ―normal people‖ don‘t display; therefore, essentially something closer to reality might be a more useful starting point for a‗model‘.




WHY NOT APPROACH BEHAVIORAL FINANCE AS AN ADDITIONAL MODULE OR

ADD-ON TO ‗MODERN FINANCE‘?

This book doesn‘t try to approach behavioral finance as an add-on to classic finance (as most

currently do) because current and past textbook finance is wrong not just in the output of its

‗models‘, but in its overall approach to finance. The reliance on theoretical models with mostly

patently wrong assumptions isn‘t the problem per say, it‘s largely the approach itself which

encouraged and accepted such models in the first place. I don‘t, and you shouldn‘t; and by the

end of this book I endeavor to explain why.

REFERENCES

Colander, D., Follmer, H., Haas, A., Goldberg, M., Juselius, K., Kirman, A., Lux, T., and B.

Sloth, ―The Financial Crisis and the Systemic Failure of Academic Economics‖. Working Paper,

2009, 1-17.




Copeland, Thomas E.., and John J. Weston, Financial Theory and Corporate Policy (Third

Edition), Addison-Wesley Publishing Company, Inc., 1988.

Fama, E., ―Efficient Capital Markets: II‖, Journal of Finance, Volume 46, Issue 5, 1991, 1575-

1617.

Krugman, P., ―How Did Economists Get It So Wrong?‖, New York Times, September 2, 2009.

Patel, J., Zeckhauser, R., and D. Hendricks, ―The Rationality Struggle: Illustrations fromFinancial Markets‖, American Economic Review, Volume 81, Number 2, Papers andProceedings o the Hundred and Third Annual Meeting of the American Economic Association,May 1991, 232-236.

Sharpe, W., ―Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk‖,

Journal of Finance, Volume 19, Issue 3 (Sep., 1964), 425-442.

Statman, M., ―Behavioral Finance: Past Battles and Future Engagements‖, Financial Analysts

Journal‖, Volume 55, Number 6, November/December 1999, 18-27.







Introduction & setting the stage: discussion and some definitions

All that you learn in finance (and economics) is wrong; but that doesn‘t mean it is all useless. By

the end of this book the reader should be able to understand why this is true and why it may not

matter much.

WHAT IS FINANCE?

Finance is a subset of economics.7

That is, if economics is essentially the study of potentially

unlimited wants/demand in a world with limited resources/supply, then the information,

knowledge, and concepts associated with finance are completely contained within economics.

This is important in that the weaknesses of economics will likely ultimately apply to finance as

well (i.e., to the extent they overlap, which they do). Therefore, any general (and sometimes

specific) issues related to economics are likely impacting finance (and vice-versa).

7 In contrast, Ross (1987) has indicated that he thinks that a large part of modern finance is outside of economics,but ends up being subsumed by economics eventually (i.e., as economics picks up on it).




At its core, finance is relatively simple: it is the study of discounted cash flows, which can also

be called present values. In short, there are only two things that make up discounted cash flows:

1. The cash flows themselves, and

2. The discount rates associated with them.

Therefore, from the simplest model to the seemingly most complicated contingent claims/option

pricing model there are essentially only two practical questions that require actual answers:

1. What is/are the cash flow(s)?

2. What is/are the discount rate(s)?

What could be simpler? But like so many subjects, it is often the case that ―the devil is in the

details.‖

Given that in the real financial markets, for example the equity markets, we are largely

concerned with stock price movements, one should understand the usefulness of understanding

finance through this prism of cash flows and discount rates. The additional benefit is that it

doesn‘t require making wrong or even absurd assumptions to understand what is actually

happening in the actual financial markets; and, therefore, only requires that any theory parallel

what is actually known about one or more financial markets.




WHAT IS, AND WHY, BEHAVIORAL FINANCE?

―All truth passes through three stages. First, it is ridiculed. Second, it is violently opposed. Third,

it is accepted as self-evident.‖

Arthur Schopenhauer

This book doesn‘t try to approach behavioral finance as an add-on to classic finance (as most do)

because current and past textbook finance is wrong not just in the output of its models, but in its

insistence on accepting the incorrect models themselves as correct. It bears repeating, the models

are wrong, but some may be useful to varying degrees. Which brings us to behavioral finance,

and unlike current academic finance, it is reconcilable with the evidence.8 Again, this doesn‘t

mean some of the mathematically derived wrong ―models‖ aren‘t useful on some level. The

point is that, given the actual reality of the financial markets, while the models (to varying

degrees) may be salvageable the approach is not. In short, what is required is an approach that

much better fits the evidence. This, in turn, brings us to behavioral finance.

Behavioral finance is defined as follows9:

8 It has recently been noted (Krugman (2009)): ―But what‘s almost certain is that economists will have to learn tolive with messiness. That is, they will have to acknowledge the importance of irrational and often unpredictablebehavior, face up to the often idiosyncratic imperfections of markets and accept that an elegant economic ‗theory of everything‘ is a long way off. In practical terms, this will translate into more cautious policy advice‖. Although,

especially with regard to ‗policy advice‘, I suspect he doesn‘t practice what he preaches. 9 Barberis and Thaler credit Shleifer and Summers (1990) with identifying these ―two pillars‖ of behavioral finance(limits to arbitrage and investor psychology).Also, ―note that most asset pricing models use the Rational Expectations Equilibrium framework (REE), whichassumes not only individual rationality but also consistent beliefs (Sargent (1993)). Consistent beliefs means thatagents‘ beliefs are correct: the subjective distribution they use to forecast future realizations of unknown variables isindeed the distribution that those realizations are drawn from. This requires not only that agents process newinformation correctly, but they have enough information about the structure of the economy to be able to figure outthe correct distribution for the variables of interest.‖ (Barberis and Thaler (2002, p. 2 footnote #1)) Obviously, few,if any, investors have ―consistent beliefs‖.




―Behavioral finance argues that some financial phenomena can plausibly be understood using

models in which some agents are not fully rational.

The field has two building blocks:

(1) Limits to arbitrage, which argues that it can be difficult for rational traders to undo the

dislocations caused by less rational traders; and

(2) Psychology, which catalogues the kinds of deviations from full rationality we might

expect to see.‖

Barberis and Thaler (2002, p. 1)

I would add to that definition that actually, as it turns out, not ―some‖ but most, if not all,

―financial phenomena can plausibly be understood using models in which some agents are not

fully rational.‖ It is important to note that it is likely without limits to arbitrage the psychology

part might be of limited interest. That is, it is likely that limits to arbitrage are a necessary but

possibly not sufficient condition for psychology to impact pricing in the financial markets.

Therefore, the reader should rightfully be convinced empirically and logically that there are

limits to arbitrage before he or she accepts that psychology has a significant impact on pricing.

Finally, I wouldn‘t stress the ‗models‘ part of the first part of the definition, and that to the extent

behavioral ‗models‘ are developed they, by definition, should emphasize reality.




THREE THEORETICAL APPROACHES, ONE THAT FITS FINANCE (AND ECONOMICS),

BUT IT‘S NOT THE ONE CURRENTLY USED (AND IT SHOULD BE)

According to Bell et al. (1988) there are three kinds of theories of decision making under

uncertainty10:

1. Normative theories state how agents should behave (i.e., typically as ‗rational‘ agents).

2. Descriptive theories describe how agents actually behave.

3. Prescriptive theories advise agents on how to behave when confronted with their own

cognitive limitations.

More to the point, economics has been and is largely a ‗normative‘ field of study (e.g., game

theory, etc.). It spends most of its time (and research), and often implicitly, concerned with how

economic agents should behave. Remember, finance is a subset of economics and is therefore

another normative field of study (at least currently11). Now contrast economics and finance with

psychology, which has been and is largely a ‗descriptive‘ field of study. In psychology,

psychology researchers often first observe what humans do, then develop theories and related

models. In finance (and economics) it is often the opposite. In finance and economics researchers

assume economic agents behave in certain ways (e.g., rationally) and markets, etc. function in

certain ways (e.g., ―efficiently‖), then a model and/or theory is proposed. Which should better fit

the field of finance, normative or descriptive? My answer would be that, clearly, finance should

be primarily a descriptive field of study.

10 Especially see Bell et al. (1988, pp. 9-30).11 Olsen (2001, p. 54) has indicated that he considered finance prior to ‗modern finance‘ (circa 1951 -1952) to be a―descriptive discipline‖. If that is accepted, then it went from descriptive to normative around the early 1950s, towhere it has stayed until at least the writing of this book. Either way, it is has been principally normative sincearound the time it was recognized as its own discipline (i.e., around the 1950s).




It is absurd for finance (and economics) to organize itself as a normative field of study when its

natural design should12 be descriptive. Given that finance (and economics) has largely turned

down a dead end street, behavioral finance is a natural paradigm shift for finance.13

Again, its

two pillars are limits to arbitrage (which depends largely on actual market microstructure) and

psychology (which in some cases can directly depend on things like human biology and related

natural science). Psychology has and is largely a descriptive endeavor; whereas limits to

arbitrage is largely an area of finance that was, until more recently, an area of finance that was

neglected because it was assumed not to matter (e.g., think ―efficient markets‖, ―no free lunch‖,etc.). The actual reality of the financial markets is that it is best suited for descriptive study, not

normative. Unfortunately, most financial economists insist that behavioral finance is some add-

on to the larger study of normative financial economics. It is not. Finance by its very nature is a

naturally descriptive field of study, not normative. There are actual people and markets that can

be observed. There is limited or no need in finance to theorize in a vacuum. We can observe

many discounted present values or financial asset prices and their movements and levels are

increasingly difficult to reconcile with normative theories. Might it be time to switch to a more

descriptive field of study, or just call a naturally descriptive field of study for what it is?

As an aside, I have mentioned that some financial ‗models‘ may be useful, even if they are

clearly wrong (e.g., mathematically). It is the dichotomy between normative and descriptive that

12 This, of course, is a normative statement. My rationale for making this statement is empirically driven. To wit,finance is the study of discounted cash flows, which in turn are influenced by economic agents, driven by humanpsychology and related things, which are very different from say the laws of physics. This should become clearer bythe end of the book.13 In response to where economics should now go in the future (i.e., given that it has miserably and catastrophicallyfailed), even Krugman (2009) states: ―There‘s already a fairly well developed example of the kind of economics Ihave in mind: the school of thought known as behavioral finance.‖




largely drives my rationale. First, something like the CAPM is a normative theory. Few, if any,

of its explicit or implicit assumptions are realistic. It is a contrived bit of math for actual markets

that cannot, by definition, truly reflect that reality, but possibly by accident. Even so, to the

extent that actual humans drive actual prices away from normative theory (which they do), but

those prices eventually reflect something close to the contrived math (which they might), then

using those ―wrong‖ models might be of some prescriptive use (that is, assuming they do, which

they might not). Hopefully this will become clearer to the reader as the book progresses and

these and related issues are addressed. The important takeaway is that not all wrong models in

finance are as useful, and it can depend on the model and timeframe we are looking at.

The aforementioned brings us to the point of mentioning the prescriptive part of this book

specifically and behavioral finance in general. In short, given that actual humans don‘t often

follow (if ever) the insights and forecast of normative models, and we can descriptively observe

what they actually do in the actual markets, then there may be room to guide those humans with

prescriptive advice, or at least offer some. This book will offer some prescriptive advice to better

allow those humans that so desire to make more optimal financial market decisions (i.e., ceteris

paribus from a wealth maximization perspective) than those documented by empirical research

(with or without the guidance of normative models). In effect, what this book will try to do is

help guide actual agents in what is currently a normative field of study by focusing more on

descriptive proof and applying a modicum of logic.

To summarize up to this point:




1. Everything you will learn in economics and finance that is based on normative theory is

wrong (which is most, if not all, of economics and finance).

2. Although, even though those largely ―normative‖ theories are wrong (i.e., empirical

reality is not generally supportive) it does not mean that they are not useful (the key is

in what ways & conditions under which they might be useful and especially which ones

might be useful).

3. But there is a circularity problem. Specifically, because most of us humans are biased14

and irrational (specifically, in ways that affect pricing in the financial markets) it would

seem that it is likely that we need tools (be they models and/or theories) to guide us in our

decision making. Without a normative toolkit, we are likely to continue to mess up

pricing in the financial markets. But because we are so messed up (i.e., from a normative

perspective, e.g., ―hindsight bias‖) we tend to use normative tools in a way that only

reinforces our biases (e.g., in searching for confirming evidence we may use the

normative based tools of statistics to confirm our biased belief).

The key is the last point (#3). We must be careful to use tools and techniques to avoid costly

wealth minimizing decisions that we, as humans in the financial markets, are prone to do.

Because we are so messed up (i.e., from a normative economics perspective) we need normative

based tools like statistics, for example; but because we are so messed up we tend to use any tool

and pervert it in an attempt to ―rationalize‖ our irrational judgment and beliefs. Hindsight bias

and the search for confirming evidence are examples of this. I can think of what seems like to me

14 Most humans seem hardwired for denial. Essentially our emotions (particularly the way we register information)can overwhelm our cognitive (the way we organize information) thoughts. In particular, it tends to be difficult for usto unemotionally approach investing, and even science itself. Once we hold some view, for example that the marketsare ―efficient‖, we tend to have an emotional vested interest in keeping that view alive and well. This makes itespecially difficult to breaking out of this circle of thoughts and emotion.




an endless string of events in my working life where I have witnessed firsthand examples of

this.15 In fact, we tend to pervert these tools when we need them most (e.g., during bubbles we

tend to use the ―new paradigm‖ argument more than during ―normal‖ times; thus, for example,

suggesting that our typical valuation techniques no longer apply).16

In the final analysis, learning/training is the only way out of this, but, given how messed up we

are as humans (i.e., specifically in terms of normative finance and economics), learning for most

is best done in a strict organizational setting where feedback and motivation are present (which is

atypical in the ―real world‖, let alone in the financial markets). This should be clearer as the book

progresses, but keep in mind that care and concentration are critical in avoiding typical pitfalls in

training one-self to avoid typically human actions and reactions in the financial markets.

15 One example is the term ‗hedge‖. By hedge academics typically mean two largely offsetting positions often meantto reduce risk, whereas most practitioners I have observed only refer to hedging when they have a loss they canblame on it. Therefore, for many, if not most, practitioners a hedge is an unexpected loss, which is very differentfrom the standard textbook definition.16 Again, our need to deny that which we have a vested interested in perpetrating, even if the interest is onlyemotional and not economic and/or logical, makes it especially difficult to break out of this circle of bias and denial.




REFERENCES

Barberis, N., and R. Thaler, ―A Survey of Behavioral Finance‖, NBER Working Paper #9222,

Addison-Wesley Publishing Company, Inc., September 2002, 1-78.

Bell, David E., Raiffa, Howard, and Amos Tversky (edited by), Decision Making (Descriptive,

normative, and prescriptive interactions), Cambridge University Press, New York, N.Y., 1988.


Olsen, R., ―Professor Burrell‘s Proposal for a Behavioral Finance: Some Reflections‖, Journal of

Psychology and Financial Markets, Volume 2, Number 1, March 2001, 54-56.

Ross, S., ―Finance and Economics: The Interrelations of Finance and Economics: Theoretical

Perspectives‖, American Economic Review, Volume 77, Number 2, May 1987, 29-34.

Sargent, Thomas, Bounded Rationality in Macroeconomics, Oxford: Oxford University Press,

1993.

Shleifer, A., and L. Summers, ―The Noise Trader Approach to Finance‖, Journal of Economic

Perspectives, Volume 3, Number 2, Spring 1990, 19-33.




Chapter 1: Are the markets „efficient‟?

I advise all students in economics when asked an open ended question to, as a rule, begin their

answer with ―it depends‖.17 Therefore, regarding ‗market efficiency‘, are the markets efficient?

Generally the answer is no, but that depends primarily on your definition of efficiency. More

recent (and normative) tortured definitions will tend to cloud the issue. That said, the most

common textbook definition is that of Fama (1970, p. 383):

―… security prices at any time ‗fully reflect‘ all available information. A market in which prices

always ‗fully reflect‘ available information is called ‗efficient‘.

… efficient markets model. … First, weak form tests, in which the information set is just

historical prices, are discussed. Then semi-strong form tests, in which the concern is whether

prices efficiently adjust to other information that is obviously publicly available (e.g.,

announcements of annual earnings, stock splits, etc.) are considered. Finally, strong form tests

concerned with whether given investors or groups have monopolistic access to any information

relevant for price formation are reviewed. We shall conclude that, with but few exceptions, the

efficient markets model stands up well.‖18

17

Bill McTague suggested this to me in 1985.18 Remember, to quickly incorporate the information means that those receiving the news late shouldn‘t be able toprofit (e.g., reading it in newspapers or company reports). To correctly incorporate the information means that theprice adjustment in response to the news should be accurate on average (i.e., no overreaction or underreaction to thenews). Also, since a security‘s price should equal its value, prices should not change without any news about thevalue of the security (i.e., prices should not react to changes in supply or demand of a security that are notaccompanied by news about its fundamental value). Stale information is relatively easy to define (the ‖weak form of EMH‖ defines it as past prices and returns, the ‖semi-strong form of the EMH‖ defines it as any publicly availableinformation, and the ‖strong form of the EMH‖ defines it as insiders‘ information or information not publiclyavailable).




First, note there is essentially one general hypothesis (i.e., ―security prices at any time ‗fully

reflect‘ all available information‖) and three more specific ones (i.e., ―weak‖, ―semi-strong‖, and

―strong‖ forms). Henceforth, I will refer to the general hypothesis as the Efficient Market

Hypothesis (―EMH‖) and that larger set of hypotheses and theory as the Efficient Market Theory

(―EMT‖). Thus, I will refer to this set as the EMH/EMT19.

Therefore, the textbook ‗efficient markets‘ theory or model has at least three testable hypotheses:

1. Weak form – All past prices or information are incorporated into prices.

2. Semi-strong form – All publically available information is incorporated into prices.

3. Strong form – All information, whether public or private, is incorporated into prices.

Note, the EMH/EMT doesn‘t just state that information should be reflected into prices, but that it

should correctly reflect the information. In short, there shouldn‘t be too much reaction or too

little, but just the right amount. For example, a headline and story that conveys no fundamentally

19 ‗Modern finance no longer seems to be able to identify the basic difference between a hypothesis and a theory.For the following go to Wikpedia.com – ―Karl Popper‗s … demands falsifiable hypotheses, framed in such amanner that the scientific community can prove them false (usually by observation). According to this view, ahypothesis cannot be ‗confirmed‘; because there is always the possibility that a future experiment will show that it isfalse. Hence, failing to falsify a hypothesis does not prove that hypothesis: it remains provisional. However, ahypothesis that has been rigorously tested and not falsified can form a reasonable basis for action, i.e., we can act asif it is true, until such time as it is falsified. … In science a theory is a testable model of the manner of interaction of a set of natural phenomena, capable of

predicting future occurrences or observations of the same kind, and capable of being tested through

experiment or otherwise verified through empirical observation. … In common usage, the word theory is oftenused to signify a conjecture, an opinion, a speculation, or a hypothesis. In this usage, a theory is not necessarily

based on fact; in other words, it is not required to be consistent with true descriptions of reality. … According to theUnited States National Academy of Sciences, some scientific explanations are so well established that no newevidence is likely to alter them. The explanation becomes a scientific theory. In everyday language a theory means ahunch or speculation. Not so in science. In science, the word theory refers to a comprehensive explanation of an

important feature of nature that is supported by many facts gathered over time. Theories also allow scientiststo make predictions about as yet unobserved phenomena.‖ Therefore, based on standard accepted ‗scientific‘definitions, the EMH is no longer a hypothesis and the EMT has never been a theory (i.e., the evidence hasfalsified the hypothesis (i.e., the EMH), and it has not been in support of the theory). Actually, the EMT/EMH isreally neither hypothesis nor theory. It used to be a hypothesis (i.e., in the Popper sense of being testable, but notsince the 1990s or so, since the tests falsified it) but is no longer.




significant information concerning XYZ Company should not affect the price of the stock of the

XYZ Company. Based on Fama (1970), the markets are not efficient by any stretch of a rational

person‘s imagination. In fact, a careful review of the key early empirical work in the field

contradicts the assertion made in the Fama quote at the time the quote was made. Since 1970,

there has been a flood of evidence against the original definition of ‗market eff iciency‘. In fact,

the original more normative definition has been and continues to be modified as evidence has

tended to reject all three more specific hypotheses. Somewhat ironically, the EMH/EMT has

turned from a more normative theory to a more descriptive one, some might even argue it has

become a form of religious ‗faith‘ (see, Ross (1987, p. 33)). ―After all, finance has progressed

very far by having a faith – some would say religious but I prefer to think of it as a proven first-

order approach to problems – in the broad efficiency of markets.‖20

At this point, the notion of ―arbitrage‖ should be mentioned. Standard finance (and economics)

relies heavily on the notion that ―arbitrage‖ corrects any prices that differ from the ―efficient‖

price (i.e., one that reflects all past and public information, and possibly some or all private

information). For example, assume we know the true economic or fundamental price of a share

of IBM is $100 yet the price is $90. The traditional argument is that one or more arbitrageurs

will enter the market for IBM shares and continue buying shares until the price is driven up to

$100. Conversely, if the price is $110 per share, one or more arbitrageurs will enter the market

20 Additionally, Patel et al. (1991, p. 232) state: ―For most economists it is an article of faith that financial marketsreach rational aggregate outcomes, despite the irrational behavior of some participants, since sophisticated playersstand ready to capitalize on the mistakes of the naïve. (This process, which we call poaching, includes but is notlimited to arbitrage.) … Descriptive decision theory, especially psychology (see D. Kahneman et al. (1982) can helpto explain such aberrant macrophenomena.‖ Indeed, the one part I would correct is that most and not ―some participants‖ display ‗irrational‘ behavior.




and short shares until the price is driven down to $100 per share. Note for upcoming chapters,

critical to this description is that:

There are no limits to arbitrage (e.g., no taxes, transaction costs, etc.). Yet there are. We

will review some evidence in a forthcoming chapter.

Only one arbitrageur is required. That is, standard financial theory works at the margin.

Thus, we only require one rational arbitrageur to offset a theoretically unlimited number

of investors willing to accept a mispricing. We will show at least one case were this not

only didn‘t happen, but the likely arbitrageurs helped to pushed the price away from the

―efficient‖ price.

We know what the true fundamental or economic price is; when in fact we rarely, if ever,

do. We will review the evidence in forthcoming chapters.

In short, the standard arbitrage story hardly fits what actually occurs in the markets and we have

plenty of descriptive evidence to show this. Finally, note, the EMH specifically and the EMT

more generally rely on arbitrage or the normative academic equivalent of ‗all hell breaks loose.‘




A FEW REMINDERS AND SOME THEMES

Before proceeding it may be useful to warn against what psychologists call implicit and explicit

processing21

(i.e., as it concerns the EMH/EMT). Implicit processing is more automatic vs.

explicit which requires more cognitive effort and training. Regarding the EMH/EMT, the

evidence would suggest one thing, but most of what is learned keeps telling you that you

explicitly process that evidence by ignoring it. Don‘t do it! If so, then reconcile your implicit and

explicit processing of the EMH/EMT by searching for evidence and trying to make up your mind

by yourself without the author‘s or anyone else‘s input. Because the academic field of finance

still relies on normative models (like the EMH/EMT) that are wrong, it spends a great deal of

energy dismissing the empirical evidence in favor of the normative theory itself (e.g., investors

should maximize their own risk adjusted returns vs. what we observe is that investors often

don‘t).

A list of themes:

• The markets are not efficient (not in the Fama (1970) sense of markets fully reflecting all

available information), but it is difficult/tricky taking advantage of this. Thus, market

inefficiency is the norm, not the anomaly or exception.

• Some reasons why ‗market efficiency‘ doesn‘t dominate is that there are (1) limits to

arbitrage, and (2) it is still hard to say that one can make risk-free excess returns (i.e.,

some form of risk is typically encountered along the way and it may not be simple to

21 Psychologists have shown that there are two different types of processing systems — the implicit and the explicit.Implicit processing – automatic and unconscious (e.g., face recognition). Explicit processing – more ―evolved‖ andrequires effort and control (e.g., performing calculus); and it can suppress the implicit processing (e.g.,brainwashing). Note: There can be conflicts, but that in general is usually not healthy.




measure or observe, e.g., irrational trader or ―noise trader risk‖). Therefore, due to

realistic costs & other constraints and risk misspecification there really is ‗no free lunch‘;

in fact, the ‗free lunch‘ may be a kind of normative illusion.

• Agents/actors in the financial markets are not generally the ‗rational‘ economic beings

described by normative finance and economics. In fact, we are only generally rational in

more of a dictionary sense, not that which normative economics has defined. In short,

based on the economics and finance strict normative definition, we are not ‗rational‘ at

all; rather we tend toward the irrational.

• Arbitrage still bounds the markets, but is typically risky in the real world and limited in

other ways. In short, what looks like a ‗free lunch‘ may turn out to be difficult to eat.

• Behavioral explanations are key and accurate at the micro level (i.e., in terms of the root

plausible causation), but tend to be more nuanced and complicated as financial market

data is aggregated.

• Whether purely academic or practitioner, you would be wise to study the behavioral

aspects of finance.

Always keep in mind that behavioral finance is not a panacea for the ‗anomalies‘ that have been

observed in the financial markets, but it is useful, if not true. Behavioral finance builds upon

individual studies (more micro foundations) and works toward the markets, while traditional

finance has tended to work in the other direction (observing first then fitting square pegs into

assumed square holes that sometimes turn out to be round – think Fama). Often EMH/EMT

promoters have been and are focused on the symptom not the cause. Of course, as in medical




care, often the cure is derived by focusing on the cause, not the symptom (e.g., for mutual funds,

the market typically makes the manager).

Also, a few words should be devoted to the normative importance of individual rationality

(which is a basic assumption embedded in most financial models). Note that the rational

expectations equilibrium framework ―assumes not only individual rationality, but also consistent

beliefs (Sargent (1993)).22 Consistent beliefs mean that agents‘ beliefs are correct:‖ (and maybe

that investors apply Bayes‘ law correctly). Therefore, economic agents need to both process

information correctly and have enough information (as well as the right model). Bounded

rationality typically assumes investors are initially limited in their information set (e.g., investors

do not initially know the growth rate of an asset, but learn as best they can from the data). Thus

bounded rationality tends in the same direction of more strict rationality with typically similar

results. Behavioral finance typically relaxes the assumption of strict rationality. Thus behavioral

finance is unique in that respect, and reflects descriptive reality.

Finally, I wish to make statement that should make much more sense to the reader by the end of

the book: there may appear to be „free lunches‟, but no truly free lunch . As evidence is

reviewed it should become clear that the original EMH/EMT emphasis on information being

embedded in pricing in an ‗efficient‘ manner is not very helpful in describing actual financial

22 Also, as Shiller (1990, p. 55) points out, rational expectations models collapse both the model economic agentsuse and the one they use to generate their expectations into one ‗elegant‘ model. That is, by assuming people‘sexpectations are always optimal, we don‘t have to worry about them. Additionally, he notes this is a ―grossoversimplification‖ done for tractability. Reality is not a strong suit of normative economics. In my opinion therational expectations assumption is just another ridiculous simplifying assumption of normative economics, madeprincipally for mathematical tractability (i.e., to get an answer, not to enhance the odds of that answer reflectingreality). The original academic reference, regarding individuals always having exactly the same predictions as the―relevant economic theory‖, is Muth (1961).




market reality. Incidentally, this suggests there are ‗free lunches‘ to be had in those markets. This

is doubtful. Even though there may appear to be ‗free lunches‘ it is likely these are artifacts of

things like limits to arbitrage and/or the inability to measure the true economic ‗risk(s)‘

associated with the financial asset or liability examined.

I will now proceed in the following way:

1. Review of rationality.

2. Summary/review of key early evidence and theory supposedly in favor of the EMH/EMT

and list examples of mostly predictable ―free lunch(es)‖.

3. Limits to arbitrage – Pillar I.

4. Psychology and its links to finance and financial decision making – Pillar II.

5. What we know about the agents.

6. ‗Bubbles‘.

7. The true anomaly (i.e., when prices are ‗right‘ and under what conditions).

8. A rough measure of how ‗wrong‘ price levels can be.

9. ‗Overearction‘/‘underreaction‘.

10. Bankruptcy.

11. Illusions.

12. Descriptive theories.

13. Volume & volatility.

14. Corporate events.

15. Can we expect to learn our way out of this?




I will cover the core pieces and provide actual evidence. Again, given the nature of finance, the

approach will be more descriptive than normative.

I LEAVE YOU WITH A GRAPH, A FEW QUESTIONS, AND A PIECE OF ADVICE

What follows is a graph with two lines that should be of some interest to the reader:




Based on monthly data, the graph covers the period January 1997 through July 2009. The blue

line represents the annual total return on the Standard & Poor‘s 500 equity index (S&P 500).

Note, that arguably, and especially over the time period examined, the S&P 500 is world‘s best

known stock index. As an example of reading data points on the S&P 500 return line, around

May 2009 (time is on the horizontal axis) a person who had invested in the S&P 500 from May

of 2008 and held through May of 2009 would have lost about 40% of his or her investment

(annual return for the S&P 500 is on the left vertical axis). The red line represents Wall Street

Strategists (―WSSs‖) recommended stock allocation. As an example of reading data points on the

WSSs recommendation line, on May 2009 they were recommending an about 52% allocation to

50%

55%

60%

65%

70%

75%

-45%

-40%

-35%

-30%

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

J a n - 9

7

M a y - 9

7

S e p - 9

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M a y - 9

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S e p - 9

8

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S e p - 9

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0

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9

Wall Street 'Strategists' and Market Timing

SPX

STALSTOX




stocks (recommended stock allocation is on the right vertical axis – therefore, 48% outside of

stocks, or equivalently 100% - 52%). First, note that WSSs are some of the best paid and most

listened to people in the actual financial markets. Second, note that what is generally important is

not the level of their stock allocation but when they increase or decrease their allocation (e.g.,

from 50% to say 60%, or vice versa, i.e., up or down). In short, they always recommend some

stock allocation and it generally has varied between 50 and 70 percent. Third and most

importantly, note that the stock market tends to go in the opposite direction of their

recommendations, with a lag.

23

In fact, it is an excellent signal for timing the stock market; it‘s just that you must systematically do the opposite of what the ―experts‖ ar e telling you.24

You may be asking yourself some version of the following question: How can you be that

consistently bad and keep your job (or possibly: How does one get that job?)? Could it be that

maybe, just maybe, they actually know something about the stock market, or how else could they

have achieved consistently negative timing ability? Could it be that maybe, just maybe, they are

paid to essentially lie? The reader might then ask: Lying isn‘t exactly what could be considered a

highly paid skill, is it? Furthermore, how does the EMH/EMT fit into this? Answer: It really

doesn‘t? Remember, the EMH/EMT is about information getting embedded into pricing in an

23 More recently, that is since the ‗financial crisis‘ began after July-August 2007; this has not been the case. In this

author‘s opinion, it is likely some structural changes in the industry threaten this relationship. 24 See, for example, Fisher and Statman (2000). They also find a significant negative relationship between small,individual investors‘ sentiment and future S&P 500 returns. Note that Fisher and Statman (2000) find significance atthe 5% level; and if the projection is longer term and the regression equation is expanded one can find statisticalsignificance well beyond the 1% level. Also note that going back in time as far as data has been kept on WSSs‘stock allocations (December, 1985), the relationship holds. In essence, it is not a far stretch to argue that WSSs havesome notion of whether the stock market will be going up or down, they just tell investors to do the opposite. Thus,investors would be well advised to do the opposite of what they tell them to do.Even ‗superstar‘ money managers tend to have no useful insight (e.g., see Desai and Jain (1995), regarding stock recommendations).




‗efficient‘ manner; it is not about wrong/false information impacting pricing and/or behavior. In

fact, the more the average person or economist looks at such a relationship the more they might

be tempted to ignore it altogether as an ‗anomaly‘. But I would recommend being careful, WSSs

and the like aren‘t anomalous, but rather closer to normal with respect to what actually happens

in the recent world of actual finance. The reader now has the rest of this book to be convinced

that this sort of thing isn‘t anomalous, but is quite ordinary. And, yes, additionally it contradicts

the ―semi-strong form‖ of market efficiency (i.e., you shouldn‘t be able to use public information

to forecast security prices (in this case, the U.S. stock market itself).

The aforementioned then brings us to our first bit of prescriptive advice:

As a general rule, do the opposite of what WSSs advise. Therefore, if WSSs advice

increasing your stock allocation, consider lowering it; and if they advise decreasing your

stock allocation, consider increasing it. Actually it‟s a fair thing to say that any prescriptive

advice from Wall Street, and related firms, should be minimally ignored and possibly

turned on its head.25

25 The best way to consider this is to imagine you are a hen chicken and you ask a fox for advice on living in theproverbial hen house. If the fox advises you to say step out of the hen house and go to his or her den, what wouldyou do? Do you follow the fox‘s advice to probably be never seen again, or do you do the opposite?




REFERENCES

Desai, H., and P. Jain, ―An Analysis of the Recommendations of the ‗Superstar‘ Money

Managers‖, Journal of Finance, Volume 50, Issue 4, September 1995, 1257-1273.

Fama, E., ―Efficient Capital Markets: A Review of Theory and Empirical Work‖, Journal of

Finance, Volume 25, Issue 2, Papers and Proceedings of the Twenty-Eighth Annual Meeting of

the American Finance Association New York, N.Y. December, 28-30, 1969 (May, 1970), 383-

417.

Fisher, K., and M. Statman, ―Investor Sentiment and Stock Returns‖, Financial Analysts Journal,

Volume 56, Issue 2, May/April 2000, 16-23.

Muth, J., ―Rational Expectations and the Theory of Price Movements‖, Econometrica, Volume

29, Number 3, July 1961, 315-335.

Patel, J., Zeckhauser, R., and D. Hendricks, ―The Rationality Struggle: Illustrations from

Financial Markets‖, American Economic Review, Volume 81, Number 2, Papers and

Proceedings o the Hundred and Third Annual Meeting of the American Economic Association,

May 1991, 232-236.

Ross, S., ―Finance and Economics: The Interrelations of Finance and Economics: Theoretical

Perspectives‖, American Economic Review, Volume 77, Number 2, May 1987, 29-34.




Sargent, Thomas, Bounded Rationality in Macroeconomics, Oxford: Oxford University Press,

1993.

Shiller, R., ―Speculative Prices and Popular Models‖, Journal of Economic Perspectives, Volume

4, Issue 2, Spring 1990, 55-65.




Chapter 2: The „rational‟ economic agent

Let us begin at the more general definition and move to the more specific. That is, we will begin

with a dictionary definition of rationality and then lay out what economists (general and

financial) mean when they write or speak of ―rationality‖. Also, note that r ationality is a

necessary, but not sufficient, condition for ‗modern‘ economics (and finance) to have a fighting

chance of being applicable. What you will learn in this section is that as it is defined by

economists (and financial economists), it isn‘t applicable. It bears repeating, if the strict

economic definition of rationality doesn‘t hold most economic ―models‖ (i.e., those currently

taught and in your textbooks, at least at the time of this writing) don‘t strictly hold (i.e.,

tautologically or mathematically, and likely descriptively).26

First, a dictionary definition of rational:

―rational: adj. 1. based on or agreeable to reason: a rational decision. 2. exercising reason: a

rational negotiator. 3. sane; lucid: The patient seems rational. 4. Math. …‖

Webster‘s College Dictionary (1998)

What‘s to argue with there? Contrast that seemingly innocuous definition with that relied on by

finance (and economics): Rational behavior is consistent behavior that maximizes an individual‘s

satisfaction (i.e., utility). It rests on the following three assumptions (see McKenzie and Lee

(2006, p. 100)):

26 This is especially true of those models that assume ―all expectations are optimal forecast‖, such as made by the―rational expectations‖ modelers (see, e.g., Shiller (1975) for a critique).




1. The individual has an identifiable preference (within limits/constraints) as to

what he or she wants.

2. The individual is capable of ordering his or her wants consistently (i.e., most to

least preferred).

3. The individual will choose consistently from these ordered preferences to

maximize his or her satisfaction (i.e., utility).

None of the three fundamental assumptions consistently hold for most people in the actual

world.

27

With respect to rationality, and particularly economic rationality and assumed

consistency, the principal problem is that humans‘ actions and decisions tend to be context

dependent. Specifically, I may have nicely ordered preferences under one set of conditions, yet

revise and/or reverse those under another. For example, when the price of IBM shares is

generally increasing I may be a buyer, yet when it is dropping I may turn into a seller, ceteris

paribus. Unfortunately, finance models generally don‘t allow for this very human behavior. This

version of strict rationality is a key assumption for all of economics, especially something like

finance (which is a subset of economics) where agents are trading financial assets on a regular

basis. Most of us break all of these assumptions.

Descriptively, humans in the financial markets often change their preferences and are anything

but consistent in ordering their wants and preferences. Currently, mainstream economics (and

finance) does not recognize natural human inconsistency, but holds to a very tight definition of

rationality that does not accurately describe decision making and the subsequent actual price

27 See, for example, Tversky and Simonson (1993). That article concerns the context dependent nature of actualhuman preferences vs. those assumed by standard utility theory (which is foundational for economics).




setting in most financial markets most of the time. While the assumption based theory of choice

assumes stable and consistent preferences, the reality is context dependent preferences and

values.28

Preferences and values are constructed dependent on context and ―these constructions

are contingent on the framing of the problem, the method of elicitation, and the context of

choice.‖ (Tversky and Simonson (1993, p. 1187)) In short, nothing, or almost nothing, about the

28 Or example, Tversky and Simonson (1993): Context-dependent preferences contradict the standard theory of choice, based on value maximization. Value maximization suggests that the highest value option is always chosen.Often the choice between x and y, for example, is influenced by a third option z, and the value of an option can be

increased by enlarging the offering set (i.e., a violation of regularity: implies P( x, R) ≥ P(x, S)).Therefore, the independence of irrelevant alternatives is violated. In short, most of the underlying assumptions of Utility Theory are violated. For example, a liberal candidate x may defeat a conservative candidate y in a two personrace, but lose if another liberal candidate is included (i.e., get fewer votes). Or, for example, the introduction of atop-of-the-line camera is expected to reduce the market share of a midline camera more than the share of a basiccamera. Tradeoff Contrast not only applies to a single attribute, such as size, but also to the tradeoff betweenattributes (e.g., price and quality). Background Context example (Simonson and Tversky (1992)) – tires (price andmileage warranty) and books (coupons). For tires, first exposed to a small change in price/large change in mileagewarranty vs. another group first exposed to the opposite, tended to select less expensive tires and vice versa. LocalContext (market share increase example by Huber et al. (1982), also, Simonson and Tversky (1992)) - $6 vs. high-end Cross pen (64% chose cash), then a cheap pen introduced (now, the Cross pen increased from 36% to 46%,contrary to regularity). Extremeness Aversion (Kahneman et al (1991), Tversky and Kahneman (1991)) – Gains andlosses are defined relative to a neutral reference point that generally corresponds to the decision maker‘s status quoor current endowment. In some situations, however, decision makers may evaluate options in terms of their

advantages and disadvantages, defined relative to each other (disadvantages loom large). As a consequence, optionswith extreme values within an offered set will be relatively less attractive than options with intermediate values(Extremeness Aversion Hypothesis), which gives rise to tow effects: compromise (take the middle option if there isa symmetric form of extremeness aversion) and polarization (if extremeness aversion is with respect to one attributeonly, e.g., radios of varying quality).The findings of tradeoff contrast and extremeness aversion, which violate the assumption of value maximization,have both theoretical and practical implications. Context is clearly important (in perception as well as choice) andpeople tend to complicate rather than simplify. This is unfortunate for models of consumer behavior based onstandard definitions of rationality/logic.Kahneman and Tversky (1979) – Prospect theory is an alternative descriptive model of decision making under risk (i.e., as opposed to expected utility theory). People tend to underweight outcomes that are merely probable incomparison with outcomes that are obtained with certainty (i.e., the certainty effect). This effect contributes to risk aversion in choices involving sure gains and to risk seeking in choices involving sure losses. For example, a sure

loss of – 100 vs. the following: a 50% chance of losing 300 and a 50% chance of gaining 90 (i.e., -150 + 45 = -105).Also, people tend to discard components that are shared by all prospects under consideration (i.e., the isolationeffect). This leads to inconsistent preferences when the same choice is presented in different forms (for example, thegamble example above).Kahneman and Tversky (1992) – Cumulative prospect theory employs cumulative rather than separable decisionweights. Two principles, diminishing sensitivity and loss aversion, are invoked to explain the characteristiccurvature of the value function and the weighting functions. Fourfold pattern of risk attitudes: (1&2) risk aversionfor gains and risk seeking for losses of high probability, and (3&4) risk seeking for gains and risk aversion for lossesof low probability.




standard assumption laden description of decision making under uncertainty actually applies to

what economic agents actually do in the financial markets.

Finally, there are six ‗axioms‘ (they are also called the ‗von Neumann/Morgenstern Axioms‘29 or

‗Savage Axioms‘) of ‗expected utility theory‘ that mathematically summarize the economic

foundation of ‗rationality‘ (i.e., as used by economists and financial economists)30:

Axiom 1: Comparability – For any pair of investment opportunities, A and B, one of the

following must be true: the investor prefers A to B, B to A, or is indifferent between A and B.

Axiom 2: Transitivity – If A is preferred to B, and B is preferred to C , than A is preferred to C .

Axiom 3: Continuity – If investment outcome A is preferred to B, and B to C , then there is some

probability P such that the investor would be indifferent between the certain event B and the

uncertain event {P * A + (1 – P) * C }.

Axiom 4: Independence – If an investor is indifferent between the certain outcomes A and B,

and C is any other certain outcome, then the investor is also indifferent between the uncertain

events

{P * A + (1 – P) * C } and {P * B + (1 – P) * C }.

29 Financial economics is grounded on the paradigm of expected utility of wealth. Frankfurter and McGoun (2001, p.422-423) state that ―in excess of 60,000 ‗scientific‘ papers both published and presented before learned societies, is

exclusively based on the VM axioms.‖ Axiom 6 is explicitly assumed by Markowitz (1952) (and it is hard to get bywithout assuming the first four), and Sharpe‘s derivation of the CAPM is based on Markowitz‘s results, plushomogeneous expectations, etc.Also, given that axioms 5 & 6 depend on axioms 1 through 4, and the people seems to violate 1 through 4,essentially humans violate all of these axioms. Finally, Shiller (1998, p. 4) thinks: ―The axioms (Savage, 1954) fromwhich expected utility theory is derived are undeniably sensible representations of basic requirements of rationality.‖ Again, being sensible in the dictionary sense doesn‘t make them true. 30 The original references are von Neumann & Morgenstern (1944 & especially the 1947 2nd edition with appendixcontaining the axioms of utility) and Savage (1954). Frankfurter and Phillips (1994) provided the actual six axiomslisted.




Axiom 5: Interchangeability – If an investor is indifferent between two uncorrelated risky

income streams, then the securities that produce them are interchangeable in any investment

strategy – simple or complex.

Axiom 6: Risk Aversion – If securities A and B offer the same positive rate of return, R = X ,

with probabilities Pa and Pb, respectively, and otherwise R = 0 with probabilities (1 – Pa) and (1

– Pb), respectively, then A is preferred to B if Pa > Pb. Moreover, one‘s relative preference for A

in this case is a (possibly complex) monotonic function of the relative certainty coefficient Pa / Pb

(Frankfurter and Phillips (1994, p. 7)).

Like much concerning economics and ‗rationality‘, the six listed axioms seem normatively

sensible but are descriptively wrong. If an axiom is defined as a ―self evident or universally

recognized truth‖, then the aforementioned six axioms are not axioms at all, but falsifiable

assumptions that can in fact be falsified. For example, ask yourself, do all humans all the time

actually follow the six ‗axioms‘ listed? If they don‘t, which they don‘t, then all that flows

mathematically from them is wrong.




REFERENCES

Frankfurter, G. and H. Phillips, Forty Years of Normative Portfolio Theory, JAI Press,

Greenwich, CT, 1994.

Frankfurter, G., and E. McGoun, ―Anomalies in finance: What are they and what are they good

for?‖, International Review of Financial Analysis, Volume 10, 2001, 407-429.

Huber, J., Payne, J., and C. Puto, ―Adding Asymmetrically Dominated Alternative: Violations of

Regularity and the Similarity Hypothesis‖, Journal of Consumer Research, Volume 9, June 1982,

90-98.

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Chapter 3: Efficient Markets and Efficient Market Theory (“EMT”)

An EMT economist and his companion are walking down the street one day when they come

upon a $100 bill lying on the ground. The companion immediately reaches down to pick it up,

whereupon the economist abruptly stops him and says: ‗Don‘t bother, if it was a real $100 bill,

someone would have already picked it up.‘ They resume their march down the street with the

companion looking back over his shoulder and the economist looking directly ahead.

Taken/modified from: Findlay and Williams (2000, p. 195)

Most academics, and many practitioners, assume that ‗if there is no free-lunch, then the markets

are ‗efficient‘.‘ This is not logical or descriptively true. Based on the commonly accepted finance

definition of ‗market efficiency‘ (i.e., Fama (1970)), the markets appear to contain many ‗free-

lunches‘ and yet they are clearly ‗inefficient‘. In addition, I would assert, that what might appear

to be a free-lunch, is generally an artifact of the definition of market efficiency and/or the

‗model‘ used to control for ‗market risk‘31.

Furthermore, the assumption that there is no ‗free‘ $100 bill lying around, because if that was

true ―someone would have already picked it up‖, presupposes or assumes that there are no limits

to picking it up in the real world (translated, no limits to arbitrage), which there are. We will

review some ‗free lunches‘ and some of the limits to ―picking them up‖ in upcoming chapters.

31 It might be added/emphasized that neither of which financial economists currently agree upon.




Again, note that the limits to arbitrage are a necessary, but not a sufficient, argument for the

psychology piece to matter.

THE ASSUMPTIONS BEHI ND ‗MARKET EFFICIENCY‘ AND THE EMH/EMT

First, it is important to remember that the EMH is a direct consequence of equilibrium in

competitive markets with fully rational investors (for the mathematical origin of this see

Samuelson (1965) and Mandelbrot (1966)32). As defined by economists, most, if not all, of us are

not ‗fully rational‘. Therefore, the results of the mathematical derivations assuming such are

wrong.

Even ignoring the descriptive contradiction with ‗full rationality‘, according to Shleifer (2000),

three arguments and series of assumptions underlie the EMH:

32 Although, it should be noted that Mandelbrot (1966) expanded the definition beyond ―random walk‖ to includemartingales. Essentially, even though Samuelson is credited with the mathematical definition of ‗efficient markets‘,it is Mandelbrot‘s more general definition that the EMH/EMT as evolved into (particularly as ‗anomalies‘ haveessentially eliminated the more easily dismissed ‗random walk‘ version of the EMH and seriously compromised or destroyed what is left of the EMT, i.e., depending on your view of these things).




1. Investors are assumed rational and hence to value securities rationally (i.e., in addition to

being ‗fully rational‘, security valuation is always assumed „rational‘).

2. To the extent that some investors are not rational, their trades are random and therefore

cancel each other out without affecting prices (also known as ‗cancelation‘).

3. To the extent that investors are irrational in similar ways, they are met in the market by

rational arbitrageurs who eliminate their influence on prices.

In addition, there are three key empirical implications and issues associated with the EMH:

1. When news about the value of a security hits the market, its price should react and

incorporate this news both quickly and correctly.

2. Therefore, not just quick and accurate reaction to fundamental information, but non-

reaction to non-information.

3. Keys are defining ‗stale‘ information and adjusting for risk (if not defining it).

Let‘s begin at the top with the part concerning investors being assumed ‗rational‘, remember that

financial economists (and economists generally) don‘t use the dictionary definition of ―rational‖.

With respect to rationality and human behavior, economists assume that which doesn‘t exist. For

example, Friedman (1953, p. 14) is often quoted in defense of this fact: ―Truly important and

significant hy potheses will be found to have ‗assumptions‘ that are wildly inaccurate descriptive

representations of reality, and, in general, the more significant the theory, the more unrealistic

the assumptions.‖33 Regarding the EMH/EMT specifically, none of the three assumptions or

33 In this famous essay Friedman pointed out that, of course, this was only true as far as the theory with the wildlyincorrect assumptions (i.e., that wildly differed from actual reality) was excellent at predicting/forecasting(essentially he applied a version of Occam‘s razor). Hardly something that describes economics (and financialeconomics) even some half century later (i.e., at this time), especially based on normative models. Also, this is




arguments strictly holds in all markets all the time (actually not most of the time in most); and

none of the three empirical implications seems to hold. Could this be that the theory isn‘t

‗significant‘ enough, just plain wrong, or both? Again, we come back to the descriptive reality of

the financial markets vs. traditional normative theory.

As De Bondt (1996, p. 185) stated:

―The psychological analysis of preference and belief indicates that it is not possible in general

to reconcile normative and descriptive accounts of individual choice. The reason for this

conclusion – which may be regarded by some as pessimistic or even negative – is that decision-

making is a constructive process. In contrast to the classical theory that assumes consistent

preferences, it appears that people often do not have well-defined values, and that their choices

are commonly constructed, not merely revealed, in the elicitation process. Furthermore, different

constructions can give rise to systematically different choices, contrary to the basic principles

that underlie classical decision theory.‖

Again, ―it is not possible to reconcile normative and descriptive accounts of individual choice.‖

Therefore, while there may be specific cases where a rational economic individual consistently

almost the opposite of the ‗hard sciences‘ like chemistry and physics. The irony or likely contradiction here is thatnormative economics has pushed math and wildly incorrect assumptions under the methodological theory that only

the forecasts matter (actually, ―fruitfulness‖ and ―simplicity‖). In fact, the forecasts suck vs. say particle physicswhere the predictions/forecasts are extraordinarily precise (i.e., from an economists viewpoint) yet theory in suchdescriptively oriented fields is grounded in empirical reality. Which brings us back to my point about economics(and finance) being more conducive to descriptive theories vs. the current normative emphasis with methodological justifications made that rely on principally beliefs that forecast accuracy will be coming any day now. This isespecially true in such sub-fields of economics such as macroeconomics, where predictions and reality may not evenhave the same sign. This is ironic on two levels: (1) the article by Friedman appeared in a larger body of work thatwas pushing the notion that economics was/is/will be a positive science; whereas Friedman emphasized thenormative, the book interpreted positive to mean descriptive. (2) There is no known true ‗science‘ that emphasizesthe wilder the assumptions, the better the theory.




displays coherence and invariance, these individuals are not average people acting in a manner

we normally observe.

Regarding the second assumption concerning irrational trades/traders cancelling each other out

due to their inherent randomness (also called ‗cancellation‘), there is little empirical reason to

believe this. In fact, a case will be shown where the exact opposite of ‗cancellation‘ occurs.34

That is, in many cases (and possibly most) ‗irrational‘ (i.e., based on traditional economics

definitions of rationality) traders/agents often push prices further away from their fundamental

economic basis (i.e., efficiency).

Regarding ‗irrational‘ traders being met in the market by ‗rational‘ arbitrageurs, I believe this is

the key argument that could hold, but empirically doesn‘t seem to. This is also why I have noted

that limits to arbitrage is a necessary condition for the psychology part to matter (i.e., for

behavioral finance to have predictive power). There is no doubt that descriptively there are

rational (or maybe it would be more accurate to call them less irrational) economic agents who

stand ready to push prices back toward more fundamentally justified or efficient pricing; but it

would seem that they are often foiled by various limits to arbitrage in the actual financial

markets. This is especially where the response ―it depends‖ applies. For example, it may be the

case that the nature of limits to arbitrage may be very different in the market for foreign

34 The one case/area where cancellation may have been found to occur is racetrack betting (Camerer (1998)); butthen again upon reading that case I doubt it. For example, the bet is made and then withdrawn, which wouldlogically tend to show no eff ect anyway. In addition, there is some evidence that for ―maiden‖ horses even this canimpact betting patterns.




currencies than it is for say domestic small-cap stocks; and this seems to indeed be the case, but

it can also change over time, depending on a host of factors.

Much of financial economics relies on the notion/story that ‗rational‘ arbitrageurs (note that

rational and arbitrageur is essentially synonymous in finance) will always and anywhere (i.e.,

irrespective of market or time) correct mispricing. Finance theory (e.g., Friedman (1953) and

Fama (1965)) relies on arbitrageurs. Whether over- or under-priced from fundamental value,

arbitrageurs are assumed to bring the price back in line. The corollary is that relative to their

rational peers, irrational investors lose money. But what if the reverse holds? Furthermore, what

if sometimes and in some markets more ‗rational‘ investors made money at the expense of

‗irrational‘ investors and in the very same markets (at other times) they lose money. Is it possible

that ―it depends‖ on the market and timing in that market whether the ‗irrational‘ or ‗rational‘

tend to set pricing and/or make money at the expense of the other?35 This book aims to show that

the normal efficient market narrative can sometimes apply, but normally it does not. If that is

true empirically then the nuanced and a much more behavioral narrative/story is more applicable.

Regarding the empirical implications, prices don‘t incorporate ―news both quickly and

correctly‖, and they react to non-information. We will review examples of both of these basic

contradictions to the EMH/EMT.

35 If the arbitrage narrative well describes financial market reality, one would expect that one should find a set of systematically good market timers.




Regarding adjusting for risk, we would have to have an accepted model to do that. We don‘t;

therefore, adjusting for risk is somewhat incongruously impossible in finance at this time. Worse

yet, remember, all finance models are wrong. Essentially, finance has become so seemingly

sensitive or touchy regarding the breadth and scope of so called ‗anomalies‘ that it no longer

cares to even open itself up basic hypothesis testing. Fama (1970) pointed out that there is a

dependence of most tests of market efficiency on a model of risk and expected return. It seems

that as ‗anomalies‘ have been found, critics have argued that the market model used was wrong

(even if they recommend and/or recommended the model). As De Bondt (1995, p. 9) pointed out:

―Thus, the sad but honest truth is that modern finance theory offers only a set of asset-

pricing theories for which no empirical support exists and a set of empirical tests for which

no theory exists.‖

By now the reader may be coming around to understand why behavioral finance is really an

alternative methodology, because we really don‘t need more ―asset-pricing theories for which no

empirical support exists‖.

Therefore, again, never forget, we need:

1) To know what true fundamental or economic value is.

2) One or more fully rational well informed arbitrageurs must correct any mispricing (i.e.,

when pricing deviates from efficient fundamental value).

3) Failing 1 and/or 2, irrational trades must cancel each other out.

Or else we are in trouble. In fact, it seems that the prime argument/condition in old or newer

versions of the EMH/EMT is that fully rational well informed arbitrageurs correct any




mispricing (i.e., when pricing deviates from fundamental value) and/or irrational trades cancel

each other out. Assuming we know the true definition of fundamental, economic, or intrinsic

value (i.e., the true economic pricing model for each financial asset), it is both necessary and

sufficient for this to happen under the EMT for the ―efficient‖ pricing of financial assets.

Therefore, without the ability for one or more fully rational economic agents/actors correcting

mispricing (again, assuming we know what that is) at the margin (and assuming the margin is

always where prices are set) and if irrationally based trades do not cancel each other out, the

EMT pricing argument breaks down (i.e., at least in theory, if not actually), and in reality it does.

First, with respect to irrationally based trades canceling each other out, there is little or no

evidence in psychology or empirical finance to suggest this happens in any meaningful way (e.g.,

see Tversky and Kahneman). In fact, the empirical finance research and clinical psychological

research would strongly suggest that the opposite happens (i.e., irrational human biases tend to

either remain or be reinforced). Although, it is possible that irrational traders can learn their way

out of this (i.e., such that biased pricing will either be reduced or ideally disappear). But given

the current structure of the finance industry combined with human nature, this is currently not the

case as well as unlikely to happen any time soon.

Second, therefore the sole argument that remains (i.e., in order for the EMT/EMH to hold in any

meaningful way) is that fully rational well informed arbitrageur(s) is/are left to correct any

deviations from ―efficient‖ asset pricing. As Kahneman (1996, p. 203) stated: ―The assumption

that agents are rational is central to much theory in the social sciences. Its role is particularly




obvious in economic analysis, where it supports the useful corollary that no significant

opportunity will remain unexploited.‖ Again, as it turns out, it seems there are some well

informed arbitrageurs (maybe not always so ‗rational‘), but they do not set pricing in all markets

at all times. It would seem that in most financial asset markets most of the time it is some

combination of well informed arbitrageurs and less rational traders that set pricing (and this is at

least in part determined by the breadth and depth of the limits to arbitrage in the particular asset

market under consideration). Therefore, ‗it depends‘, at least in part, on limits to arbitrage.

That is, for example (and assuming we know the true pricing model, which we don‘t):

1) Investors don‘t seem to act strictly rational.

2) Irrational trades don‘t seem to cancel each other out.

3) Rational arbitrageurs seem to have limited impact, or even encourage mispricing.

Therefore, the EMH/EMT is descriptively challenged, and actually rejected, by financial

market(s) reality.36

36 Given that irrational trades tend not to cancel each other out and fully rational arbitrageurs do not determinepricing in all financial markets all the time, the reliance of finance on normative mathematical models where strictrationality is implicitly or explicitly assumed seems misguided at best and counterproductive at worst. Furthermore,based on the Fama (1970) definition of ―market efficiency‖, due to the biased and seemingly irrational pricing thatseems to occur in even the most liquid financial markets much of the time, it seems rather odd that finance modelscontinue to rely on mathematical tractability (e.g., the ability to integrate or differentiate) over the actual descriptivereality of the markets, that is, unless they do so out of mathematical tractability itself.




A BRIEF HISTORY AND REVIEW OF ‗MARKET EFFICIENCY‘ AND THE EMH/EMT

―The idea behind the term ‗efficient markets hypothesis‘, a term coined by Harry Roberts

(1967), has a long distinguished history in financial research, a far longer history than the term

itself has. The hypothesis (without the words efficient markets) was given a clear statement in

Gibson (1889), and has apparently been widely known at least since then, if not long before.‖

Shiller (1998, p. 1)

The more modern precursors to EMT

37

and the EMH itself can be traced to Friedman (1953) and

Fama (1965) as well as mathematically to Samuelson (1965) and Mandelbrot (1966).

That is, with Fama (1970) being used as the primary definition of the EMH and a broader body

of work setting the foundation for EMT.

―The efficient markets theory reached the height of its dominance in academic circles around the

1970s. Faith in this theory was eroded by a succession of discoveries of anomalies, many in the

1980s, and evidence of excess volatility of returns. Finance literature in this decade and after

suggests a more nuanced view of the value of the efficient markets theory, and, starting in the

1990s, a blossoming of research on behavioral finance. … Wishful thinking can dominate

much of the work of a profession for a decade, but not indefinitely.‖

Shiller (2002, pp. 1, 3)

37 Remember, the EMT argument is an ideal that was accepted as empirical fact some forty years ago. But eventhough it is descriptively wrong, that doesn‘t mean it isn‘t useful in some way (e.g., measuring when pricing isn‘t―efficient‖). Today, it is really a question of how useful it is, not whether it describes reality per say.




Essentially, efficient markets are a very good idealized representation of what we should

normatively strive for, but it is not a reflection of reality. It is the direct empirical contradictions

that have lead to psychological analysis being added to finance; but, again, this has been done

without modifying the fundamental method or approach of finance (or economics), which is still

normative.

A FEW KEY ARTICLES ON THE EMH/EMT

―Every finance professional employs the concept of market efficiency. The theory, evidence and

counter-evidence focus on a couple of dozen highly influential articles published during the

twentieth century.‖

Dimson and Mussavian, (1998, p. 91)

Considering its pervasiveness in the field of finance, it is rather surprising that finance textbooks

do not review at least a few of the critical articles supposedly responsible for the EMH/EMT.

There are at least two reasons to review some of the key historical works in the area of the




EMH/EMT: (1) as stated, they are important to understanding ‗modern finance‘, and (2) ignoring

other more plausible interpretations, the empirical articles are not exactly in full support of the

theory. Thus, even decades ago, the normative theory of finance has never matched its

descriptive reality. In chronological order, we will now do so here.

Bachelier‘s (1900) dissertation is considered to be the first mathematical finance paper, and he

could be considered the first normative finance academic. Thus, even though that dissertation

was essentially discovered by Cootner, and published in English by him in 1964, it can be

considered the first normative finance paper. Bachelier anticipated the Wiener process of

Brownian motion and noted that ―past, present and even discounted future events are reflected in

market price, but often show no apparent relation to price changes.‖ Bachelier was the first to

formally state the ―Random Walk Hypothesis‖ (―RWH‖ – which is essentially a mathematical

representation of Fama‘s ‗weak-form‘ EMH, i.e., past prices are incorporated into current prices)

and lay the foundations for the EMH/EMT. It is not an overstatement to suggest that over half

the early literature in finance associated with questions of the EMH were concerned with the

RWH and related issues covered by Bachelier‘s dissertation (although most at the time were

probably unaware of it).38

Pearson (1905) came up with the term and formula for ―random walk‖. Pearson (1905, p. 342) –

―The lesson of Lord Rayleigh‘s solution is that in open country the most likely place to find a

drunken man who is at all capable of keeping on his feet is somewhere near his starting point!‖

38 Bachelier was not discovered until after his death. Thus credit was not given until well after his death. In addition,the basis for option pricing and/or contingent claims analysis can be credited to him. It may be worth noting that hedied an untenured academic in an unremarkable college in France, and without much money to his name.




Hence, the ‗random walk‘ and the notion that returns are serially independent. Rayleigh‘s

response to Pearson‘s (1905, p. 318) question was: ―If n be very great, the probability sought is:

.‖ Pearson‘s (1905, p. 294) question was: ―A Man starts from a point O and walks l

yards in a straight line. He repeats this process n times. I require the probability that after these n

stretches he is at a distance between r and from his starting point, O.‖ Thus, the ―random

walk‖ turns out to be a mathematical description of how a drunken man walks across a field, but

ends up being applied to the process by which financial asset prices evolve over time.39

Cowles (1933) – An empirical study on stock market forecasting/forecasters (essentially stock

recommendations by various finance companies) over 4-1/2 years ending July 1932 (beginning

January 1928).40 He concluded (1933, p. 324): ―the most successful records are little, if any,

better than what might be expected to result from pure chance. There is some evidence, on the

other hand, to indicate that the least successful records are worse than what could reasonably be

attributed to chance.‖ (e.g., of the 16 financial service companies and 20 fire insurance

companies, the average common stock underperformed by 1.43% and 1.20% per year,

respectively). Therefore, Cowles provides the first empirical proof that ‗smart money‘ isn‘t (i.e.,

smart).41

That noted, he does find systematic evidence that although the stock market forecasters

seem unable to systematically ―beat the market‖, some systematically lose. Thus, the evidence is

not unambiguously supportive of randomness.

39 The EMH (‖weak form‖) reduces to the RWH (i.e., ―prices have no memory‖), but goes beyond that with the―semi-strong‖ and ―strong‖ forms. 40 Note, this is a very volatile period and it contains the famous crash of 1929.41 As an aside, it is astonishing to me the lengths to which EMH/EMT promoters will go to rationalize what seemsalmost impossible to rationalize (e.g., Barsky and De Long (1990) using ―smart money‖ earnings analysts‘ forecastsof earnings to justify the notion that there is no such thing as an asset bubble, specifically U.S. equities).




Working (1934) is the first to directly show what a random series looks like and then check

commodities (Cowles was looking at stocks and stock pickers). Working (1934) showed that a

―random-difference series‖ might look non-random. Specifically, he stated that (1934, p. 12):

―An outstanding characteristic of a series of this type is that its changes are largely random and

unpredictable.‖ After presenting ―experimental‖ random-difference series, he presented a wheat

price series and stated (1934, p.24): ―I find that to the important extent that wheat prices

resemble a random-difference series, they resemble most closely one that might be derived by

cumulating random numbers drawn from a slightly skewed population of standard deviation

varying rather systematically through time.‖ Thus, again, the evidence is not purely supportive of

randomness.

Cowles and Jones (1937) extended Cowles‘ (1933) results to other economic series (e.g., stock

price indices over various periods of time – 26 different ones were considered) and they included

transaction costs. They concluded that (1937, p. 294): ―This type of forecasting could not be

employed by speculators with any assurance of consistent or large profits. On the other hand, the

significant excess of sequences over reversals for all units from 20 minutes up to 6 months, with

the exception of units of 2 weeks and 3 weeks mentioned previously, represents conclusive

evidence of structure in stock prices.‖ Therefore, they may be the first to f ormally indicate the

existence of an ‗anomaly‘.42

42 To me, it is quite amazing that Cowles and Jones (1937) found an ‗anomaly‘ (actually, many of them), yet most of the work after them ignores this.




Cowles (1944) was a direct extension of his 1933 study (he now had ten to 15-1/2 years of data

from eleven ―leading financial periodicals‖). The conclusions regarding ‗professional‘ stock

market forecasters were as follows (Cowles (1944, p. 214)): (1) Over the entire sample of eleven

forecasting periodicals, they ―fail to disclose evidence of ability to predict successfully the future

course of the stock market.‖ (2) ―The record of the forecasting agency with the best results …

3.3 per cent per year better … than the Dow-Jones industrial average … capital-gains tax might

wipe out most of this advantage.‖ (3) Even though the stock market was in a ‗bear market‘ over

most of the period under study and lost about 2/3rds of its value, ―more than four times as manywere bullish as bearish (of the 6,904 forecasts recorded).‖ Although the study is probably the

first to analyze stock newsletters, with essentially the result that they are mostly negative market

timers, he didn‘t point this out. The early EMH papers are specifically focused on one tailed

tests, when in fact it should have been about two tailed tests (i.e., positive or negative timing).

Also, key to the Cowles (1944) study is that in the conclusion he mentions that the market has

―structure‖ (i.e., it seems inefficient in some way, but the newsletters don‘t key on this statistical

anomaly, instead they forecast/recommend by feel or ‗seat of the pants‘). Specifically, he again

states (1944, p. 214): ―While prospects for the speculator are, therefore, not particularly alluring,

statistical tests disclose positive evidence of structure in stock prices which indicates a likelihood

that whatever success may be claimed for the very consistent 40-year record is not entirely

accidental. A simple application of the ‗inertia‘ principle43, such as buying at turning points in

the market after prices for a month averaged higher, and selling after they have averaged lower,

43 Cowles (1944) proposed a trading rule (called the ‗inertia‘ rule). This may be the first academic reference to aquantifiable trading rule, yet it ignored (e.g., academics pushing the EMH did not point this out).




than for the previous month, would have resulted in substantial gains for the period under

consideration.‖ In short, he found predictable structure.

Kendall and Hill (1953) analyzed 22 weekly time series (twenty stock series and a wheat and

cotton series) and fitted time series models to them. They summarized their findings by stating

(1953, p. 11): (1) ―In series of prices which are observed at fairly close intervals the random

changes from one term to the next are so large as to swamp any systematic effect which may be

present. The data behave almost like wandering series. (2) … difficult to distinguish by statisticalmethods between a genuine wandering series and one wherein the systematic element is weak.‖

(3) Therefore, ―trend fitting … is a highly hazardous undertaking. …, but it may be impossible to

discriminate between quite different hypotheses which all fit the data.‖ (4) … ―aggregate index

numbers behave more systematically than their components.‖ (5) ―Unless individual stocks

behave differently from the average of similar stocks, there is no hope of being able to predict

movements on the exchange for a week ahead without extraneous information.‖ (6) Although not

emphasized, he found significant serial correlations (see p. 34). This article is odd; they

emphasized the noise in the data, but go ahead and analyze it regardless.44 They push the notion

that due to the noise and overall structure of the time series one shouldn‘t try to make heads or

tails of it (points #2 & #3), but do so anyway. Most importantly, they find structure in the time

series (e.g., p. 23), but dismiss it. Then a discussant (Professor Cox, pp. 32-33) points out: ―It is

worth pointing out explicitly that the presence of certain types of non-randomness is shown,

not the occurrence of high serial correlations, but by other features of the correlogram, such as

44 As a general rule, the noisier the return series (or any series for that matter) the harder it is to make sense of theseries (i.e., even if there was indeed something to be made sense of to begin with). Thus, and furthermore, ‗noisetraders‘ can turn financial data from useful to useless by the simple act of trading on the noise they create.




the existence of a run of coefficients all with the same sign. For example, if the successive

differences of price were completely random, price itself would undergo a simple random walk

and would not be stationary. If there were in addition some tendency to return to a stable level of

price, the correlations of differences would be small and negative (see, for example, Table 2, last

column.)‖ Note, this was the wheat price series, which went from serial correlations of – , +, +,

then the next seven were negative. In responding to Cox, Kendall (p. 34) stated: ―… agree with

him except on one point. He calls attention to patterns of signs in the serial correlations which

indicate that, although small, the correlations are not haphazard. I think he is probably right, but I

do not regard the point as settled beyond all doubt.‖ Again, the research finds structure, but it is

largely ignored, or just dismissed.45

Roberts (1959) was a follow up to Kendall and Hill (1953) where he (1959, p. 3) describes ―the

chance model more precisely,‖ and discusses its ―common-sense interpretation‖. Roberts (1959)

is really a theoretical discussion/argument of why ―chance‖ (random) describes particularly well

stock price movements, both short-run and long-run (see p. 6-7). He goes through various

arguments (e.g., smart money removing obvious patterns).

Working‘s (1960) note on the correlation of first differences of averages in a random chain

showed that autocorrelation could be introduced into a series by using time averaged security

45 I do like his final quote (p. 34) in response to a discussant saying their results are silly without ―an acceptabletheoretical framework‖: ―I have tried to elicit certain facts about economic series. They may be wrong, but if theyare correct they are facts, irrespective of any theoretical framework.‖ Those are words to live by, but obviouslylargely lost on normative frameworks (e.g., ‗modern finance‘).




prices (he also gave some approximate magnitudes for this effect). Essentially, he warned about

taking differences and then averaging (i.e., you may think you have something, when there isn‘t).

Based on a general stochastic model, Samuelson (1965, p. 41) in his ―Proof That Properly

Anticipated Prices Fluctuate Randomly‖ really set up the notion that in competitive markets

where buyer and seller settle on one price to transact: ―‘If one could be sure that a price will rise,

it would have already risen.‘ Arguments like this are used to deduce that competitive prices must

display price changes over time … that perform a random walk with no predictable bias.‖ Also,into his model is embedded the notion of a ‗fair game‘/martingale property (i.e., zero expected

capital gain is replaced by a fair rate of return). Interestingly, he states at one point (p. 45): ―The

theorem is so general that I must confess to having oscillated over the years in my own mind

between regarding it as trivially obvious (and almost trivially vacuous) and regarding it as

remarkably sweeping. Such perhaps is characteristic of basic results.‖ In short, Samuelson in

large part laid the mathematical foundation for the normative notion of ‗market efficiency‘.

Fama (1965) essentially published his dissertation. In it he extensively reviewed and empirically

tested ―the ‗random walk‘ model of stock price behavior‖ and summarized the primary result as

follows (p. 34): ―The main conclusion will be that the data seem to present consistent and strong

support for the model. This implies, of course, that chart reading, though perhaps an interesting

pastime, is of no real value to the stock market investor. This is an extreme statement and the

chart reader is certainly free to take exception. We suggest, however, that since the empirical

evidence produced by this and other studies in support of the random-walk model are now so




voluminous, the counterarguments of the chart reader will be completely lacking in force if they

are not equally well supported by the empirical work.‖ Although targeting so called ―technical

analysis‖, little effort was made to dispute contrary evidence to the RWH. Fama‘s (1965, p. 34)

main question was: ―To what extent can the past history of a common stock‘s price be used to

make meaningful predictions concerning the future price of the stock?‖ He described the theory

of the random-walk (p. 34) as: ―By contrast the theory of random walk says the future path of the

price level of a security is no more predictable than the path of a series of cumulated random

numbers. In statistical terms the theory says that successive price changes are independent,

identically distributed random variables. Most simply this implies that the series of price changes

has no memory, that is, the past cannot be used to predict the future in any meaningful way.‖

Fama (1965, p. 35) stated: ―The theory of random-walks in stock prices actually involves two

separate hypotheses: (1) successive price changes are independent, and (2) the price changes

conform to some probability distribution. We shall now examine each of the hypotheses in detail.

…‖ Fama (1965, p. 41) noted that Osborne (1959) essentially independently derived Bachelier‘s

result almost fifty years later (―assuming that price changes from transaction to transaction are

independently, identically distributed random variables.‖). Fama (1965) also noted what he

called the Mandelbrot hypothesis about stock prices not being normally distributed (empirically a

Paretian, not a Guassian distribution), and on p. 87 noted ―some evidence of that large changes

tend to be followed by large changes of either sign‖.46Therefore, counter evidence was found

even for the RWH.

46 What is especially odd is that he essentially taunts ―chartists‖, then later on claims they may be responsible for the―efficient‖ pricing (and/or with fundamental stock pickers).




In what may be his most famous article, Fama (1970, p. 383) reviewed ‗fair game‘ models (e.g.,

the CAPM), the submartingale model, and the random walk model; then made an empirical

literature synthesis follow-up to the now three famous forms of ‗market efficiency‘:

1. Weak form (past prices),

2. Semi-strong form (publicly available), and

3. Strong form (all information, both public and private).

As a reminder: ―A market in which prices always ‗fully reflect‘ available information is called

‗efficient‘.‖ The final rather lengthy summary and conclusions (p. 413-416) he summed up to

provide strong evidence in support of ‗market efficiency‘. As in most, if not all, of his articles, he

at least dismisses, or just ignores, any contrary evidence.

Grossman and Stiglitz (1980) mathematically formalized the idea that with costly information

there is room for sensible information gathering on the part of stock analysts, brokers, etc.

Furthermore, they stated (p. 404): ―We showed that when the efficient markets hypothesis is true

and information is costly, competitive markets break down.‖ Specifically, they noted (p.404):

―‘Efficient Markets theorists have claimed that ‗at any time prices fully reflect all available

information‘ (see Eugene Fama, p. 383). If this were so then informed traders could not earn a

return on their information. … Efficient Markets theorists seem to be aware that costless

information is a sufficient condition for prices to fully reflect all available information (see Fama.

P. 387); they are not aware that it is a necessary condition. … We are attempting to redefine the

Efficient Markets notion, not destroy it.‖ Even more important is the irony here, because they

have been, in a sense, ―hoisted on their own petard‖ (i.e., the EMH/EMT promoters have been




ignoring contrary evidence with such comments as: well if that was the case you would be rich;

and transaction costs and/or taxes would eat up any profits, etc.). Now consider information costs

are analogous to transaction costs and taxes, and Grossman and Stiglitz (1980) show,

information must be costless for markets to be efficient, then, assuming their normative model

applies, if there are such things as information costs (and transaction costs and taxes, etc.) then it

is impossible for the markets to be efficient, because market participants must garner a return on

this costly information (and transactions costs, taxes, etc.). Therefore, by definition, there must

be a tradeoff/equilibrium between information costs and efficiency. Ironic, isn‘t it? But of course, it doesn‘t stop the EMH/EMT promoters from redefining market efficiency as Grossman

and Stiglitz (1980, p. 404) do, or as others have done over time as ‗anomalies‘ have emerged.

Regardless, it is ironic that for the same type of reason(s) that EMH/EMT promoters had been

ignoring evidence against the EMH/EMT, they now have problems with it (i.e., from a normative

perspective).

I cannot let the last point about information costs slide; therefore a thought on the notion of

transactions costs, costly information, taxes, etc. and market efficiency. It is inconsistent to use

these as arguments to ignore real evidence yet use them as reasons to modify normative models.

Economics and financial economics focus on prices being set at the margin, yet the reference to

transactions costs is often an absolute reference or even a reverse margin point, that is, the exact

opposite of a marginal analysis. For example, if the highest transaction costs for an asset is 500

basis points (―BPs‖)47 and the lowest are 5BPs (i.e., for an institution), then the relevant

boundary condition is 5BPs, not 500BPs as the EMH/EMT promoters would indicate, and

47 A basis point of ―BP‖ is 100th of 1%. Therefore, for example, 100 BPs is 1%.




therefore, transaction costs are not much of an issue, except for very illiquid securities, but

certainly not most equities that are studied on the CRSP tapes48.

Finally, note that every classic referenced empirical study had contradictory evidence. In

addition, I didn‘t read any alternative explanations that might better fit the complete set of

evidence. Therefore, the empirical work was actually far from universally supportive; and it was

based on only one interpretation of the data (vs., for example, another normative

theory/hypothesis).

48 CRSP stands for Center for Research in Securities Pricing. It is a database of primarily security prices and relatedinformation started by James Lorie at the University of Chicago in 1960, based on a request from Merrill Lynch‘sLouis Engel in 1959 (Merrill Lynch made an initial $300,000 grant to start the database). It was first intended tocover all NYSE stocks, and was later expanded to cover a larger universe. It was originally a magnetic tape of dailyU.S. stock prices, hence the term, ―the CRSP tapes‖.




WHERE DO WE STAND TODAY?

You may at this point be asking yourself, what is the current state of knowledge in finance? The

following table summarizes what we now know with a fairly high level of statistical confidence

(see Cochrane (1999)):

Therefore, for example, finance spent a great deal of time and effort ignoring or marginalizing

evidence against the EMH/EMT only to accumulate enough evidence to conclude that not only

were we wrong, but we had it essentially reversed. For example, not only doesn‘t the stock

market follow a random walk, but it is predictable (i.e., has predictable components).49

Therefore, the most basic theory and hypothesis don‘t seem to fit the facts. Could it be that the

normative route wasn‘t a productive path to proceed down? Therefore, regarding the RWH, the

EMH, and EMT in general, the assumptions, the theory, and reality turn out to be wrong (i.e., at

least from a normative perspective).

49 In addition, even liquid stock markets (e.g., the U.S. equity markets) do not appear to even adapt to this. See, forexample, Daniel and Titman (1999, p. 28): ―To examine whether unexploited profit opportunities exist, we tested for a somewhat weak form of market efficiency, adaptive efficiency, that allows for the appearance of profitopportunities in historical data but requires these profit opportunities to dissipate when they become apparent. Ourtests rejected the notion that the U.S. equity market is adaptive efficient.‖ Therefore, even ‗weak-form‘ inefficiencypersists.

Cochrane's 'New Facts in Finance' - What we thought we knew, and what we now know.What was thought (until about the mid-1980s): What we now know:

1 "The CAPM is a good measure of risk and thus a good explanation of

the fact that some assets earn higher average returns than others."

"There are assets whose average returns can not be explained by their

beta." Multifactor models help the explanation.

2 "Returns are unpredictable, like a coin fl ip. This is the 'random walk'

theory of stock prices."

"Returns are predictable. In particular: Variables including the

dividend/price (d/p) ratio and term premium can predict …"

3 "Bonds returns are not predictable. This is the 'expectations model' of

the term structure." "Bond returns are predictable."

4"Foreign exchange rates are not predictable." "Foreign exchange rates are predictable."

5"Stock market volatility does not change much through time." "Volatility does change through time."

6 "Professional managers do not reliably outperform simple indexes and

passive portfolios once one corrects for risk (beta)."

"Some mutual funds seem to outperform simple indexes, … However,

multifactor models explain most fund persistence."

7 These views "reflect a guiding principle that assets markets are, to a

good approximation, informationally efficient (Fama 1970, 1991)."

In short, although "many results are hotly debated … the old world is

gone."




THE ISSUE WITH PREDICTABILITY

One of the primary empirical problems with the EMH/EMT is predictability. A major problem

with finance being so dependent on the EMH (e.g., Fama (1970)), and then around the 1990s

until recent later versions of EMT, is the issue of return predictability and its cause(s). As it has

become more and more difficult, if not impossible, to reconcile the 1970 version of EMH/EMT

with empirical reality, a new and evolving view has emerged in parallel with all perceived threats

to its view and related agenda.

What sorts of empirical proof have made the original version, and even recent versions, of the

EMH/EMT untenable? As it turns out quit a wide variety of proof has emerged over the decades.

The following list is just a list of predictability, ignoring for the time being other possibly more

damming proof. What now follows is a partial list of predictable patterns, largely taken from an

article by Daniel et al. (2002).50

1. Closed-end fund discounts/premia predict future returns on small firms.

2. Long-term bond returns are positively predicted by the difference between long-term

interest rates and the short-term rate, or based on the difference between the forward rate

and the short-term spot rate.

3. Increases in a country‘s bond yield relative to another country‘s bond yield forecasts

future appreciation of the country‘s currency (i.e., the ‗forward discount puzzle‘).

50 Also, who knows how much of this may be due to the ―perceived irrelevance of history‖ (a commonpsychological bias).




4. Cross-sectionally, small market value and high fundamental/price ratios predict high

stock returns in many countries, even after controlling for beta (e.g., B/Mkt, E/P, CF/P,

S/P, D/E, etc.51

).

5. For the stock market as a whole, high fundamental/price ratios (D/Mkt or B/Mkt) seem to

predict high long-horizon stock returns.

6. For the stock market as a whole, stock market returns are predictable based on various

macro variables (e.g., term spreads and default spreads).

7. Investors are surprised by the good subsequent performance of value stocks and the poor

performance of growth stocks (i.e., part of the ‗earnings drift‘ phenomenon). Note, in

order for purely ‗rational‘ stories/models to apply, the implied levels of covariance risk

on earnings announcement dates would have to be extreme.

8. Accounting ratios provide additional power to predict returns (they are roughly divided

into the following three types: (1) fundamental ratio analysis, (2) accruals analysis, and

(3) fundamental value analysis).52

9. Accruals (adjustments to accounting earnings) are strong negative predictors of future

stock returns. These non-cash flow effects are independent of B/Mkt and size effects.

Note these tend to lend more direct support to the behavioral approach (i.e., baring

extreme forms of collusion/illegal behavior), in that investors and analysts seem to be

rather easily systematically fooled by not adjusting for these systematic errors.

51 B = Book, Mkt = Market, E = Earnings, P = Price, CF = Cash Flow, S = Sales, and D = Dividends.52 Add to #8, that accounting method matters (see Daniel et al. (2002, p. 170-171)). For example, for M&A‗pooling-of-interests‘ method is treated differently than ‗purchase‘ method accounting and managers seem to like itmore (as well as analysts). This doesn‘t sound like EMT and it f urther suggests that framing is critical in the financefor accounting information.




10. Constructed fundamental value indices predict future stock returns (e.g., a residual

income model). In fact, cross country trading strategies using those types of valuation

models can be very profitable. Like accruals, this type of result tends to be supportive of

the notion of systematic errors on the part of investors.

11. There are positive short-lag autocorrelations (e.g., gold, bonds, and foreign exchange)

and negative long-lag autocorrelations (e.g., stock markets in general) in many asset and

security markets. That is, momentum in the shorter run and mean reversion in the longer

run.

12. Cross-sectionally, there is strong short-run momentum and long-run reversal. Again,

although cross-sectionally, that is, momentum in the shorter run and mean reversion in

the longer run. The Sharpe ratios achievable through U.S. momentum strategies alone

appear to be too large to be consistent with a rational frictionless model. Also, note that

the momentum effect is strongest in (1) small firms, (2) growth firms, and (3) using

industry components; and it doesn‘t appear to be related to macroeconomic conditioning

variables. Finally, real estate displays predictable price momentum (both residential and

commercial).

13. Momentum is associated with subsequent abnormal performance at earnings

announcement dates (about ¼ of the returns from the momentum strategy are from

returns on announcement dates).




14. The selection, timing, and manipulation surrounding ―corporate events‖ indicate some

predictability of the event itself. For example, the ―timing‖ view of corporate events

indicates mispricing.53

15. Stock returns after discretionary corporate events exhibit post-event continuation (with

the same sign as that on the event-date), except for private placements (where the

incentive is the reverse).54

That is, the ―post-event continuation hypothesis‖.

16. The equity share in total new issues predicts poor future performance of the U.S stock

market.

17. Investor expectations and analysts‘ forecasts about seasoned equity offering firms are

favorably biased, and the long run post-event abnormal returns of these firms are

associated with corrections of these biases. This is a type of almost direct evidence that

investors expectations are systematically wrong (e.g., negative ‗surprises‘/abnormal

returns for new issue firms around earnings announcement dates, and positive

‗surprises‘/abnormal returns for post-split firms around earnings announcement dates).55

The new EMT would argue that there is a great deal of uncertainty resolved around these

dates. This is unlikely. Why should covariance with the stock market suddenly become

very high or low a few days a year? For example, does information jump out on quarterly

reporting dates?

53 This is my own interpretation, with some additional insight/speculation.54 #15 includes: equity carve-outs, spinoffs, tender offers, open market repurchases, stock splits, dividend omissions,dividend initiations, seasoned equity and debt offerings, public announcements of insider trades, venture capitaldistributions, and accounting write-offs (Daniel et al. (2002, footnote 21, p. 162)).55 #17 could be called the Homer Simpson effect, earnings announcement ―doh‖, then an earnings announcement,―doh‖, etc. In essence, the same mistake is exposed and repeated over and over again.




18. Investors entrust large amounts of resources to mutual funds that, net of fees and costs,

do poorly. Hence, a kind of reverse market timing that is predictable.56

19. While abnormally positive mutual fund performance is questionably predictable (with the

possible exception of shorter run momentum effects), negative abnormal performance is

almost a sure bet.57

20. Analysts forecast revisions and recommendations are associated with subsequent

abnormal returns. Unfavorable recommendations have significantly stronger forecasting

power than favorable ones (e.g., strong underperformance after a downgrade, but weak

outperformance after an upgrade).58 Note, given that analysts make poor use of

observable predictable variables, it is unlikely that the effect is due purely to expert

observation (i.e., vs. an inside information explanation).

21. Firms in which long-horizon analyst forecasts of earnings are relatively high earn low

subsequent stock returns.59

22. WSS recommendations are significant negative forecasting signals for the overall

market.60

56 See e.g., Karceski (2002).57 This one is actually not in their list.58 They also differ if made by analysts at investment banks or independent research firms, whether they are buy orsell recommendations, and if during a bubble. Barber et al. (2004, abstract) summarize their findings as: ―dailyabnormal return to independent research firm buy recommendations exceeds that of the investment banks by 3.1basis points, or almost 8 percentage points annualized. In contrast, investment bank hold and sell recommendations

outperform those of independent research firms by 1.8 basis points daily, or 4½ percentage points annualized.Investment bank buy recommendation underperformance is concentrated in the subperiod subsequent to theNASDAQ market peak (March 10, 2000), where it averages 6.9 basis points per day, or slightly more than17 percent annualized. More strikingly, during this period those investment bank buy recommendations outstandingsubsequent to equity offerings underperform those of independent research firms by 8.7 basis points (almost 22 percent annualized).‖ Investment bank analysts are not to be trusted with buy recommendations, yet the vastmajority of their recommendations are buys.59 ―The predictability of returns from forecast errors is possible if investors rely too heavily on the forecasts, or investors and analysts are subject to similar cognitive biases, or both rely too heavily on some other information.‖Daniel et al. (2002, p. 166) This would also apply to #21.




23. Cash or earnings surprises are followed by positive abnormal returns in the short run,

especially for firms with low institutional ownership. The case of ‗earnings drift‘.61 Note

there is a debate as to whether earnings surprises are followed by negative abnormal

returns in the long run, and whether it has disappeared more recently.

24. Short sellers make abnormal profits through value strategies.

25. Time changes, weather, and feelings affect security prices (e.g., changes to and from

daylight savings time, and cloud cover in New York City causes low returns).62

26. Seasonality predicts security returns (e.g., the ‗January effect‘, and winter).

63

27. Etc., etc., …

It seems that the more interesting, if not simpler, question is not what is associated with the

possibility of mispricing and/or inefficiency, but what is not?

In summary on predictability, note the specifics are broad and deep:

• Past returns predict future returns (even Fama himself shows this, see Fama (1991)). This

is a firm rejection of even ―weak-form‖ efficiency.

• Measures of value predict future returns (e.g., price-to-earnings, book-to-earnings, etc.).

60 This one is actually not in their list.61Like #17, this could be called the Homer Simpson effect, earnings announcement ―doh‖, then an earningsannouncement, ―doh‖, etc. In essence, the same mistake is exposed and repeated over and over again.

Concerning #23, Daniel et al. (2002, p. 167) point out that Daniel and Titman (2000) find that ―stock pricemovements which can be linked to changes in accounting variables do not reverse, while price movements thatcannot be linked to accounting variable changes experience strong reversals. Daniel and Titman interpret thisevidence as consistent with overconfidence, based upon psychological studies showing that investors exhibit moreoverconfidence about vague or intangible information.‖ 62 Obviously, there could be a list of related effects. This has been modified this from the original.63 This one is actually not in their list; and obviously, there could be a list of related seasonal effects. Also, winterseems to cause return changes by the amount of daylight. For a study on seasonality see, for example, Lakonishok and Smidt (1988). They confirm the existence of the: (1) turn of the week, (2) turn of the month, (3) turn of the year,(4) holidays predictable/persistent returns over a ninety year period of the Dow Jones Industrial Average.




• Corporate events predict future returns (e.g., dividend changes, share repurchases,

seasoned equity offerings, etc.).

• Etc.

Predictability alone64 shows this is clearly not the case of ‗data mining‘ or specification search,

since almost every corporate ‗trigger‘ event seems to have predictable power. Therefore, not

only is it not data mining, but the reverse in the case of EMH/EMT promoters.

Please note: ―Most of the patterns of return predictability summarized have alternative (though

not equally plausible) explanations based on either risk premia or mispricing.‖ (See Daniel et al.

(2002, p. 150)) Also note, that when I write that ―returns are predictable‖ I am not saying they

are perfectly predictable, or that all risk has been controlled for. There is, in many cases, a great

deal of some form of risk associated with the prediction. Which is why I continually state that

even though there appear to be many ―free lunches‖, it is important to remember that those ‗free

lunches‘ are only free as defined by EMT type finance, not behavioral finance. Finally, it is not

clear what the causes of return predictability are; but again, the most likely causes are

64 Regarding evidence for the EMH/EMT, essentially, there are few portfolio managers who systematically seem to‗beat the market‘. That is, the superficial appearance of ‗no free lunch‘.

―It is our view, this fact is interesting but not particularly supportive of market efficiency. Under free choice thefunds that attract investors will be those that appeal to investors‘ emotions and beliefs, however biased. For example, if at some point investors are irrationally thrilled about the tech sector, cash will flow to funds heavy intech portfolios. More rational portfolios that are light on tech will on average earn high subsequent returns, but at therelevant moment will be unpopular with investors – that‘s the very source of the mispricing.The fact that vast amounts of invested wealth are placed in funds that appear to be wasting resources on activemanagement does not support the view that investors are good at choosing funds, nor that funds make good choiceson behalf of investors. There is some dissonance between the views that investors trade foolishly to create potentialinefficiencies, and that they are smart enough to invest in mutual funds designed to exploit these inefficiencies.‖ Daniel et al. (2002, p. 165)




behaviorally based, not EMH/EMT based65

. That is, because the behavioral approach is based on

a descriptive method, it will fit the facts better, by definition.

Also, note the following:

• Patterns of return predictability can have alternative explanations, and not all

explanations are equally plausible (specifically, strictly ‗rational‘ vs. behavioral).

• The behavioral approach is consistent with risk based factor premia (i.e., people do price

various forms of risk, but there are other psychologically based things going on,

specifically, psychological biases influence pricing). Actually, it was the original

EMH/EMT and the CAPM that lead the majority of academics and practitioners to

believe that there was only one risk 66 worth taking account of, whereas the behavioral

approach has always maintained that explaining what motivates investors is more

nuanced.

• The mispricing of factors is consistent with those factors identified as new additions to

the evolving EMT asset pricing model (i.e., the three factor model of Fama and French

(1993) or the four factor model of Carhart (1997)). Therefore, the ex post identification

and subsequent addition of new risk factors to the evolving EMT asset pricing model

doesn‘t mean those factors are anything more than the mispriced factors they were

identified as being in the first place.

65 Daniel et al (2002, p. 162-163) have a very good discussion on methodological problems of the new EMT modelsand what they actually show (especially with respect to event studies). In my opinion, they make the new EMT look a bit silly.66 That is, ‗market risk‘.




• In particular, the cross-section of securities returns is very difficult to rationalize based

upon ‗rational‘ risk measures (i.e., using size, value (B/Mkt as a proxy), and momentum).

Again, for example, the Fama and French (1993) and Carhart (1997) asset pricing models

may help to ‗explain‘ away a great deal of the mispricing or ‗anomaly‘, but that doesn‘t

mean it isn‘t caused by mispricing and/or is in fact mispricing. The seemingly important

question has been largely ignored. That is, are those factors true risk factors?

• Ignoring for a moment that they were added after the fact (ex post identification, which is

a type of theoretical specification search, ironically a claim made against others) and they

have no real theoretical grounding in economics, what is a momentum, value, or size risk

anyways? If those factors were truly risk factors then the factor realizations should

strongly covary with investors‟ marginal utility across states. Specifically, rational

asset pricing models would suggest that low marginal utility states (e.g., economic

booms) should be associated with high relative returns for ‗value‘ stocks and high

marginal utility states (e.g., recessions/depressions) should be associated with low

relative returns for ‗value‘ stocks. In fact, they either don‘t seem to move much or move

in the opposite direction as that expected by the newly evolved theory. Therefore, there is

little or no evidence to support the ‗insurance‘ theor y proposed by the evolving theory

(see Cochrane (1999) on an outline of what is predicted by this type of theory).

• The bottom line is that, short of extreme preferences not accepted by or incorporated into

any ‗rational‘ model, it is very difficult to explain the very high Sharpe ratios achieved by

forming portfolios based on size, value, and/or momentum. This is true no matter how

high the correlations between the returns of portfolios based on these ‗factors‘ and




innovations in macroeconomic variables (see Daniel et al. (2002, p. 152-153)), that is,

those studies that produce statistics with signs in the ‗right‘ direction.

• In addition to there being little evidence to suggest these returns are correlated with

macroeconomic variables that might proxy for marginal utility, there is very little

evidence of size, value, and momentum returns being correlated across countries.

Therefore, forming these types of portfolios internationally would result in even higher

risk-adjusted returns with even more problems for the evolving theory.

• Therefore, stay tuned for the next adjustment(s) to the theory.

In summary of these notes:

―Taken together, this evidence seems to imply that a frictionless, rational model which would

explain this evidence would have to have very unusual (and perhaps implausible) preferences

to accommodate very large variability in marginal utility across states.‖

Daniel et al. (2002, p. 153)

Again, just because people are influenced by their biases doesn‘t mean that they do not care

about risk or risk factors (quit the contrary, as humans they may be unduly motivated to ‗fear‘

risk more than a ‗rational‘ model would indicate). As mentioned, the data mining accusation is

truly absurd and hypocritical given that Fama in particular has thrown this one at almost anyone

pointing out an ‗anomaly‘. The critical point is that ‗value‘ stocks return more than ‗growth‘

stocks (part of the ‗good stock/good company‘ effect), then in a ‗rational‘ world this extra return

is due to extra risk, and this extra risk will become more clearly visible in the extreme negative

states of the world (specifically, recessions or depressions). Furthermore, most of these types of






etc.).68

And a final note, we will indirectly and possibly directly come back to these types of

questions again. I want the reader to think about what is causing them. It seems to me if they are

‗factors‘ people don‘t go out and think, well momentum is worth X to me forever. They may do

that but not in a way that is easily specified.

To me the obvious analogous debate of this ‗factor‘ vs. ‗characteristic‘ debate is the Earth being

just a planet in a larger solar system/galaxy/etc. vs. its being the center of the universe. As new

evidence is brought forth it became harder and harder to view the Earth as the center of all things

larger than itself. Again, just because people are influenced by their biases doesn‘t mean that

they do not care about risks or risk ‗factors‘ (quit the contrary, as humans they may be unduly

motivated to ‗fear‘ risk more than a ‗rational‘ model would indicate), but there seems to be other

more behaviorally based influences.

Therefore, I have good and bad news. The bad news is that the markets are inefficient (i.e., in the

traditional finance sense). The good news is that the markets are inefficient (but unfortunately

not in the traditional sense). Therefore, it may be of little or no practical use to know that they

are ‘inefficient‘; but at least the message seems consistent.69

68 Again, denial is strong motivator for many most humans, and EMT proponents are no exception.69 This is much like the good and bad news with respect to the social ―sciences‖. That is, the bad news is thateverything you learn therein is wrong, but the good news is that some of it is useful.




REFERENCES

Bachelier, M., Theorie de la Speculation, Gauthier-Villars, Paris, 1900.

Barber, B., Lehavy, R., and B. Trueman, ―Comparing the Stock Recommendation Performance

of Investment Banks and Independent Research Firms‖, Working Paper, August 2004, 1-40.

Barsky, R., and B. De Long, ―Bull and Bear Markets in the Twentieth Century‖, Journal of

Economic History, Volume 50, Number 2, June 1990, 265-281.

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APPENDIX A: TWO BASIC LOGICAL FALLACIES IN FINANCE AND ECONOMICS

Often based on or derived from the efficient market debate, there are two common logical

fallacies that exist with finance and economics, and they both have similar root causes regarding




their lack of logical consistency. The first is that ―if there is no ‗free lunch‘, the markets must be

efficient (and ‗prices are right‘).‖ The second is that ―if the markets are inefficient, then the

government must intervene.‖ Both statements are logically, and factually, false.

In the traditional framework, agents are rational and there are no frictions. Again, for example,

the EMH states that prices reflect their ‗fundamental‘ values. The agents in such an ‗efficient

market‘ understand ‗Bayes‘ rule‘70 and have well behaved utility functions. Under the EMH:

―‘prices are right‘‖, (Barberis and Thaler (2002, p. 3)) and any deviation from fundamentalvalue will be met by an arbitrager who will correct the mispricing (i.e., the ‗no free lunch‘

argument). Barberis and Thaler (2002, p. 4) point out the following:

―Prices are right‖ => ―no free lunch‖, but ―no free lunch‖ ≠>‖prices are right‖.

Restated, ‗prices are right‘ implies ‗no free lunch‘, but ‗no free lunch‘ doesn‘t imply ‗prices are

right‘.

Thus they, and I, would argue that in the case of ‗efficient‘ prices and ‗free lunches‘, the

causality only runs in one direction. Therefore, the two statements are not equivalent. Yes, if

prices are ‗efficient‘ (i.e., by the definition established by economics and finance), then, it is

tautologically true that there should be ‗no free lunches‘. Whether this can be proven without an

acceptable valuation ‗model‘ is another issue. But, yes, I can live with the implication that

70 Bayes‘ rule/law/theorem is about conditional probabilities (prior and posterior probabilities) which people have a

notoriously bad habit of messing up; and few have well behaved utility functions meeting all the basic axioms of utility (e.g., independence).




‗efficient prices‘ mean ‗no free lunch‘ based on current normative definitions.71That noted, it is

far from clear that not being able to find a clear ‗free lunch‘ means ‗prices are wrong‘.

To show this, think of the following sets of information and pricing:

71 Although, I should note that I have read normative EMH/EMT proponents claim that prices don‘t have to beefficient in order for market efficiency to hold (this argumentation tends to run through martingales, sub-martingales, and related fair games).

The set of all financial market prices

Prices are right Prices are wrong

‘No free lunch’




Divide the set of all financial market prices into two non-overlapping sets, where prices are

either ‗right‘ or ‗wrong‘ (i.e., according to normative finance). Thus, all that is required is that if

within the set of all finance prices (which is the sum of ‗prices are right‘ and ‗prices are wrong‘)

there is just one ‗wrong price‘ where there is ‗no free lunch‘, then the ‗price is wrong‘ but there

is ‗no free lunch‘. I will argue that, in fact, most prices today fall into this set of being incorrect

from a market efficiency standpoint, yet you would be hard pressed to extract a purely free lunch

from them.

Regarding factual or empirical proof of at least one example of this, it will be shown in one of

the following chapters that factually there are limits to arbitrage; additionally, several cases will

be presented where we know that: (1) prices were ‗wrong‘, yet (2) no ‗free lunch‘ seemed

available (e.g., many ―Internet ‗carve-outs‘‖). This is important not just in its bearing on the

argument concerning market efficiency, but also on its potential impact on the second fallacy.

Regarding prices being ‗wrong‘ and government intervention a similar argument and factual

accounting can be made. But, in addition, it should be noted that:

―Much of the scientific debate over market efficiency has a policy undercurrent. The efficient

markets hypothesis is associated with the free market school of thought traditionally championed

at the Universities of Chicago and Rochester. Imperfect rationality approaches are in part

associated with East Coast schools that have tended to be much more enthusiastic about

government activism.




… Proponents of laissez faire seem to have drawn a brittle defensive line: if markets turn out to

be substantially inefficient, the city of freedom is open to be sacked.

We argue that this link between efficient markets and the desirability of laissez faire is logically

weak. An important weakness is that even if investors are imperfectly rational and assets are

systematically mispriced, policymakers should still show some deference to market prices.

Individual political participants are not immune to biases and self-interest exhibited in private

settings. … Indeed, the economic incentives of officials to overcome their biases in evaluating

fundamental value are likely to be weaker than the incentives of market participants. … In sum,advocates of laissez faire who rest their case on market efficiency are in some respects

needlessly vacating the high ground of the debate without clash of arms.‖

Daniel et al. (2002, p. 141-142)

In short, there are ideological ―axes to grind‖ that tend to push people to claim that a ‗failure of

the market‘ is sufficient justification for increased government involvement, and it is not. In fact,

it is neither justified from a logical or factual perspective. Logically, as with the ‗free lunch‘ and

‗right prices‘ vs. ‗wrong prices‘ logical inconsistency, it does not follow that:

Prices are wrong => government intervention, in fact it isn‘t clear that:

Prices are wrong => intervention (i.e., any kind of intervention, let alone governmental). What I

would argue is that intervention is only warranted if and only if (―iff‖) it is proven empirically to

improve pricing. In fact, government intervention tends to move prices further away from market




efficiency (this will be shown in one of the following chapters).72

Therefore, and assuming

‗prices are wrong‘, what could be implied is the following:

Prices are wrong => intervention, but intervention ≠> improved pricing (i.e., prices pushed

toward being economically optimal). Again, therefore, intervention is only warranted iff it

improves pricing. Factually, and as a general rule (if not absolute rule), government historically

and currently hurts more than it helps.73

Furthermore, and stated slightly differently, it is important to say that even though I am highly

confident that in many ways, as of today, ‗prices are wrong‘ this doesn‘t mean government

should determine the pricing mechanism(s) (Daniel et al. (2002)). This would be tantamount to

making the same type of logic mistake made by those who professed that the lack of a clear ‗free

lunch‘ establishes the fact that ‗prices are right‘. Beyond that, it will be shown that government

involvement in the financial markets, on balance, is, has and continues to contribute to large and

persistent miss-pricings in the financial markets. If that point proves to be true, then not only

isn‘t it clear that government involvement won‘t help improve pricing, but that type of

involvement is almost assured to make it worse.74

72 At a minimum, the reason for this is that government tends to cause wrong/inefficient prices. For example, interestrates matter (and shouldn‘t be pushed below what the market(s) would set. ―… rational economic activity is

impossible in a socialist commonwealth.‖ See Ludwid von Mises, 1920, "Economic Calculation in the SocialistCommonwealth". The effect: misallocation of capital (i.e., ―malinvestment‖), which will need to be reallocated

away from the malinvestments. This will likely be painful; but the sooner the better.73 Comments made by Daniel et al (2002), seem most appropriate here: ―Investor credulity and systematicmispricing in general suggest a possible role for regulation to protect ignorant investors, and to improve risk sharing.The potential for improvement does not imply that government activism will help. The political process is subject tomanipulation by interest groups, and political players have self-interested motives. So a global default to laissezfaire is superior to a hair-trigger readiness to bring the coercive power of government into play. … Just as much as if markets were perfectly efficient, government can do great good simply by doing no harm.‖ Daniel et al. (2002, p.142)74 In my opinion, government involvement is best to probably adjust information to help correct for biases (e.g.,S.E.C. should have individual investors become aware of fundamental value, volatility, expenses, etc.) and set up a




APPENDIX B: SOME COMMONLY USED EXCESS RETURN MEASURES

This appendix is intended to provide background on the following terms associated with excess

returns:

1. ―alpha‖ (both ―Jensen‘s alpha‖ and the ―Treynor -Mazuy quadratic performance

measure‖),

2. ―Sharpe ratio‖, and

3. ―information ratio‖.75

The reason for this digression is that the one critical set of empirical proof primarily running

contrary to the EMH/EMT is the fact that so many strategies show statistically significant

statistics for strategies and/or tactics that shouldn‘t show them (i.e., according to normative

consistent, simple, and rational legal framework. This type of involvement would likely be useful in the brokerageand money management industries.75 Therefore, I am reviewing four measures. For example, Jensen‘s alpha equation is used to proxy for unsystematicrisk while its beta is a proxy for systematic risk. Note, there are potentially an infinite number of these equations, butthose listed are early ones that have withstood the test of time, and that have an intuitive appeal.




financial theories associated with the EMT). Specifically, the ―alphas‖ of certain well

documented strategies and/or tactics show statistically significant and often large ―alphas‖.

Therefore, let‘s just establish what we mean by the terms ―alpha‖ and ―information ratio‖ (a

related concept). In addition, we will be referring to many studies that focus on these concepts.

Also, if you get a job in the ―real world‖ of finance these are useful terms /concepts to know.

Remember, as with most or all of empirical finance, these are subject to semantics and

interpretation. Therefore, let‘s set the semantics and definitions.

From Gallo and Lockwood (1999, see p. 45):

Sharpe‘s Reward-to-VARiability (―RVAR‖) is:

=

, where = mean monthly return for mutual fund i, = risk-free rate (the 30-

day U.S. T-bill rate), and = standard deviation of monthly returns for fund i. RVAR was used

to compute the average excess return per unit of the fund‘s total risk. It‘s a basic, useful measure

of risk-adjusted return.

Jensen‘s (1968) alpha:

-= +(-)+ , where for month t :

= return for fund i,

= return for the market portfolio (they used the Wilshire 5000 Index), and

= the random error term.




The alpha is intended to represent the difference between the realized mean return of the fund

and its risk-adjusted required return (i.e., as determined by the CAPM). Therefore, a positive and

statistically significant alpha is considered to be the overall goal of a Portfolio Manager (―PM‖).

Whereas, from an EMH/EMT perspective any statistically significant deviation from zero is

evidence against (i.e., assuming it is the ‗correct‘ model, which of course, according to most, if

not all, EMT supporters, all models are wrong; therefore, the exercise of checking for

inefficiencies is virtually impossible, if not silly76).

Treynor-Mazuy (1966) quadratic performance measure (i.e., a form of attribution model):

+= +(-+ , where:

= the security selection ability of the manager, and

= the manager‘s market-timing skill.

The quadratic equation is derived by assuming that the fund beta in the Jensen‘s alpha equation

may change in response to the market index (specifically, is replaced with = -

)). A positive implies superior market timing skill, and a positive implies superior

security-selection skill.

Regarding deriving the ‗information ratio‘, Grinold and Kahn‘s (1992) suggested technique

relies on a series of regressions. First, perform the following regression:

(1) ( - ) = - ) +

76 Of course, by extension, scientific method cannot be applied, because no evidence is then admissible. Thus, onemust accept some form of test or there can be no rejection or acceptance of a hypothesis or hypotheses. Again, weseem to be at a stage where, as De Bondt stated, we cannot test any theory and yet the actual facts seem to contradictthe theories we cannot test.




in order to estimate the portfolio‘s alpha and beta. Therefore, alpha =

. Second, evaluate whether alpha is significantly different from zero:

(2) t-stat =

where SE = the standard error of the estimate. Third, measure PM ‗Value-Added‘ (―VA‖):

(3)

where VA = alpha minus a risk tolerance (lambda) times the annualized residual risk (Note:

Grinold and Kahn assume the manager tries to maximize this risk-adjusted annual alpha).

Therefore, the investor‘s risk tolerance will tend to drive the level of aggressiveness of the PM to

maximize this VA.

A few things to note or highlight before proceeding:

Most key values are annualized. This is standard practice (e.g., if monthly data, then

alpha needs to be multiplied by twelve, and the number of months will determine what

you need to do to residual risk).

The basic equation is a Sharpe invention (i.e., maximize return/alpha less risk tolerance

adjusted risk).

Lambda or risk tolerance is a key part and typically is between two and three (actually

institutions have typically used three), but has been shown to vary under different

conditions for people (e.g., behavioral approach and framing).

Please remember, although these methods/techniques are based on normative theories, they are

very practical and useful.




Furthermore, Grinold and Kahn (1992, p. 19) state: ―Detailed analysis shows that the maximum

value-added rises in proportion to the square of the manager‘s information ratio, IR, the ratio of

annual alpha to annual risk:

(4)

,

(5) with

.

The information ratio is essentially an investment signal-to-noise ratio. An information ratio of

0.75 means that we can expect 3% alpha per year if we take a 4% per year residual risk. …

value-added rises with the manager‘s information ratio, regardless of the level of risk aversion.

Because of this direct connection to investment value-added, the information ratio is the best

overall statistic to use for information analysis.‖ Although the IR may not be the ―best overall

statistic to use for information analysis‖, it is very useful.

Note, the IR and t-statistic are closely related (at least by definition), in fact:

(6) ,

therefore, the two tend to converge as the number of observations increases.

Summing up the t-stat and IR, Grinold and Kahn (1992, p. 19) state: ―Overall, the t-statistic

measures the statistical significance of the return, but the information ratio also captures the risk-

reward trade-off of the strategy and manager‘s value-added. … An information ratio of 0.5

observed over five years may be statistically more significant than an information ratio of 0.5

observed over one year, but their value-added will be equal‖ (because risk was arbitrarily defined

over one year). A critical practical point about the IR (i.e., as defined here) is that in the real




world it is useful to have more time to prove that good performance wasn‘t a statistical fluke, but

the reality is that you may not have the time to prove it.

Finally, most practitioners would recognize the following as the IR:

IR = [portfolio return – benchmark return]/standard deviation (of portfolio return – benchmark

return).77

This takes its inspiration directly from the ‗Sharpe ratio‘ (i.e., which uses a risk-free rate of

return in place of the benchmark return). Keep in mind, although generally a useful concept, it is

important to note what exactly someone means by IR.

77 The difference between this and the Sharpe measure is the risk-free rate is used in the Sharpe ratio and thebenchmark return here.




Chapter 4: Limits to arbitrage or the first „pillar‟ of behavioral finance

Again, there are two key parts to behavioral finance: (1) limits to arbitrage, and (2) psychology.

This chapter will focus on the limits to arbitrage part. Specifically, I will focus on those clear and

relatively unambiguous cases where limits to arbitrage must exist.78

Also, as already mentioned,

after reading this chapter the reader should at least begin to understand why limits to arbitrage is

a necessary but not sufficient condition for the psychology part to matter, as well as begin to

contemplate or understand the possible importance of the psychology part.

It has been stated that behavioral finance: can practically be applied to those situations where

cognitive psychology and microeconomics are best employed in combination. For example,

Thaler (1994, p. 62) states: ―The key ingredient is the existence of a cognitive illusion, a mental

task that induces a substantial majority of subjects to make systematic error. … Whenever suchan illusion can be demonstrated, the possibility that market outcomes will diverge from

predictions of economic theory is present.‖ Although true, I would argue that in those cases

where we would expect no divergence or ‗systemic error‘ would then be in those areas where no

‗cognitive illusion‘ at all applies. Simply stated, if no ‗cognitive illusion‘ is even possible, then

something else must be driving pricing. We will shortly cover such cases, that is, cases were we

know with a very high degree of confidence what the price should be and expect no ‗cognitive‘

or other type of illusion, yet prices diverge anyway. The likely explanation is limits to arbitrage

78 If the answer isn‘t limits to arbitrage, the reader will see that finance and economics has more fundamentalproblems than a poor methodology.




(for a classic more normative model based view on how this is likely, see Shleifer and Vishny

(1997)79).

First begin by thinking (and assuming, i.e., before it is proven) of the following two extremes of

arbitrage, and then reality:

1. No arbitrage of any kind anywhere (i.e., in any market).

2. Pure arbitrage everywhere (i.e., in every market).

3. We live in a world where there are limits to pure arbitrage (i.e., somewhere in between),

and many, if not most, investors are not being strictly rational (in the traditional

economic/Savage sense).

In the case of no arbitrage anywhere at any time, imagine that prices could be at any level with

no particular reason for them being there. Now this is unlikely. Isn‘t it likely that at some point

there must be some boundary condition? Minimally, remember that finance is essentially

discounted cash flow analysis. The trick is to figure out the cash flows and associated discount

rates. For example, assume the simplest case, a case of a riskless80 one cash flow and one

discount rate (e.g., a T-bill where you get back your original investment plus some implicit

interest – one payment with principal and interest combined). Therefore, the cash flow and

discount rate are known. How likely is it that if you are to receive in exactly one year a riskless

cash flow of $11,000 for a security you paid $10,000 for, that its price will deviate greatly over

79 Thus, it is possible to construct normative models where there are limits to arbitrage. The critical consideration isthat descriptively the evidence strongly suggests they exist, not that one can mathematically construct a model withthe typical assumptions (e.g., extreme economic rationality, etc.).80 Of course, there really is no such thing as a ‗riskless‘ risky security, but bear with me for the example.




the next 365 days? Isn‘t it likely at some point, ceteris paribus, if say the price drops to say

$5,000 someone will step in (an arbitrageur) and buy the security and just wait to collect the cash

flow? Clearly, and with the general caveat of ―it depends‖, at some point someone will act like a

rational arbitrageur and limit the amount of deviation from $11,000. At least for fairly low risk

cash flows, it isn‘t that much of a stretch to conclude mentally that there must be some limit to

how far away from something approaching a general reasonable value that cash flow can deviate.

Now taking the aforementioned one riskless cash flow example and imagine that there is always

and everywhere some arbitrageur prepared to step in if there is any deviation from true value. In

this case, assume the discount rate is zero. Therefore, the true discounted cash flow value or

present value is $11,000. Therefore, any deviation from $11,000 will be met by an arbitrageur

coming into the market and pushing the price back to $11,000. But how likely is that? In this

contrived case, based on incorrect assumptions, we left out, for example, transaction costs. Now

imagine that transaction costs for any arbitrageur were say $1,000 per transaction, and all the

other previous assumptions held. Based on this one change, who would actually bother to enter

the market and buy or short that security if the price didn‘t deviate more than $1,000 from

$11,000? Certainly no ‗rational‘ economic being would.

At this point, I am not really trying to prove that we live in a world where there are limits to

arbitrage, I just want the reader to entertain the thought that it is reasonable to expect that there

would be limits. Therefore, I would like the reader to mentally picture at least one case

theoretically where this could be the case, and why it could be the case.




Again, the real key is the lack of pure arbitrage, without it the psychological factors might be of

limited interest. It is a necessary condition for the psychological part to matter in an economic

sense. Although, the psychological factors seem to at least in part be responsible for the lack of a

pure arbitrage (so ultimately they depend on and/or cause the other).

Arbitrage is often required to make markets efficient (see Scholes (1972) on large block sales).

Exact substitutes are easy cases, where you go long the cheap asset and short the expensive asset

to make money and bring prices back in line with market efficiency. Ultimately, it comes back to

cash flows and discount rates. To the extent two cash flows are similar or mirror opposites of

each other, then they are useful substitutes.81 Where arbitrage is closer to one for one (e.g., short

government debt), arbitrage tends to work, but this tends to have little to do with informational

efficiency per say.82 Given that there aren‘t any ‗pure arbitrage‘ opportunities, unless criminal

behavior is included, it should be viewed as an ideal, not as reality.

Here is a list of some major limits to arbitrage:

(1) transaction costs (e.g., brokerage commissions, taxes, etc.),

81 Michael Milken, of ‗junk bond‘ fame made a point close to this. He once was asked why he preferred distressed

bonds over stocks. He explained that with stocks, given that they essentially have very small cash flows (i.e.,dividends) you could grow old before you realized a positive return (i.e., you were waiting for the market price to goup), therefore you were largely dependent on other people to realize the stock had value (which might be never),whereas with distressed bonds you often had such high cash flows (i.e., coupon payments) that you might not evencare whether the market thought the company would ever pay you coupons, let alone the principal. In short, whatmattered with debt was whether you got the cash flows (i.e., coupons, and eventually the principal) not what themarket thought about the price. Essentially, he felt more comfortable on a cash flow basis with debt over equity inthe same company because the debt had a shorter duration, ceteris paribus.82 Although now it is generally accepted in academic circles that there is much autocorrelation in financial timeseries, ala things like ARCH/GARCH etc.




(2) noise or various other behavioral limits to arbitrage (e.g., LTCM type where perceived

inefficiency increases rather than decreases)83,

(3) capital constraints (depending on the investor), and

(4) liquidity constraints (related to the other three, especially #1).

And here is a list of some problems with pure arbitrage:

1. Fundamental risk (e.g., Nokia vs. Ericsson example), since no close substitute.84

2. ‗Noise trader‘ and/or ‗irrational‘ trader risk.85

3. Implementation costs (transaction costs, price impact, short-sales constraints

86

, legal

constraints, horizon, information costs (e.g., buying and/or reading this book), taxes,

etc.).

But the evidence of limits to arbitrage is problematic for at least the following reason: Because of

the ‗joint hypothesis problem‘ of testing a mispricing and the model of fundamental valuation, it

is difficult to find cases where a mispricing can be proved beyond a reasonable doubt. We must

have both a value function or ‗model‘ where we are certain any price deviation from it is wrong

83 Note, and as mentioned before, as a general rule, the noisier the return series (or any series for that matter) theharder it is to make sense of the series (i.e., even if there was indeed something to be made sense of to begin with).Thus, and furthermore, ‗noise traders‘ can turn financial data from useful to useless by the simple act of trading onthe noise they create. Therefore, noise trading can turn what should normatively be a fundamental series into alargely useless string of numbers, or not.84 Summers (1986) makes a very strong argument that it is impossible for an arbitrageur or academic to know whatthe true value should be. Essentially, he argues that current tests of market efficiency have very ―low power‖ in astatistical sense. He argues that just as academics cannot effectively determine whether prices are efficient or notwith such tests and/or models, by extension neither can an actual trader know because they are using the same tests

and/or models. Thus EMH/EMT supporters are essentially ―hoisted on their own pittard‖. To the extent they argueall models are wrong, then the statistical power of those models must also be low, and the obvious question is notonly what the price should be, but how could any price be corrrect from a market efficiency perspective, especiallythe actual one?85 Even in theory irrational traders can essentially take money from rational traders (see, e.g., Hirshleifer et al.(2002)). In practice one only has to imagine what generally happened as technology shares drifted further away fromfundamental values during the technology/Dot.com mania, especially during the late 1990s.86 For example, see Jones and Lamont (2002). They found significant overpricing for stocks with high loan rates.Based on short sales constraints alone, they indicated that their evidence and analysis was consistent with the notionof limits to arbitrage.




(i.e., from a market efficiency standpoint)87

, and/or a case where we can derive a value we know

must have certain properties (e.g., being negative, positive, or zero). There are precious few

cases like that, but there are some (we will cover all the known ones in this chapter, and one

more directly related to arbitrage and demand for securities).

Given limits to arbitrage, try to imagine, for example, what you would do to actually carry out

the ‗arbitrage‘ strategy of say going long Nokia and short Ericson (two cell phone manufacturing

companies). That is, say your analysis informs you that the fundamental value of Nokia is too

low relative to Ericson. Specifically, how would you calculate how many shares of each to buy

or sell, then effect that trade (i.e., of going long Nokia stock and short Ericson). Keep in mind

this is a relatively simple example as it only involves two like companies‘ equity securities.

Some things to consider are the following: (1) currency, (2) timing, (3) short sales constraints88,

(4) costs, and (5) idiosyncratic risk. Regarding currency, Nokia is headquartered in Finland while

Ericson is headquartered in Sweden. Currently, Finland uses the Euro while Sweden the Swedish

Krona. Therefore, one would need to go long and short Euros and Swedish Krona in order to

eliminate currency risk. Consider how you would make that calculation; then consider the risk of

one or both countries changing their currencies during the trade. Regarding timing, imagine that

in order to be confident that you are shorting one and going long the other you must trade each at

exactly the same time. For example, imagine that you first go short 100 shares on Ericson, but

87 In addition, we need to show the capital is flowing to rational arbitrageurs and away from those who have moved prices away from their fundamental value. Even Treynor (1998) doesn‘t believe this always and everywherehappens. And, again there is Summers‘ (1986) argument concerning the logical impossibility of any price beingprovable as normatively efficient, or practically and actually correct (i.e., from a market efficiency perspective).88 Apparently, besides loan supply, loan fees and recalls either influence security pricing and/or are symptomatic of demand/supply differentials that cause deviations in price (see D‘Avolio (2002)).




you are unable to buy an equivalent number of Nokia shares (i.e., assuming you needed the same

number) at exactly the same time (a common occurrence), but before you can purchase the Nokia

shares their price increases and/or Ericson shares decrease in price. How would you assure that

that doesn‘t happen? Regarding short sale constraints, imagine that you short shares, but then the

government wherever you are domiciled for investment purposes passes a law against short

sales; or there is already a law passed that limits price movements for short sales, etc. How do

you eliminate that/those risks? Regarding costs, imagine that transactions costs alone change

from the time you enter the first leg of the trade (i.e., before you unwind it) and say the

government passes a law increasing or lowering the tax on short term gains, or loses, or

something else. How would you control for that/those risks? Regarding ‗idiosyncratic risk‖,

clearly Nok ia isn‘t exactly structured like Ericson (e.g., Ericson has a higher exposure to

transmission equipment relative to cell phones than Nokia, and sales are geographically different

in their span and concentration for each company). How do you take account of that kind of

specific business risk, or any other related risk for that matter? Of course I could go on. The

point to be made here is that what might seem a simple trade isn‘t. When one begins to consider

what it would take to truly perform an arbitrage or hedge, you begin to realize there really isn‘t

one where you are not exposed to many forms of risks and costs you hadn‘t originally considered

or wanted to be exposed to, any of which has an impact on both your ability and desire to make

the trade in the first place. In short, limits to arbitrage are ubiquitous; you just have to think about

it.




In addition to clear limits to arbitrage, there are at least two reasons prices may not be driven

toward their fundamental values quickly (or ever) by ‗smart money‘:

(1) prices can reflect an average of beliefs and/or expectations (e.g., if investors are risk

averse); and

(2) there are some psychological biases most, if not all, people will be unable to overcome

(e.g., overconfidence89

).

Furthermore: ―Just as rational investors trade to arbitrage away mispricing, irrational investors

trade to arbitrage away rational pricing. The presumption that rational beliefs will be

victorious is based on the premise that wealth must flow from foolish to wise investors.

However, if investors are foolishly aggressive in their trading, they may earn higher rewards for

bearing more risk or for exploiting information signals more aggressively, and may gain from

intimidating competing informed traders. Indeed, one would expect wealth to flow from smart

to dumb traders exactly when mispricing becomes more severe, which could contribute to

self-feeding bubbles.‖

Daniel et al. (2002, p. 141)

In finance and economics all hell breaks loose when the marginal investor/bidder/buyer doesn‘t

determine price. For example, asymmetric information, and specifically the ‗lemons problem‘ is

a study in average pricing driving all sellers of non-lemons out of the market, which then results

in the market effectively locking up and no trades taking place (see Akerlof (1970)). The

89 It is important to note that even though it has been suspected that overconfidence and/or miscalibration causes, forexample, excessive trading, it is not exactly clear what effects it has in financial markets (see, e.g., Glaser andWeber (2003) on their finding that miscalibration doesn‘t appear to be associated with trading volume, it seemsmore likely that the extent of a differences of opinion is driving much of the result, at least for ‗day traders‘).




economic ‗lemons problem‘ originally resulted due to sellers having ‗asymmetric information‘

relative to buyers. That noted, what drives the result is, as usual, the math. The math is driven by

the price not being set at the margin. This is a general issue in economics, whereby when

marginal buyers and sellers don‘t set pricing we have a fundamental problem. Specifically, even

assuming specific economic forms of rationality, the theoretically normative market is likely not

to clear. In fact, there is reason to believe prices are not generally set purely by marginal

analysis.

Regarding psychological biases that are difficult to overcome, and although I am getting slightly

ahead of the subject, they almost surely exist. Common examples are overconfidence and

regret90.

―The field of modern financial economics assumes that people behave with extreme rationality,

but they do not. Furthermore, people‘s deviations from rationality are often systematic.

Behavioral finance relaxes the traditional assumptions of financial economics by incorporating

these observable, systematic, and very human departures from rationality into standard models of

financial markets. We highlight two common mistakes investors make: excessive trading and the

tendency to disproportionately hold on to losing investments while selling winners. We argue

that these systematic biases have their origins in human psychology. The tendency for human

beings to be overconfident causes the first bias in investors, and the human desire to avoid regret

prompts the second.‖

90 Regret is more than the pain of loss. It is the pain associated with feeling responsible for the loss, and the intensityof that can vary with the loss. For example, I choose to drive to work according to a new route on a whim one dayand wreck my car in an accident, or short Nokia and the price immediately increases, etc., etc ... Regret will tend toencourage me to not change my driving habits or investing habits in those two cases.




Barber and Odean (1999, p. 41 – abstract)

Regardless of reason, traditionally, if prices deviate from fundamental or economically intrinsic

value finance assumes that a rational arbitrageur will push prices back in the proper direction.

The one problem we now have is that finance academics no longer can agree on the pricing

model. If we cannot know the true economic value of a security, then we cannot say if prices

deviate from it, or whether the tale/story of the arbitrageur applies. Therefore, because no finance

‗model‘ is correct, it is impossible to know what is the true price is in economic modeling

terms.91 But what if we actually knew the model? What if we could know what the true price

should be? Do we have any cases such as that? Answer: Yes we do, and let‘s review all of

them.92

Actually, it‘s not much of a ―model‖; it‘s more of an identity called the Law Of One Price

(―LOOP‖), and basic algebra. Specifically, the LOOP is really a statement that the price of two

goods that are the same must be the same.93

It directly applies to commodities, and clearly must

apply to something like two shares of the IBM. That is, specifically, there can be a slight bid-

offer spread between two identical shares, but their prices must be about the same if they are sold

around the same time. The nice thing about the LOOP is that it is something even EMT

academics can agree with. But note if LOOP breaks down, then there is not much we can say

91 Again, as Summers‘ (1986, p. 591) notes: ―speculation is unlikely to ensure rational valuations, since similar problems of identification plague both financial economists and would be speculators.‖ Therefore, to the extentacademics don‘t know the model, why would prospective arbitrageurs be expected to know it? 92 I am leaving one related case for another chapter.93 Of course, regarding the financial markets, small discrepancies can exist for things like transaction costs, andlarger differentials for timing differentials.




about anything in finance or economics. By extension, basic algebra should apply. In short, if

one plus one doesn‘t equal two, well then there isn‘t much we can say mathematically is there

(and effectively all ‗modern‘ finance and economics depend on mathematics)? Therefore, instead

of a model we are really just applying common sense and very basic math. That is why, the cases

selected were chosen, if one questions the ‗model‘ then basic logic and math can no longer be

applied.

Of the following four areas of evidence of mispricing and limits to arbitrage, three are cases of

the LOOP and/or basic addition, and the last is related more to limits to arbitrage and demand for

securities.

1. ‗Twin shares‘ or ‗dual-listed companies‘ (e.g., Royal Dutch 60% vs. Shell Transport

40% of cash flow implies 1.5 times ratio of pricing of Royal Dutch to Shell Transport). In

the Shell case they were virtually perfect substitutes.

2. ‗Internet carve-outs‘ (e.g., 3Com selling 5% of Palm Inc., the implied price of all of

3Com‘s other businesses was -$60 a share or at one point or -$23 billion). These are

direct violations of the LOOP and/or basic math. In 3Com case, the sum ≠ parts.

3. Closed-end fund discounts/premiums (e.g., most, if not all, closed-end funds –

‖CEFs‖). Also, these are direct violations of the ‗law of one price‘ and/or basic math. In

this case also, the sum ≠ parts.

4. ‗Index inclusions‘ (e.g., S&P 500 adds and drops, especially when pricing adjustment

lags).




In each and every case economic ‗law‘ is violated. Therefore, in every case we know the ‗model‘

or what should or shouldn‘t happen, yet it is violated anyway. My bet is that this isn‘t a random

thing, but I‘ll let the reader decide.

‗TWIN SHARES‘ OR ‗DUAL-LISTED COMPANIES‘

‗Twin shares‘ or ‗dual-listed companies‘ are also referred to as a ‗Siamese twin‘. They are two

companies incorporated in different countries, but possessing a contract where they agree to

operate their businesses as if they where one company. Each of the two legal entities effectively

agrees to a legal document where this structure is explicitly stated, and without agreement from

both, cannot be broken or changed in any way. In addition, they retain separate legal identities

and each is listed on a stock exchange. There is no economic or financial difference between the

two companies; in theory they should trade in an exactly parallel fashion. In short, there are no

known limits to arbitrage and we know the relative pricing function or ‗model‘ for each pair of

‗twins‘. For example, if company A and company B are the ‗twin shares‘ and their legal

agreement states that one share of company A is entitled to 60% of the combined company and




one share of company B is entitled to the remaining 40%, then A‘s shares must always trade at

1.5 times B‘s shares (0.6/0.4 = 1.5). Any deviation from this theoretical parity is not suppose to

happen (i.e., from a normative finance perspective) and, at a minimum, is likely due to limits to

arbitrage.

One classic case of ‗twin shares‘ is Royal Dutch NV (listed in Amsterdam) and Shell Transport

(listed in London) with a theoretical parity ratio of 1.5X. Therefore, any deviation is a clear sign

of the wrong relative price. What follows is such a graph where deviations from zero are

deviations from theoretical parity. Again, in normative theory there should be no deviations from

the zero line.

-32.0%

-30.0%

-28.0%

-26.0%

-24.0%

-22.0%

-20.0%

-18.0%

-16.0%

-14.0%-12.0%

-10.0%

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

20.0%

22.0%

Royal Dutch NV to Shell Transport Deviation from Theoretical ValueJanuary 1, 1980 - July 20, 2005




Source: Underlying data is from Mathijs A. van Dijk (webpage: http://mathijsavandijk.com/dual-listed-companies).

Even in the worst normative case what we should see is the blue line tightly oscillating around

0.0%. That is, if the markets were ‗efficient‘ in a traditional sense the relative differential or

value between the two companies‘ shares should be zero, or very close to it. They aren‘t.

Regarding the issue of magnitude, the maximum deviation from expected value is about 20%,

while the minimum deviation from expected value is about 30%. Therefore, historically the

overall swing around is about 50%.94 This is large and very problematic. Did you expect such a

deviation from a known value?95 Note also that the two shares are rarely equivalently priced (i.e.,

theoretical parity is rarely achieved). Therefore, based on relative pricing, most of the time the

prices of the two shares must be wrong (i.e., from a market efficiency perspective). Even if we

only had this one case96

, it would be problematic, but as far as I know it happens in every ‗twin

share‘ case, similar to Shell‘s.

Furthermore, in case the reader had any doubts about the relative value of the two shares or

‗model‘, note that on October 28, 2004 the two companies announced they would be formally

unifying; and on July 20, 2005 the two shares were delisted.97

As of delisting the relative values

94 Froot and Dabora (1999) find a similar swing relative to parity.95 It is also problematic for ‗arbitrageurs‘ trying to take advantage of relative differentials narrowing (e.g., LTCMtrafficked in this one). Arbitragers are notoriously short-horizoned with limited risk tolerance Also, see Rosenthaland Young (1990).96 For mathematical purposes you only need one case to prove something is mathematically wrong. Therefore, this ishighly problematic for a field like finance that generally uses mathematical proofs to form normative models andtheory.97 One share of Royal Dutch Petroleum Company was exchanged for two shares of Royal Dutch Shell plc class ‗A‘shares (i.e., Shell Transport and Royal Dutch were merged at a 2-1 ratio, as the original ratio was legally stipulated).




converged to the expected differential of 0%. If the ‗model‘ were incorrect, why would that

happen?

How about another ‗twin share‘?

Source: Underlying data is from Mathijs A. van Dijk (webpage: http://mathijsavandijk.com/dual-listed-companies).

Again, what we should see is the blue line tightly oscillating around 0.0%. It doesn‘t, at least not

for long. The maximum deviation from expected value is about 44%, while the minimum

deviation from expected value is about 45% (although, both occurred relatively early in the

series). Therefore, historically the overall swing around true value is about 90%. Again, this is

large and very problematic. I could list others, but it seems that other cases illustrate the same

-45.0%

-40.0%

-35.0%

-30.0%

-25.0%

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

Unilever NV to Unilever PLC Deviation from Theoretical ValueJanuary 1, 1975 - October 3, 2002




kinds of lessons (albeit, regarding the magnitude of absolute deviation, the others wouldn‘t be as

dramatic as the two shown). Therefore, it isn‘t just that I sought out a single counter example to

prove some obscure point. In fact, this is the norm for the dozen or so ‗twin shares‘ cases that

exist and have been documented (see De Jong et al. (2008)).

Perhaps, other types of examples are available? What about another area where we know the

‗model‘, for example, how about ‗carve-outs‘?

‗CARVE-OUTS‘

An equity ‗car ve-out‘ typically refers to the Initial Public Offering (―IPO‖) sale of common stock

by a corporation of one of its business units (i.e., the parent company publically sells a portion of

one of its subsidiary companies).98

Typically, this involves selling less than the entire amount

(usually less than a controlling interest, or less than 50%), such that that parent retains a

98 There is a well established relationship between stock IPOs and market returns (see, e.g., Lowry and Schwert(2002)). This would plausibly suggest that a ‗carve-out‘ is likely to be prompted by the expectation of a fundamentaldifference between the parent‘s stock and the partial IPO candidate.




controlling equity stake in the subsidiary company. During the late 1990s, there was a relative

surge in this type of IPO, especially for technology companies.

The ‗model‘ in the case of ‗carve-outs‘ is algebra. Logically, the sum of a parent company‘s

parts should add up to the whole. For example, say parent company YZ is composed of company

Y and company Z. Algebraically, YZ = Y + Z (or at least very close to that).99

That is, short of

logically justified extenuating circumstances, YZ cannot be worth significantly more or less than

the sum of its parts.

In their article aptly titled: ―Can the Market Add and Subtract? Mispricing in Tech Stock Carve-

outs‖, Lamont and Thaler (2003, p. 228) state: ―The most basic test of relative valuation is the

law of one price: the same asset cannot trade simultaneously at different prices. … the law of one

price is in many ways the central precept in financial economics.‖ Indeed, as we saw with ‗twin

shares‘, it isn‘t clear how normal violations are. Perhaps, violations are just a ‗twin shares‘

phenomenon, then again, perhaps they are the norm. If the latter is descriptively true, then the

basic normative foundations of finance must be called into question.

Furthermore, Lamont and Thaler (2003, p. 228) state: ―The driver of the law of one price is

arbitrage, defined as the simultaneous buying and selling of the same security for two different

prices. The profits from such arbitrage trades give arbitrageurs the incentive to eliminate any

violations of the law of one price. Arbitrage is the basis of much of modern financial theory,

99 Or possibly, that is, if the parent has positive net worth on its own, YZ ≥ Y + Z; or possibly, YZ ≤ Y + Z, if theparent has negative net worth on its own. But, as a general rule, it is unlikely the value of the sum of the parts willdiverge significantly from the whole.




including the Modigliani-Miller capital structure propositions, the Black-Scholes option pricing

formula, and the arbitrage pricing theory.‖ Indeed, if the LOOP doesn‘t hold in one case, the

math of the aforementioned ‗models‘ can be called into question; but if the LOOP doesn‘t hold

in most known cases, then it is far from hyperbole to state that the basis for almost any normative

financial model is questionable (i.e., the ones that depend on arbitrage to derive their results).

Lamont and Thaler (2003) looked at eighteen ‗high-tech‘ equity ‗carve-outs‘ that are followed by

a ‗spin-off‘

100

during the period April 1996 through August 2000. Of particular note was their

definition of the ‗stub‘ (i.e., the remaining value of the parent company‘s business(es)). This is

critical because this is the algebraically implied value (i.e., given that we can observe the price of

the parent company‘s common shares and subsidiary‘s shares after IPO, and we know the

amount of outstanding shares for each). Their argument was that those cases of a ‗negative stub‘

are clear violations of the LOOP, whereas a positive stub may or might not be.101 Given their

filter for selecting ‗carve-outs‘ that also had defined ‗spin-offs‘, their formula for ‗stub‘ was the

following:

100 In these cases, they identified those cases where the ‗spin-off‘ consisted of the parent company giving the non-

IPOed remaining subsidiary shares to the parent‘s shareholders. They were attempting to limit the argument thattheir relative price comparison was being something other than actual observed prices for the parent and IPOedsubsidiary.101 That is, we expect that the equity value parent company‘s other businesses are worth something positive, we justdon‘t know how much. Thus, for example, the true value of the parent company‘s other businesses might be 100, yetwe observe 1,100. Clearly, this appears to be a large positive mispricing, but we cannot know this without anaccepted valuation ‗model‘. Again, there is the ‗joint hypothesis‘ issue. Specifically, we don‘t have a clearlyaccepted valuation model with which to analyze and test mispricing. But we do know that logically the value cannotbe negative, or else finance is turned on its head.Of course, based on the same logic, a zero value also might denote mispricing.




, where = the ‗stub‘ as a fraction of the parent, = the parent stock price per share at date 0,

= the subsidiary stock price per share at date 0, x = the ratio of subsidiary shares that are

given to parent shareholders at the distribution date (i.e., for the ‗spin-off‘), and = the ‗stub‘

value. Again, they are trying to imply the value of the unknown ‗stub‘ from the known parent

and subsidiary values (traded share prices and known shares amounts). The major point of the

analysis is that in no case should the parent‘s other businesses be negative in value. Thus, in no

case should there be a ‗negative stub‘; additionally, it follows that a ‗negative stub‘ is a clear

violation of the LOOP.102

Of the eighteen cases analyzed, six or one third turned out to be ‗negative stubs‘. Of those six,

the case of the parent 3Com and its subsidiary Palm is particularly striking (see the following

graph of the implied stub share price). At one point the ‗negative stub‘ was valued at -$22 billion

or about -77% of the parent 3Com (i.e., all other businesses outside of Palm).103

Per share, the

‗negative stub‘ worked out to be about -$60 per share (i.e., per the above calculation) at its

greatest deviation from intrinsic value.

102 In a sense this would be a violation of a simple and logical boundary condition.103 Mitchell et al. (2002) make a similar analysis of 3Com and Palm, and also find a significant ‗negative stub‘.




Source: Lamont and Thaler (2003, p. 240).




It has been proposed that at least part of the explanation for the mispricing in cases like

3Com/Palm can be explained by investor overconfidence and salience/limited-attention effects104

(see, e.g., Daniel et al. (2002, p. 154)). In addition, ‗noise traders‘ may often play some role in

creating risk that is difficult to quantify, but may retard arbitrage efforts.105 Finally, Lamont and

Thaler (2001) and Mitchell et al. (2001) describe some of the frictions not allowing people to

short Palm and go long 3-Com. Whatever the reasons, it seems very likely there are limits to

arbitrage that effectively stifle arbitrage of what are viewed by academics as clear cases that

should be arbitraged, yet are not (i.e., at least in the way that traditional finance textbooks

suggest).

Lamont and Thaler (2003, p. 265) concluded by stating: ―There are two key findings of this

paper that need to be understood as a package. First, we observe gross violations of the law of

one price. Second, they do not present exploitable arbitrage opportunities because of the costs of

shorting the subsidiary. In other words, the no free lunch component of the efficient market

hypothesis is intact, but the price equals intrinsic value component takes another beating.

… Why should we be concerned? …

We think that a sensible reading of our evidence should cast doubt on the claim that market

prices reflect rational valuations because the cases we have studied should be the ones that are

particularly easy for the market to get right.‖

104 One of the more egregious documented set of cases is corporate name changes during the technology bubble,where, for example, Cooper et al. (2001) found that companies that merely changed their names to Internet relatedor ‗dotcom‘ names produced excess returns of around 74% for the ten days around the announcement, and the effectseemed permanent.105 ‗Noise traders‘ are often put forth as a possible reason/excuse for deviations from market efficiency, especiallymore recently. A classic theoretical reference on ‗noise traders‘ is De Long et al. (1990).




In short, the cases they reviewed received relatively high publicity and at the time analyzed

represented relatively large, liquid companies. If the markets don‘t get the price right on each of

those cases, which ones would we expect them to get the price fundamentally correct? Therefore,

by inference, what hope do we have for less liquid assets? Answer: ―Not bloody likely.‖ In fact,

there are many more cases where negative ‗stubs‘ have existed and persisted (see, e.g., Mitchell

et al. (2002)).106 In the final analysis, it often isn‘t even arbitrageur s that correct such obvious

deviations from intrinsic or true value, but the companies themselves.

Again, not only are such direct tests of the ―price is right‖ rare, but the current version of

EMH/EMT may be completely insulated from what proof we do have. Although, at a minimum,

the following can be inferred:

1. Internet ―carve-outs‖ clearly show that the price can be wrong.

2. Internet ―carve-outs‖ seem to show that ―free lunches‖ are hard to come by.

3. Therefore, Internet ―carve-outs‖ can be said to be cases where there is no ―free lunch‖,

but the market is not efficient (or alternatively, the price is not right).

In fact, Internet ―carve-outs‖ show that irrational traders can determine pricing for some large

seemingly liquid financial securities. Thus, the price is wrong, but there is no ‗free lunch‘,

106 In their study, they analyze 82 cases where the subsidiary is worth more than their parent (i.e., all ‗negative stub‘cases). They also find (Mitchell et al. (2002, p. 553) that ―negative stub values are not risk-free arbitrageopportunities.‖ In many cases the parent companies ultimately devise ways to eliminate the arbitrage or an outsideentity may acquire the parent and/or subsidiary, but the arbitrage may last for longs periods of time. Like Lamontand Thaler (2003), they too interpret their results to strongly support the ―package‖ notion that: (1) the equitymarkets analyzed are not priced correctly, yet (2) it may be difficult to take advantage of this (especially risklessly).




whereas textbook finance has always assumed that as long as there was no ‗free lunch‘ the prices

must be right. The key to this seeming contradiction of basic finance is limits to arbitrage.

If ‗twin shares‘ and ‗carve-outs‘ don‘t support basic economic arguments concerning arbitrage,

are there any other areas where we are confident of the ‗model‘ and/or analysis to show whether

basic arbitrage is of questionable validity? A third area we could check is CEFs; and by now the

reader should be realizing that these examples are more likely to be the ―tip of the iceberg‖ than

the iceberg.

CLOSED-END FUNDS (―CEFS‖)

In its typical and most simple configuration, a CEF is a fund with a fixed number of shares

outstanding.107

Typically, these shares are offered for public purchase in an IPO, after which they

are often traded on a stock exchange (but can also often be traded Over-The-Counter or ―OTC‖).

107 There are some that will issue shares periodically, but unlike an open-ended fund this is typically not done daily.This also ignores levered CEFs.




These shares represent an underlying interest in a portfolio, often bonds or stocks, but also

covering most other standard financial assets. The market price for CEFs is determined at one or

more stock exchanges by buyers and sellers of the CEFs. In practice the traded exchange derived

price can and usually does diverge from its theoretical intrinsic value called Net Asset Value

(―NAV‖). If the market price is less than its NAV it is said to be trading at a ―discount‖, if the

market price is higher than its NAV it is said to be trading at a ―premium‖. Normatively, market

prices should not diverge from their respective NAVs, but they do.108

For CEFs the intrinsic model/formula is as follows:

, where = Net Asset Value of fund i at time t , and = the value of the fund i

at time t is equal to the sum of the value of its N securities (i.e., the sum of all j securities from 1

to N at time t). Like equity ‗carve-outs‘ we are relying on the LOOP to check mispricings, but

unlike equity ‗carve-outs‘ there is no need to infer any pricing (i.e., with ‗carve-outs‘ we implied

the value of the ‗stub‘). Thus, the value of a fund at any time is simply the sum of its known and

valued parts. Therefore, unlike carve-outs we can check for all deviations from zero (negative

and positive). Also, note that for our purposes and for the sake of simplicity, the term fund and

portfolio are interchangeable.

108 In addition they tend to be significantly more volatile than their underlying asset values would indicate (see, e.g.,Pontiff (1997)). Pontiff (1997) indicates that they are about 64% more volatile on a month-end average basis, and itis not caused by non-synchronous or infrequent trading. This is relatively strong evidence against basic efficientmarket theory.




For example, if a portfolio consisted of only two equity securities (call them A and B) that had a

per share market value of $10 and $20 at time t , respectively, and the portfolio owned 2 million

and 1 million shares at time t , respectively, then the value of the portfolio/fund would be $40

million, by definition. It is simply the LOOP establishing what should minimally be the

normative case regarding valuation.

With CEFs we actually know the pricing model, or at least we are very confident of it. Besides

‗twin shares‘ and certain equity ‗carve-outs‘, this may be the one area of finance where we

actually know the pricing model with precision, which is why it is such a clean example of the

first pillar of behavioral finance (i.e., limits to arbitrage)109

, as well as possibly being a good

example of the second pillar.

Regarding other possible explanations of the pricing ‗model‘ for CEFs, academics have tried to

put forth rationalizations of why NAV is not the appropriate ‗model‘ (see, e.g., Schnabel‘s

discussion (1992, pp. 392-394)). In short, the excuses have been pathetic, convoluted, complex,

and mostly normative with little or no actual evidence, or complex and strained interpretations of

the evidence (see, e.g., Malkiel (1977) or Malkiel (1995)110

). More recently, finance academics

109

Pontiff (1996, p. 1135) explicitly states that: ―Arbitrage costs lead to large deviations of prices fromfundamentals.‖ Also, he finds that deviations (whether discount/negative or premium/positive are increased byfunds: (1) that are difficult to replicate, (2) with smaller dividends, (3) with lower market values, and (4) wheninterest rates are high.110 Malkiel (1995) cited the following possible reasons for deviations from NAV: (1) turnover of fund shares, (2)dividend distribution or payout policy, (3) insider ownership, and (4) -(8) ―other variables‖ – which include, but arenot limited to, (4) expense ratios, (5) previous track record of the fund, (6) foreign ownership, (7) absolute pricelevel of the fund, and (8) fund size. In addition, although not analyzed, he posits a ninth variable, ―reputation and public relations effects‖ (Malkiel (1995, p. 37)). That most, or all, of these potential explanations don‘t directlysupport the notion that discounts and/or premiums can be well explained by the EMH/EMT doesn‘t seem to bother




have seemed to reconcile themselves to NAV (see, e.g., Brickely and Schallheim (1985) on

opening CEFs and convergence to NAV), but it has taken time and they do not seem to have

gone without reservations. In short, admitting to a pricing model opens one up to an actual test,

and we can‘t have that, can we? Academic history aside, there are two basic yet conclusive

reasons to be confident in why NAV is the appropriate pricing model: (1) the portfolio managers,

and their related investment companies, of the funds say and act as if it is, and (2) when a CEF is

―opened‖ the market price converges to NAV.111 Thus, even ignoring what the ―professionals‖

say or do, and much like the case of ‗spin-offs‘ combined with an equity ‗carve-out‘, the openingof a CEF results in a price convergence to NAV, thereby proving that even the market

understands what the true value model is when forced to (i.e., regardless of what financial

economists normatively theorize).

Assuming we have the correct ‗pricing model‘ (which I have great confidence that we do), any

deviation of a fund‘s market price from NAV represents mispricing. Do we have evidence of

mispriced CEFs?

As of one date112

I took the largest discount and premium CEFs and two others as examples.

First, the largest discount CEF.

the author, yet he suggests as much. Although, Malkiel (1977, p. 857) does admit that: ―This suggests that marketpsychology has an important bearing on the level and structure of discounts.‖ 111 That is, when the portfolio of the fund is actually closed and sold off, the overall value it receives for the portfoliois approximately, if not exactly, equal to its NAV. Again, see, for example, Brickley and Schallheim (1985), orBrauer (1984).112 September 5, 2009, for data typically ending on September 4, 2009 or August 31, 2009.




Source: Data from www.etfconnect.com/.

Not exactly a pillar of market efficiency? Remember, normatively the line should be right on top

of zero, but during this time period it seems to be always and significantly below zero.

Therefore, it is always discounted from July 1993 through August 2009 (and moves from a high

discount of about 73% to a low discount of about 10%, which translates to a swing of about 63%,

but market values are always below NAVs during this period, which was as far back as the data

went).

How about the largest premium CEF for that date?

-75.0%

-70.0%

-65.0%

-60.0%

-55.0%-50.0%

-45.0%

-40.0%

-35.0%

-30.0%

-25.0%

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

5.0%

J l - r - -

J l - 9 5

r - -

J l - r - -

J l - r - -

J l - r - -

J l - r - -

J l - r - -

J l - r - -

J l -

Discounts/Premiums for the Equus Total Return Fund





For the last nine months or so the fund has been selling at a premium to its NAV, but before that

it has swung from premium to discount and back again. The absolute deviation from May 2005

until August 2009 has been in excess of 80%.

Given that the first two were recent extremes, how about a specialty stock CEF and a bond CEF?

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

55.0%

60.0%65.0%

70.0%

-

J l - - -

J

- r - -

J l - - -

J

- r - -

J l - - -

J

- r - -

J l - - -

J

- r - -

J l - -

Discounts/Premiums for the PIMCO Global Stock+Income Fund





In this case we have a relatively long history of relatively volatile discounts and premiums,

especially premiums. The absolute deviation from September 1995 through August 2009 is over

90%.

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

5.0%10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

55.0%

60.0%

65.0%70.0%

75.0%

- r - -

J

-

J

- - r - t - - -

J l - - - r - -

J

-

J

- - r - t - - -

J l - - -

Discounts/Premiums for the Templeton Russia & East European Fund





In this case we have a tax-exempt New York bond CEF that even with relatively less volatile

underlying securities (i.e., say relative to the previous Russian and Eastern Europe stock CEF)

still manages to realize an about 30% absolute movement over about ten and one half years. I

could go over more individual cases, but it now might help to be more aggregated.

What follows are showing median discounts/premiums for groups of CEFs.113

113 According to the Closed-End Fund Association‘s website, as of September 4, 2009 there were 676 CEFsrepresenting about $185 billion in NAV.

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%

J

-

J

- - r - - -

J l - - - t - r - -

J

-

J

- - r - - -

J l - - - t - - -

J

-

J

-

Discounts/Premiums for the Eaton Vance New York Muni Income Trust




As you can see, sometimes the discounts and premiums of various groupings of CEFs move

together and sometimes they don‘t (but mostly they do). One thing that can be said with a high

degree of confidence is that there are large discounts and sometimes premiums across the five

groupings in the graph (Latin American equities, Pacific equities excluding Japanese, value,

growth, and general bond), and, of course, these markets aren‘t efficient in the current textbook

sense of the term. In fact, it is normal for the market price of the median CEF to significantly

diverge from its NAV. It could be said, that much like a broken clock, to the extent the price is

right, it seems more due to random chance than by the forces of something like arbitrage.

Regarding the specific co-movements of the median group discount/premium to that of the other

four, the following table should provide some background.

-45

-25

-5

15

35

55

75

D i s c o u n t / P r e m i u m

Date

Various Closed-End Fund Groupings' Median Discounts/Premiums

Latin American Funds

Pacific Ex Japan Funds

Value Funds

Growth Funds

General Bond Funds




More specifically, the correlations between the five groups are interesting. For some pairs the

correlations are negative (e.g., Latin American and General Bond) and for others positive (e.g.,

Pacific and General Bond). Therefore, clearly, not only do individual market prices significantly

deviate from NAVs, but groups can and do as well. In addition, not only do group market values

deviate from their NAVs, but group deviations can significantly deviate from other groups.114

Furthermore, not only do groups deviate from other groups, but the sign of that deviation can be

negative or positive (i.e., not all groups are impacted by the same discount/premium forces at the

same time).115

If individual and group discount and premiums are all over the place, could actual underlying

security prices deviate from economic or intrinsic value?

114 Although, they generally move together, it is just that that is not always the case.115 Thus far, the best explanation for deviations of the NAV from market price has been Lee et al. (1991). Smallinvestors are the primary power behind this explanation. Changes in small investor sentiment are correlated with thediscounts, and issuance itself.

Correlations among the Lipper CEF Groupings (monthly data - August 1988 through December 2002)

Latin American Funds Pacific Ex Japan Funds Value Funds Growth Funds General Bond Funds

Latin American Funds 1

Pacific Ex Japan Funds 0.021 1

Value Funds (0.068) (0.050) 1Growth Funds (0.088) 0.377 0.587 1

General Bond Funds (0.174) 0.592 0.256 0.399 1




THE ISSUE OF ABSOLUTE VS. RELATIVE PRICES AND ARBITRAGE

Regarding limits to arbitrage and pricing efficiency, in the three set of examples examined in this

chapter thus far (‗twin shares‘, ‗carve-outs‘, or CEFs) none were remotely supportive of the

textbook story concerning arbitrage. What is important to point out before proceeding is that all

the preceding violations of the LOOP were in many cases large and discouraging from a standard

normative financial economics standpoint, but they do not address price levels themselves being

right (i.e., efficient). At this point I have only shown cases of the LOOP were we know or are

very confident of the ‗model‘ to use to judge whether pricing is wrong from an efficiencystandpoint, but that doesn‘t mean there isn‘t even more disturbing pricing news for the

EMH/EMT.

For example, a CEF may not have a market price equal to its NAV and that is a violation of the

LOOP, but it doesn‘t mean that the prices of the securities in its portfolio are correct from a

pricing efficiency standpoint. Specifically, an equity CEF may have shares of IBM, but we

haven‘t examined whether the price of IBM is right, we only know with a high degree of

confidence that its relative market value to NAV is off say by -25%. In effect, we cannot be

confident of any of the absolute underlying stocks in the portfolio of the CEF.

Ironically, this is an endemic problem to finance. This is odd because practical finance (i.e., the

actual financial markets upon which the field of study must ultimately explain) is largely

concerned with valuing securities on an absolute basis. In academic and actual finance, most

pricing ‗models‘ assume that price levels are correct or efficient. This is highly unlikely. I will




come back to this issue in a later chapter, but it should be noted that to the extent the LOOP on a

relative basis can be off by more than 50% (and as shown by the examples in this chapter, it

can), it is dubious at best that absolute prices cannot diverge from true value by much more than

some of the more egregious cases of relative pricing inefficiency we have witnessed thus far.

To the best of my knowledge, most, or likely all, normative financial pricing models assume

(either implicitly or explicitly) that the absolute price of the asset is correct. Generally, normative

financial models (e.g., option pricing models) are not concerned with the initial price or

underlying price (it is assumed ‗correct‘). Therefore, empirical tests and analysis in finance

generally ignores whether absolute pricing is efficient and often infers that it is because relative

prices tend to move together. For example, bond prices tend to move together, yet the base level

seems to have heavy input from one market participant (i.e., the Central Bank – ―CB‖).116

Therefore, it could be said that on a relative price movement basis the government bond

market may be the most “efficient” financial market, yet on an absolute basis one of the

least efficient financial markets.117

Finally, as usual, the simplifying assumption is often done

for mathematical tractability (i.e., think derivatives); but by ignoring a critical issue that turns out

to be both relevant and significant, the results are dubious at best, and the pricing is anything but

efficient.

116Note, regarding the comment about the bond market(s), the absolute pricing is heavily dependent on the current

fiat system vs. traditional monetary systems throughout history. We will get to this again in the chapter concerningabsolute pricing and market efficiency.117 As per the above, we will address this issue in the chapter concerning absolute pricing and market efficiency.




‘INDEX INCLUSIONS‘ OR INDEX ADDS AND DROPS

As with clean examples of the LOOP, it is difficult to find clean examples of non-information.

That is, with the LOOP it is difficult to find cases where we can agree on the pricing ‗model‘;

correspondingly with informational efficiency it is difficult to find cases where we can control

for the information part of the price and information question. That is because, normally, many

things can affect market prices, by the simple fact that markets are not closed environments for

social experiments. Regarding clean cases of information and market prices, ‗index inclusions‘

or index adds and drops are one relatively clean set of examples, and they have the added benefit

of showing another relatively clean set of examples of limits to arbitrage.118

A key central tenant of efficient pricing is that information, especially public information, should

be imbedded in prices and non-information shouldn‘t move prices; in addition, it is assumed that

arbitrage is unlimited. Thus, not only should known information or non-information not move

prices, but, in addition, unlimited arbitrage means that demand curves for securities are infinitely

elastic (or perfectly flat). That is, arbitrage causes flat demand curves for securities. Related to

these two assumptions/arguments of ‗modern finance‘, and although not a LOOP issue per say,

as securities are added and dropped from indices this is not (i.e., from a normative finance

perspective) supposed to move prices. The rationale behind this is that the market for financial

securities is supposed to possess a ‗flat demand curve‘ (i.e., and/or perfect competition is

assumed). In such a market each share is assumed a perfect substitute for another. Remember,

118 ―IPO lock-up‖ periods might be another set, that also provide strong evidence that: (1) the demand curve forstocks is not flat but downward sloping (like the basic result for index adds and drops), and (2) there appears to be a‘free lunch‘, but seems unavailable for eating (much like the basic result for ―Internet carve-outs‖). See Ofek andRichardson (2000) for an enlightening example of these two results that add clearly to our view that the markets areinefficient, there are limits to arbitrage, and ‗free lunches‘ appear but are hard to come by.




finance is all about discounted cash flows. To the extent I can exactly or very nearly replicate a

cash flow or cash flows at the same discount rate(s), I am indifferent between the two cash flows

(or sets of cash flows), or so the normative argument goes. In short, if the cash flow(s) and

discount rate(s) is/are the same, it shouldn‘t matter where the cash flow(s) comes from. In a

market where securities are perfect substitutes, the price shouldn‘t move at all.119 Add to that,

the fact that prices move when the announcement of an inclusion is made and often after the

announcement, means that, at a minimum, we can infer that the demand curves for these

securities are not flat. Therefore, prices are being set where supply is clearly constrained

120

and

demand is clearly not infinitely elastic.121 It is the demand part that is particularly problematic for

normative finance, and it should not be happening (i.e., not from a normative theory

perspective).

Let‘s take the S&P 500 as an example. Based on several public criteria, a committee establishes

which stocks are included in the index, and conversely which ones will be dropped (see the

standardandpoors.com website for details).122

The index is supposed to represent many of the

500 largest U.S. companies listed on the major U.S. exchanges (typically these 500 are over

3/4ths

of the market capitalization of all U.S. common stocks). For the S&P 500 index a public

announcement is made toward the end of the month at least several days before the physical

change in the index is made concerning which stock(s)s will be excluded and which will replace

that/those being dropped, or just added to replace a merger, etc.. In a normal month at least one

119 This is the classic argument/story as described by, for example, Scholes (1972) regarding large blocks of stocks.120 This is to be expected in the short run.121 This is the normative surprise.122 Of course, if a firm is merged or goes bankrupt, etc., then an addition must be made.




common stock is dropped and another is added (hence the constant 500 moniker). There have

been several academic studies tracking the ‗anomalous‘ pricing behavior of those shares added to

and dropped from the S&P 500 index (which could be argued to be the most liquid, and hence,

least susceptible to this type of normatively inefficient pricing behavior).

Again, as mentioned at the top of the chapter, the problem with hedging is that there really are no

perfect hedges and/or arbitrage, and ‗index inclusions‘ is a good set of examples of that.

Sometimes, for example, there just isn‘t an exact substitute for a shar e of IBM. If you are

indexed to the S&P 500 and it is dropped from the index and XYZ company shares are added, it

is simply the case that you must sell your IBM shares and buy XYZ shares (i.e., unless you

desire to be exposed to more risks than are included in the S&P 500 index). Normative theory

might suggest alternatives, but the descriptive reality of the financial markets is that there just

isn‘t a riskless substitute for the two sets of cash flows.

Based on academic research, it has been found by Harris and Gurel (1986) that inclusions to the

S&P 500 index result in slightly more than a positive 3% price move following the

announcement123

, while the smaller set of drops examined resulted in about a 1.4% decline in

price following the announcement.124

Furthermore, they find that much of that movement is then

reversed within two weeks. Unless there is some information conveyed by the announcement

that fundamentally indicates future prospects for the adds are greater than the day before and

123 The finding is supported by others (e.g., see Wurgler and Zhuravskaya (2002)).124 In addition, Barberis et al. (2003) find that after inclusion in the S&P 500 a stock‘s ‗Beta‘ goes up. This type of result is also more easily, and more likely, explained by limits to arbitrage, rather than traditional fundamental basedmarket efficiency.




future prospects for the drops are worse than the day before, these moves are not suppose to

happen. Also, given the evidence, it is highly unlikely future profitability is what is impacting

prices (see, e.g., Harris and Gurel (1986) and Wurgler and Zhuravskaya (2002)). The most

plausible explanation is demand, and that is both evidence against unlimited arbitrage and

informationally efficient markets.

As Wurgler and Zhuravskaya (2002, p. 605), stated:

―The results of this article lead us to differ with Ross (1987), who writes, ‗not to say that theintuition and the theories of finance cannot be fit into the framework of supply and demand,

rather that doing so does not gain us much. The fit is awkward and irrelevant at best.‘ A

methodological investigation of the limits to arbitrage, and the implications of these limits for the

shapes of excess demand curves for stocks, seems likely to improve our understanding of the

growing set of empirical findings that are difficult to explain with models that assume unlimited

arbitrage.‖

Indeed, there is no evidence that unlimited arbitrage exists, yet there is growing evidence that

there are significant and varied limits to arbitrage that affect pricing in the actual financial

markets. Therefore, and although ‗awkward‘, it seems advisable to structure finance around the

notions that demand curves aren‘t flat and limits to arbitrage not only exist, but are also

significant and important.




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Chapter 5: Psychology or the second „pillar‟ of behavioral finance

Again, if economically rational arbitrageurs would likely correct most mispricing in most

markets without any limits to arbitrage, then, with respect to pricing in the financial markets, the

psychology part might be irrelevant.125

But there are limits to arbitrage, and depending on such

things as to which market and securities we are interested in, they can be ubiquitous, large, and

lasting. Thus, we have the necessary, but not sufficient condition for psychology to matter in the

financial markets. Of course, even if psychology turned out to be descriptively unimportant,

limits to arbitrage could mean that normative finance is still just plain silly. That is, specifically,

basing mathematical finance ‗models‘ on assumptions that are known empirically to be untrue

could be, at best, counterproductive. Furthermore, if it turned out to be the case that of the two

‗pillars‘ to behavioral finance that only the limits to arbitrage piece survived true empirical

scientific scrutiny, then I would still argue that the current normative emphasis in finance is

badly misplaced and should be replaced with a descriptive methodology and emphasis.126

Therefore, consider the psychology part of behavioral finance a kind of descriptive bonus gift.

In this chapter we will proceed as follows:

1. List off the short list of documented psychological biases that are likely to impact pricing

in the financial markets.

125 Of course, in the first place, there is the issue of pricing itself and whether we can ever know what marketefficiency is empirically.126 The problem with behavioral finance, and especially psychology, isn‘t that it can‘t explain actual financial marketphenomenon, the problem is that it can potentially explain anything. Therefore, in a sense, it often explains toomuch without the typical normative simplifications we have come to expect within economics. That is, it can oftenlack the parsimony we often find in natural science theories.




2. Regarding decision biases, list off a more complete list of documented psychological

biases that cover most of what could possibly affect decision making in the financial

markets.

3. Briefly review neuroeconomics.

4. Give an example of likely direct link from psychology to pricing in certain specific

financial markets.

It is important to remember, that these lists of ―offenses‖ are only offenses to normative finance,

statistics, economics, etc. Psychology doesn‘t consider them odd, by definition and descriptively,

they just are. Thus, from a psychological and/or biological viewpoint humans on average don‘t

have ―cognitive biases‖ per say, but they probably serve some purpose, possibly evolutionary at

some stage of evolution, even if they systematically result in financial losses due to trading in the

financial markets.




THE SHORT LIST OF KNOWN OFFENSES

Cognitive psychologists have noted the following systematic biases that relate to markets (mostly

related to beliefs and preferences):

1. Overconfidence (people are ‗poorly calibrated‘ when estimating probabilities and tend to

provide too narrow ranges).

2. Optimism and wishful thinking (e.g., everyone is above average, and underestimates

how quickly a task will be accomplished).

3. Representativeness (e.g., Tversky‘s and Kahneman‘s (1974) example).

4. Conservatism (seemingly in contradiction to representativeness).

5. Belief perseverance and confirmation bias (which is a stronger version of belief

perseverance). In short, denial is easy for most of us.

6. Anchoring (too much weight on initial value, then adjust too slowly).

7. Availability biases (e.g., probability of getting mugged in NYC).

A specific measurable characteristic of overconfidence is that when people are ‗poorly

calibrated‘ when estimating probabilities they tend to provide too narrow ranges.127 To the extent

to which there is a mainstream128

behavioral finance, overconfidence is probably considered one

of the most important psychological biases displayed by most humans. This is because

overconfidence may have significant impacts on both pricing and the willingness to trade itself.

In addition, it may be due to at least two other biases (see Barberis and Thaler (2002, p. 12

127 When overconfident the range is too small, whereas when underconfident the range is too wide – which coversonly a small fraction of the population relative to overconfidence.128 Given that it is marginalized, often ignored, and assumed to be an add-on to normative finance, this is a bit of anoxymoron. The point is that those that have published in primarily mainstream finance journals have spent a gooddeal of time referring to this as a potential psychological cause of much financial market mayhem.




footnote #10)): self-attribution bias (where people see their own talents as being responsible for

something positive, and blaming bad luck for negative outcomes) and hindsight bias (after an

event has occurred many people tend to take credit for predicting it; and people tend to claim

they predicted the past better than they actually did). Obviously, if overconfidence is a good

example, then the psychological piece isn‘t easy to compartmentalize. Like overconfidence,

many psychological phenomena are interrelated with other psychological phenomena (and likely

limits to arbitrage).

Some classic biases related to optimism and wishful thinking are: (1) Students systematically

overestimate how they will do on exams. (2) Even after knowing the actual statistics, almost all

newlyweds expect their marriages to last forever. (3) Typically over 90% of people believe they

are above average in driving skill, humor, and ability to get along with others, etc. (see Weinstein

1980)). Simply put, people tend to be overly optimistic about positive outcomes and under

optimistic about negative outcomes.129 It results in applying too high of probabilities to positive

outcomes and too low of probabilities to negative outcomes. This is fine, but applied to buying

and selling financial assets it may be normatively problematic for the investor and overall

pricing.

Representativeness or how alike something is to that which is known. Tversky and Kahneman

(1973) originated the notion of a representative heuristic, which is a rule of thumb wherein

people tend to judge the probability or frequency of something by considering how much that

thing or concept resembles available data on like things or concepts, as opposed to using a

129 Although, there is a tendency for people to overestimate small negative probability events.




Bayesian130

calculation. Specifically, using a representativeness heuristic can result in neglect of

relevant base rates and other cognitive biases. Representativeness also leads to another bias

called sample size neglect (people often fail to take account of the sample size when judging

probabilities; also known as the ‗law of small numbers‘, which can generate the ‗gambler‟s

fallacy‟ effect where people will say or insist that a tail event is due). Shefrin (2007, pp. 14-16)

provides an example of incoming freshman and how well they will do based on high school

grades vs. how well they actually do. It can also lead to such silliness as ‗hot hand‘ phenomenon

(where people expect, for example, an athlete to perform above their normal ability level during

a ‗streak‘). Regarding occupations, Kahneman and Tversky (1974) showed people who tried to

predict by taking the closest match to past patterns, without attention to the observed probability

of matching the pattern (also, see Shiller (2002, p. 18)). Specifically, for example, if an

occupation is very rare, but a person knows someone with that occupation and a description of

another person seems representative of that person with the rare occupation people tend to

indicate the probability of the other person having that occupation is much higher than it really

is. The relationship to finance is that saliency and related representativeness may drive decisions

and associated probabilities much more than they normatively should.

Conservatism leads to an over-emphasis on base rates, while representativeness leads to an

underweighting of base rates. This is due to the saliency of the model used.131

For example, in

130 From Wikipedia: ―Bayesian probability is one of the most popular interpretations of the concept of probability. The Bayesian interpretation of probability can be seen as an extension of logic that enables reasoning with uncertainstatements. To evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability,which is then updated in the light of new relevant data. The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation.‖ 131 For example, Shiller and Pound (1986) create an epidemic contagion model where saliency ―infects‖ institutionalinvestors. The survey they circulated strongly indicated the importance of social contagion and saliency.

http://en.wikipedia.org/wiki/Base_rate_fallacy


http://en.wikipedia.org/wiki/Cognitive_biases

http://en.wikipedia.org/wiki/Probability




http://en.wikipedia.org/wiki/Cognitive_biases






the conservative case if a data sample is presented as old, people overweight their priors (i.e.,

conservatism); and if the data presented is new, they tend to overweight that (i.e.,

representativeness).

Under belief perseverance one could consider the EMH and EMT as examples. It is emotionally

difficult for most, if not all, people to allow their beliefs to be subject to potent logical or

empirical challenges. Some people can even survive the total destruction of their original

evidential bases (see Ross and Anderson (1982)). It seems that the need to maintain a false belief

is strongly related to maintaining a positive self-image and can be independent of reasoning (see

Guenther and Alick (2007)). In short, people ignore and/or pervert logic and/or evidence that

contradicts their beliefs; while under confirmation bias, people will reinterpret evidence against

as evidence in favor (again, think of the supporters of the EMH and EMT). Also, consider the

importance of cognitive dissonance, in supporting both belief perseverance and confirmation

bias, and would also seem directly related to the two.

Anchoring is a bias that describes the tendency to rely too heavily on one piece of information

when making decisions. Typically people anchor on specific information or a specific value and

then adjust from that value. Once the anchor is set, there tends to be a clear bias toward that

anchor value. Thus, different starting points can, and usually do, result in different estimates (see

Tversky and Kahneman (1974)). This is especially problematic for optimal pricing. As an

example, one test requires people to submit two digits from an identification number then shortly




after to submit a bid price for a security. Normatively the two numbers are unrelated, but there is

a strong and significant tendency for people to anchor on their essentially random identification

numbers.

Availability bias results from people tending to search for actual experiences and work from

there (e.g., if they have been to NYC and not been mugged, then they put a low probability on

being mugged in NYC, largely irrespective of the true odds of being mugged).132 Therefore,

saliency is very important for this kind of bias. For example, if the media tends to report shark

attacks but not automobile accidents, then people who have seen those reports tend to

significantly overestimate the probability of a shark attack relative to a car accident. Clearly, this

doesn‘t aid in making accurate forecasts for financial market events and related pricing in those

markets.

Although this list of seven is indeed short, most, if not all, of the seven listed offenses to

normative finance involve one or more of the other seven and one or more other concepts.

Psychology, by its nature, is nuanced. Therefore, keep in mind that underlying psychological

causes are likely to be more complicated than a simple equation.

132 The opposite effect of the availability bias is denial. Something may be so disturbing that a person is willing toview that outcome as even less likely, regardless of the probability of it happening.




THE RELATIVELY LONG LIST OF KNOWN DECISION MAKING OFFENSES

In the spirit of not ―reinventing the wheel‖, the long list is taken directly from Olsen (1998).

Source: Olsen, R., ―Behavioral Finance and Its Implications for Stock-Price Volatility‖, Financial Analysts Journal, Volume 54, Number 2, March/April 1998, p. 12.

What follows is the table represented in list format:

1. Heuristics or rules-of-thumb are relied on by decision makers. This includes, but is not

limited to, stereotypical, analogic, or other intuitive or experiential decision processes

as decisions become more complex, time grows short, or emotions run high.




2. Affect influences decisions (i.e., people want to have a positive affect). Decision makers

want to feel good about their decisions, sometimes (or often) even if the decision is

suboptimal from a normative economic viewpoint.

3. Outcomes tend to be discounted inversely with the size of the outcome.

4. Tendency to overweight confirming evidence and underweight disconfirming evidence.

Again, positive affect can drive the decision.

5. Overweight probabilities of favorable outcomes and underweight probabilities of

unfavorable outcomes. Again, the need for positive affect can drive the even the

evaluation of the decision.

6. Overweight salient and/or memorable facts and evidence. We tend to focus on saliency,

often at the expense of more normative statistical method.

7. Overweight low-probability events and underweight high-probability events (low-

probability events are often treated as certain not to occur, and high-probability events are

often treated as certain to occur).

8. Fail to take account of regression to the mean (i.e., they tend not to be regressive). Thus

decision makers are often surprised by what should often normatively be expected.

9. People are social animals; therefore, decisions are heavily influenced by needs for

group acceptance and their fear of group regret. Essentially, herding is difficult to

avoid.133

10. Discount future losses at higher rates than future gains.

133 Even PMs show a very strong tendency to herd (see, e.g., Hong et al. (2005)).




11. Mental accounts based on social and economic criteria. For example, one pool of money

is set aside for a college fund, and is treated differently than one set aside for a vacation

trip to Spain next year.

12. In actuality, people are loss averse not risk averse. Essentially, normative economics has

misinterpreted human risk, and/or ignored its descriptive reality.

13. Preference for an ascending pattern of returns. Therefore, the pattern of returns can be a

critical factor in an investment decision (i.e., even if normatively it shouldn‘t).

14. Diminishing returns for gains and losses.

15. Focus on changes in vs. absolute levels of decision attributes. This would seem to be

reference point phenomenon related.

16. Outcome gain and loss segregation and/or aggregation among decisions to enhance

perceived net benefit.

17. Perceptions of risk vary inversely with perceptions of control. This can be critical in an

organizational context (e.g., would rather not make money than encounter even the

chance of a partial loss of control).

18. More weight is placed on information that is presented in a format consistent with the

choice format (e.g., a numerical decision and numerical information vs. a verbal decision

and verbal information). Often, the format and/or framing matters.

19. Stress induces a heavier weight on negative evidence. Stress can often short circuit

explicit processing.

20. Stress tends to reduce the portion of information considered. Again, stress can often short

circuit explicit processing, and in this case even contribute to ignoring basic information.




21. Tendency to overestimate the probability of conjunctive events. People just have a

tendency to be bad at probability and especially contingent probabilities.

22. Small samples are treated as overly representative of populations.

23. People tend to be unaware of the weights they place on pieces of information in a

decision. Thus, you can get probabilities summing up to more or less than unity.

24. More weight is placed on personal information than impersonal information (e.g., family

members‘ advice).

25. Tendency to overestimate one‘s ability to forecast past events (hindsight bias).

134

26. Tendency to overestimate one‘s ability to forecast, and generally make correct decisions

(see Fisher and Statman (2000)).

27. Information tends to be used in the form given.

28. Tendency to overestimate the variability of a series, especially a random series.

29. Confusion between precision and reliability (and to view quantitative information as

more reliable than non-quantitative information).135

30. Forecasting tends to be based on an anchor value and adjustments from the anchor tend to

be insufficient.

31. Greater weight is placed on information that has been made to seem complete by adding

nondiagnostic facts.

32. Greater weight is placed on nondiagnostic information as diagnostic information becomes

ambiguous.

134 This one is can be related to cognitive dissonance. For example, Goetzmann and Peles (1997) ―find that mutualfund owners recollect that their funds performed much better than was in fact the case.‖ Biais and Weber (2009)found that investment bankers in London and Frankfurt had ―significant hindsight bias‖, and that the majorityremained biased even after being informed of the degree of their bias.135 There is also a tendency to reject all numbers if just one is wrong (and this can be aided by confirmation bias).




33. Information is weighted by its arrival timing/date (the earliest data gets the most weight

for simple decisions, while the latest data tends to receive the most weight for the more

complex decisions).

34. More stress causes more inconsistency.

35. Consensus judgments are overestimated (the ―two heads are better than one‖ approach).

36. As numeric information becomes more ambiguous, more weight is given to nonnumeric

information.

37. The physical format of the information supplied influences the accuracy and speed of the

decision process.

38. Task participation is enhanced by immediate and dynamic feedback.

39. Tendency to overweight current beliefs and feelings results in very inaccurate forecasts of

future hedonic (pleasurable) states (people are ‗locked into the present‘).

40. As forecasting becomes more difficult, greater weight is placed on the anchor value.

Based on the forty item list, it should be clear that forecasting specifically, and decision making

generally, based on data and other information, just isn‘t a strong suit of humans. There is much

that can go wrong and often does. It is really a question of personnel, organizational structure,

and incentives as to whether these types of biases can be overcome.




NEUROECONOMICS – LINKING THE HUMAN MIND TO THE MARKET

CHOICE/ACTION

Neuroscience is the study of the nervous system. The nervous system in turn is composed of

cells called neurons, and other supportive cells. Neurons are cells that produce intellectual

behavior (e.g., choice), cognition, emotion, and physiological response. Neuroeconomics is an

attempt to combine neuroscience (especially cognitive and behavioral neuroscience) with

normative and descriptive economics (but especially normative). With respect to economics

generally, the seemingly obviously applicability of neuroeconomics is more on the demand than

supply side.

In addition, like those that propose behavioral finance as an add-on to finance, many currently in

the field seem hypersensitive of the need to: (1) not anger those economists that promote the

current normative economic paradigm (e.g., Camerer et al (2005, p. 55) first suggest an

‗incremental‘ approach ―in the short run‖, but ―we believe that in the long run a more ‗radical‘

departure from current theory will become necessary‖, or Glimcher et al. (2007, p. 146)), and (2)

to create mathematical ‗models‘ (e.g., Caplin and Dean (2008)). Obviously this is misguided. To

the extent neuroeconomics is descriptively true, it will often and likely lead, not follow; and with

respect to mathematics, to the extent the brain can be modeled accurately in a purely

mathematically way, great. Psychologists, psychiatrists, neurologists, and other scientists have

been modelling human behavior for some time, it is only logical that relatively inaccurate

economic ‗models‘ be swapped for more accurate behavioral ones without all the silly

assumptions.




The potential of neuroeconomics helping behavioral finance can be summed up with the

following quote from Camerer (2008, p. 419): ―Neuroscience will show more clearly conflicts in

which behavior is biologically plausible rather than logical.‖ In short, by appliyng a true science

(neuroscience) to economics what actually happens will, by definition, be emphasized over the

normative theory with its strict interpretations of such things as rationality. Put another way,

human biological reality will confront normative theory directly.136

The following diagram contrasts neuroscience with behavioral economics and economics.

136 My guess is that actual science will win.




Source: Camerer, C., ―Neuroeconomics: Opening the Gray Box‖, Neuron, Volume 60, Issue 3, November 2008, p.417.

The neuroscience view is composed of the left two columns of five boxes with connecting lines

and arrows, the behavioral economics view the second from the right column of four

unconnected boxes, and the classic economics view the far right column of four unconnected

boxes. As should be painfully clear, cognition and related things like economic choice are in

reality processes. Under neuroscience we move from representation to valuation to action, then

outcome evaluation and learning with connections and feedback sometimes occuring, sometimes

not. Under traditionial/current economics and finance, representation and valuation (which for

most depend on internal and external states) never happens, yet utility is maximized. Under

behavioral economics the possibility of representation and valuation mattering is there, yet there

is no connection between the actions. It is important to remember that this is the representation

of someone who fancies himself or herself a neuroeconomist, and hence behavioral economics

and, of course, economics are found wanting. For me, I see rationally designed behavioral

finance as something like the following:




Source: modified from Camerer, C., ―Neuroeconomics: Opening the Gray Box‖, Neuron, Volume 60, Issue 3,November 2008, p. 417 and Nov, Y., and O. Nov, ―Living in a bubble? Toward a unified bubble theory‖,International Journal of General Systems, Volume 37, Issue 5, October 2008, p. 629.

Therefore, I find no problem integrating that which fits the above concpetual model of

behavioral finance as I have tried to lay it out in this book. In my mind, neuroscience must be

included as it will reconcile biological and cognitive reality with financial market reality. In

addition, there are normative valuation approaches that ceratinly indicate that actual prices trend

toward them, at least, in the long run; but over the short to medium term (which can be decades)

values can drift away from those anchors. Clearly, it is some combination of market

Financial Object’s

Driving Value(i.e., financial market

determined price or actualprice)

Financial Object’s

External Value(i.e., it’s fundamental or true

economic value)

Behavior(i.e., buying, and/or selling,

and/or holding behavior)

Behavioral Finance Components of Valuation

Limits to Arbitrage/Market Microstructure

(i.e., taxes, transaction costs, legalissues, irrational traders, lack of

an exact substitute, etc.)

Psychology &Neuroscience

(i.e., biological predisposition,cognitive predisposition, cognitive

limitations, etc.)

NormativeEconomics(e.g., discounted

present value)




microstructure and its associated limits to arbitrage interacting with human cognitive limitations

and their associated biases that results in ‗market inefficiency‘ seemingly dominating pricing in

most financial markets most of the time. For me, this seems obvious, yet I also realize

established ‗authorities‘ and ‗professionals‘ that have based their careers on largely normative

theory can have difficulty accepting a primarily descriptive methodology (hence, this book).

Contrast the above diagram with a representation of normative finance:

Normative finance strictly holds that the ‗price is right‘ always and in all markets (i.e., strict

EMH/EMT), and ‗bubbles‘, etc. are therefore impossible with unlimited arbitrage and one or

more ‗rational‘ investors. Therefore, limits to arbitrage and market microstructure are largely

irrelevant. Additionally, psychology is irrelevant in a world with unlimited arbitrage and

‗rational‘ economic agents/actors. Finally, the models of normative finance merely reflect all the

Financial Object’s Driving Value =

Financial Object’s External Value(i.e., financial market determined price or

actual price = it’s fundamental or true

economic value)

Behavior(i.e., buying, and/or selling,

and/or holding behavior)Purely „rational‟ behavior

Normative Finance Components of Valuation

NormativeEconomics(e.g., discounted

present value, CAPM,Black & Scholes, etc.)




‗rational‘ agents and unlimited arbitrage, and are not required for valuation because market

participants who set prices at all times and in all markets already derive a similar, if not exact,

version in their minds, as their actions should reflect this.

Yet descriptive reality is considerably different than the normative finance diagram. I believe the

facts are much closer to, and more plausibly fit, the behavioral finance version. Furthermore, if

that is true, I suspect that neuroscience will have a large impact on connecting the proverbial dots

between the physical realities of the market with the physical realities of the markets.

Currently, when neuroeconomics is considered, I suspect that most people first consider

cognitive neuroscience. Cognitive neuroscience examines questions concerning how

psychological and/or cognitive functions are produced by the neural circuitry. Probably the most

common images today are the measurement techniques associated with various forms of ―brain

scanning‖ devices (e.g., fMRI – functional Magnetic Resonance Imaging, PET – Positron

Emission Tomography, etc.). Much of the work to date has indeed focused on using such

techniques to identify and map areas of the human brain (i.e., map the specific neural circuits)

that are used to address questions of economic choice (i.e., choice behavior as it relates to

economics). Clearly, the ability to directly measure thoughts and feelings should be

extraordinarily useful in analyzing decisions and choice, especially in finance and economics.

Some examples of this are:




Uncertainty itself strongly biases decisions/choice (see, e.g., Platt and Huettel (2008)).

Therefore, uncertainty itself is important and can, for example, impact an individual‘s

uncertain choices. Furthermore, this bias varies significantly across individuals and brain

systems. Although significant, standard finance models do not incorporate this bias.

Normal adults are capable of both mentalizing (i.e., a form of ‗mind reading‘) and

empathizing (see Singer and Fehr (2005)). These abilities are especially useful for

making choices when other people are involved in the decision making (e.g., related to

game theory type constructs, but real ones), yet standard financial models and related

game theory models include neither documented affective trait/ability.

‗Money illusion‘ is real. Normative economics strongly assumes that people value money

in real not nominal terms, yet they tend not to. Based on fMRI, a direct link between

money illusion and brain activity has been found (see Weber et al. (2009)). Money

illusion is extraordinarily important for finance and economics, yet standard finance

models do not even acknowledge its existence.

Not only is a lack of trust thought to inhibit economic transactions, but responses are

asymmetric between the genders. When distrustful, men generally show heightened

levels of DHT (a hormone) that women do not show, which translates into heightened

levels of agression for men (see Zak et al. (2005)). Given that it has been shown that

‗high trust‘ societies tend economically to perform signficantly better than ‗low trust‘societies, these sorts of neurological links to economics might be important, yet standard

finance and economic models do not even acknowledge their existence.




Beliefs matter. They matter because they ―play a substantial role in the behavior of the

financial markets.‖ (see de Bondt (1995, p. 7) for a review)

When faced with a loss, people tend to act differently than with gains. In fact, this is a

mild form of the same behavior seen in addictive gamblers (see Chew and Peterson

(2005) for a summary). Therefore, people tend to act as ―risk lovers‖ under one set of

conditions and typically ―risk averse‖ in another. These types of well documented

behaviors are not incorporated into standard finance and economics models.

Contrary to standard Discounted Utility Theory (―DUT‖), humans generally are irrationalwith respect to intertemporal decisions (i.e., irrational in the economics sense of the

term). A biological connection for these basic time-preference violations has been found

(see Kalenscher and Pennartz (2008) for a review of evidence and theory). Even though

these violations appear real, DUT relies on them not existing.

Aging impacts decision making. Over an adult lifespan an individual‘s dopamine and

serotine system changes. In particular, the ability to produce those two compounds

decreases. It has been noted that generally humans‘ behavior toward such things as

financial market risk also changes with age. Thus, financial decision making changes

significantly with age. Neuroeconomists have shown the direct link between dopamine &

serotine systems, aging, and decision making (see Mohr et al. (2009)). These are

critically important findings for a normative field, such as modern finance, that doesn‘taccount for such physical realities.

Etc.




Clearly, the list is not meant to be exhaustive. What the reader should glean from

neuroeconomics is that it emphasizes the descriptive causal links from and to economics and

neuroscience. I believe it not only shows promise, but may end up being critical to behavioral

finance‘s primarily descriptive development.

In my opinion, the current gap between econimcs as a ‗social science‘ and say neuroscience as a

‗natural science‘ should largely be eliminated. If economics (and finance) want to be considered

a science, then it needs to become more natural (specifically, more natural science) and less

assumption driven. Sure, we tend to be social creatures, but economics and finance have been too

long drifting in their oddly mathematical and assumption laden realm where reality and theory

seemed to be meeting less and less often.




A SAD EXAMPLE OF PSYCHOLOGY AFFECTING SECURITY PRICING

This chapter wouldn‘t be complete without some example of the second ‗pillar‘ of behavioral

finance affecting pricing in the financial markets. Although difficult to prove conclusively137

, the

example selected is what is called Seasonal Affective Disorder (―SAD‖) or the ―winter blues‖

(also called ―winter depression‖). SAD is essentially depression that is thought to be caused by

fewer daylight hours.138

It is also thought to cause anxiety.139

Minimally, its impact depends on

the relative latitude, sunlight hours actually experienced, and person or possibly population. For

example, among the very high latitude countries Iceland is considered to be an exception,

140

and

women tend to be more affected more than men. It is hypothesized that given the reduced food

availability during winter the farther north, ceteris paribus, reduced activity during that time

likely would have conferred a reproductive advantage. Normative finance would not recognize

such a thing as having the potential to affect asset prices, whereas behavioral finance would be

more open to the possibility (especially given actual limits to arbitrage).

―Affective‖ in this case means emotional. SAD is a condition that is caused by fewer daylight

hours. ―Experimental research in psychology has documented a clear link between depression

and lowered risk-taking behavior in a wide range of settings, including those of a financial

nature.‖ (Kamstra et al. (2002, p. 1)) ―SAD is clinically defined as a major depressive disorder.

While usually described in terms of prolonged periods of sadness and profound, chronic fatigue,

137 One never actually proves anything with 100% certainty using the Western scientific method (or just called‗scientific method‘). 138 See, for example, Avery et al. (2001) for a treatment example.139 Remember, anxiety predisposes decision makers to a host of potential cognitive biases, especially with respect toinformation, forecasting, and decision making generally.140 See Magnusson et al. (2000). Interestingly, although a diet high in fish is sighted as a possible reason whyIcelanders seem relatively unaffected, people of Icelandic ancestry in Canada also show the same relative lack of SAD, compared to, for example, Japanese (who also have a diet high in fish) or Swedes.




evidence suggests that SAD is connected to serotonin dysregulation in the brain. Furthermore,

positron emission tomography (PET) scans reveal abnormalities in the prefrontal and parietal

cortex areas due to diminished daylight, as described in the National Institute of Mental Health

study by Robert M. Cohen et al [1992]. That is, there appears to be a physiological source to the

depression related to shorter days. SAD symptoms include difficulty concentrating, loss of

interest in sex, social withdrawal, loss of energy, lethargy, sleep disturbance, and carbohydrate or

sugar craving often accompanied by weight gain. For those affected, the annual onset of SAD

symptoms can occur as early as September, around the time of autumn equinox.‖ (i.e., in the

Northern Hemisphere, Kamstra et al. (2002, p. 2-3))

To sum up, the key parts of an academic hypothesis and test of a link from SAD to equity market

returns:

1. Psychology has documented a link between SAD and depression (e.g., about 10% of

most populations affected, with about 1/3rd of that percentage being clinical and about

2/3rd

of that percentage being mild cases). Therefore, it is a marginal condition, unlike,

for example, overconfidence which is much more general. Thus, if it has an impact that

would be suggestive that more general conditions could be even more important.

2. Psychology has documented a direct link between depression and heightened risk

aversion (i.e., depressive symptoms are significantly correlated with risk aversion); as

well as to decision making more generally.

3. Therefore, SAD can affect market equilibrium through its impact on prices where

marginal sellers (i.e., SAD cases/investors in the fall being net sellers of risky assets, e.g.,




stocks) affect market equilibrium as the length of their day shortens (i.e., as the number

of daylight hours decreases).

4. Therefore, the testable hypothesis is the following: ―The depressive effects of SAD and

hence risk aversion may be asymmetric about the winter solstice (in the Northern

Hemisphere about mid-December, with the Southern Hemisphere being in opposite

synchronicity). Thus two dates symmetric about the winter solstice have the same length

of night but possibly different expected returns. We anticipate seeing unusually low

returns before winter solstice and abnormally high returns following winter solstice.

Lower returns should commence with autumn … followed by abnormally high returns

when days begin to lengthen ...‖ Kamstra et al. (2002, p. 4). Note, that the hypothesis

relates to the length of daylight hours or the length of the day, not to changes in the

length of day or changes in daylight hours. Therefore, the alternative hypothesis is that

short days lead to lower returns in the fall and relatively higher returns in the winter.

5. This is a descriptive/psychology driven theory with prescriptive implications vs.

normative theory.141

Therefore, the links we care about (i.e., from a finance and financial markets perspective) are

from daylight hours to depression to risk-taking to security returns. The seemingly simple and

direct testable hypothesis that SAD affects security returns is as follows: as daylight hours

decrease/increase security returns decrease/increase. In addition, because of the increasing

141 A conditional asset pricing model/version of the newer EMT/EMH allowing for ―time varying risk premia‖would probably capture this effect and suggest it is not even an ―anomaly‖. Do you think SAD is an efficient markettype risk? Therefore, ironically, it is possible SAD can affect and/or, at least partially, cause time-varying risk premia, which is a key comeback line for modern EMT. Therefore, it is possible that at least part of time-varyingrisk premia is associated and/or caused by psychological phenomena, which are assumed to be irrelevent.




variation in daylight hours each day over a normal year as we approach the poles, it is clear we

would expect to see this effect more pronounced the further from the equator we travel.

Therefore, we would expect security prices to be more affected based not just on geography and

the resulting annual variation in daylight hours, but possibly nonlinearly by relative proximity to

the poles. Add to this the complicating simple empirical observation that people (i.e., in general)

are not affected in a linear fashion as we move further away from the poles, and it is not at all

clear that SAD will have much of an effect on financial market prices at all. For example, it has

been documented that about 1.5% of the people in south Florida vs. about 9% in the northern

U.S. are impacted. Thus, per unit of latitude from the equator is not linear with respect to the

incidence, or probably even degree, of SAD observed. In addition, there is nonlinearity with

respect to the temporal impact of the depressive symptoms themselves, as evidenced by the

following graph of percentage of clinically SAD U.S. patients displaying certain symptoms over

calendar year (i.e., in the Northern Hemisphere).

Source: Taken from Modell et al. (2005, p. 663, part of Figure 4 – Pattern of reported historical seasonal changes bymonth (data double-plotted to show cyclicality; n = 1042)).




As the reader will see shortly, this actual clinical pattern of the disorder only roughly matches the

actual number of estimated daylight hours (which is much more of a muted sign wave than the

above). In short, the additional complicating factors of a nonlinear relationship between

incidence and latitude combined with the actual impact itself not matching the number of

daylight hours compounds the potential complexity of the effect (and hasn‘t been taken into

account in finance related studies thus far). That noted, it should be clear from the above graph

that SAD will tend not to have much of an impact until September and October (i.e., in the

Northern Hemisphere) and quickly recede during March and April (therefore, peaking around

November/early December, but beginning September/October). Thus, and ignoring any extra

anticipatory effect, the refined hypothesis is that security returns are actually expected to be

impacted about two months before the actual minimum number of daylight hours (possibly due

to anticipatory effects of SAD as illustrated by the aforementioned graph142).

Based on day and latitude, an estimate for the number of daylight hours can be made as follows

(see Forsythe et al. (1995)):

where D = day length (in hours), L = latitude (in degrees), and J = day of the year. There are a

number of cities with stock exchanges, but a limited number far enough from the equator to

provide useful tests of the SAD hypothesis. Here are a few important cities and their associated

latitudes.

142 Given its timing, the graph makes me wonder if this could at least be a contributing cause of the ―January effect‖.




Not only is the bulk of the world‘s financial capital is in the Northern Hemisphere, but there are

more extreme latitude stock exchange cities in the north than in the south. Therefore, we are

somewhat limited in our ability to test in the Southern Hemisphere. For example, the furthest

south we have is 34 degrees, while the furthest north is slightly more than 60. Thus, assuming a

nonlinear effect of SAD given latitude, any tests are really testing for the potential effect in the

Northern Hemisphere (because empirically there wouldn‘t appear to be enough measurable stock

markets being impacted in the Southern Hemisphere).

What follows is a graph of approximate daylight hours over a normal year for Stockholm,

Sweden (a Northern Hemisphere stock exchange city).

Some cities with stock exchanges

Latitude

Helsinki, Finland 60 10 NStockholm, Sweden 59 17 N

London, England 51 32 N

New York, N.Y. 40 47 N

Tokyo, Japan 35 40 N

Sydney, Australia 34 0 S

Johannesburg, South Africa 26 12 S




For Stockholm, the average is around 12.4 hours per day, they peak at about 18.6 during the last

part of June (at the summer solstice) and shrink about six months later to a minimum of about

6.1 hours (i.e., during the last part of December during the winter solstice). This would

essentially be the reverse for someone living in the Southern Hemisphere at the same latitude.

Next, here are the estimated daylight hours for Sydney, Australia (a southern hemisphere stock

exchange city).

0

2

4

6

8

10

12

14

16

18

20

22

24

1 - J a n

1 5 - J a n

2 9 - J a n

1 2 - F e b

2 6 - F e b

1 1 - M a r

2 5 - M a r

8 - A p r

2 2 - A p r

6 - M a y

2 0 - M a y

3 - J u n

1 7 - J u n

1 - J u l

1 5 - J u l

2 9 - J u l

1 2 - A u g

2 6 - A u g

9 - S e p

2 3 - S e p

7 - O c t

2 1 - O c t

4 - N o v

1 8 - N o v

2 - D e c

1 6 - D e c

3 0 - D e c

D a y l i g h t H o u r s E s t i m a t e

Day Length Estimate for Stockholm, Sweden




As you can see, Sydney isn‘t nearly as south as Stockholm is north (about 34.0 degrees below

the equator vs. about 59.3 degrees above, respectively). For Sydney, the average is around 12.1

hours per day; they peak at about 14.4 during the last part of December (at the summer solstice

for the Southern Hemisphere) and shrink about six months later to a minimum of about 9.8

hours.

Combining the lines gives a visual clue as to the north-south daylight hour differential and its

timing.

0

2

4

6

8

10

12

14

16

18

20

22

24

1 - J a n

1 5 - J a n

2 9 - J a n

1 2 - F e b

2 6 - F e b

1 1 - M a r

2 5 - M a r

8 - A p r

2 2 - A p r

6 - M a y

2 0 - M a y

3 - J u n

1 7 - J u n

1 - J u l

1 5 - J u l

2 9 - J u l

1 2 - A u g

2 6 - A u g

9 - S e p

2 3 - S e p

7 - O c t

2 1 - O c t

4 - N o v

1 8 - N o v

2 - D e c

1 6 - D e c

3 0 - D e c


Day Length Estimate for Sydney, Australia




Therefore, as well as the effect expected to be impact at about opposite times between the two

cities (remember to factor in what I am calling the anticipatory effect of about two months), in

addition, the effect should be much more pronounced in Stockholm than Sydney (if much at all

in Sydney). Also, given the sinusoidal pattern (and accounting for the descriptive impact of the

timing of the actual associated symptoms), SAD should mostly impact during mid-September

through mid-December in the northern latitudes (and especially the far north), and conversely

mid-April through mid-July in the southern latitudes (and especially the far south).

As far as the actual, unadjusted returns look across different stocks markets that are located

physically at different latitudes, it might be helpful to visually inspect some preliminary

evidence. Going from most south to most north here are five graphs (the first two are from the

Southern Hemisphere, then the next three are from the Northern Hemisphere) showing median

0

2

4

6

8

10

12

14

16

18

20

22

24


Stockholm, Sweden and Sydney, Australia City Day Length Estimates

Stockholm Daylight Hours Estimate

Sydney Daylight Hours Estimate




and mean rolling returns with a SAD perspective (i.e., they are rolling six month returns across

the set of calendar years each set of data for each exchange covers).

Sydney, Australia (latitude 34.0 south – All Ordinaries Index – daily data covering the

period January 1958 through October 2004))

-2.00%

-1.00%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

9.00%

C o m p o u n d e d d a i l y r e t u r n

AUS (rolling price returns) - 1/1958 through 10/2004

AUS_avg

AUS_med




Johannesburg, South Africa (latitude 26.2 south – FTSE/JSE All Share Index – daily data

covering the period May 1986 through October 2004))

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%9.00%

10.00%

11.00%

12.00%

13.00%

14.00%

15.00%

16.00%

17.00%

18.00%

19.00%

20.00%

21.00%


ZAR (rolling price return) - 5/1986 through 10/2004

ZAR_avg

ZAR_med




New York City, U.S. (latitude 40.8 north – S&P 500 Index – daily data covering the period

January 1985 through August 2004))

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

9.00%

C o m p u n

d e d d a i l y r e t u r n

U.S. rolling price return - (1/1985 through 8/2004)

US_avg

US_med




Stockholm, Sweden (latitude 59.3 north – Affarsvarlden General Index – daily data

covering the period July 1980 through October 2004))

-2.00%

-1.00%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

9.00%

10.00%

11.00%

12.00%

13.00%

14.00%

15.00%

16.00%

17.00%

18.00%

19.00%

C o m p o u n d e

d

d a i l y r e t u r n

SWE (rolling price return) - 7/1980 through 10/2004

SWE_avg

SWE_med




Helsinki, Finland (latitude 60.2 north – HEX Index – daily data covering the period

January 1987 through September 2004))

A few things to note (all somewhat expected):

1. There are noticeable seasonal patterns in all of them.

2. The seasonal patterns are roughly offset in the two Southern Hemisphere based equity

markets vs. the three Northern Hemisphere based equity.

3. Especially in the north, the further away from the equator (or, conversely the closer to the

pole), the more pronounced the pattern.

4. Especially at the extreme, the unadjusted magnitude seems large.

For me, if there is something somewhat surprising it is the magnitude.

-4.00%

-3.00%

-2.00%

-1.00%

0.00%

1.00%

2.00%

3.00%

4.00%5.00%

6.00%

7.00%

8.00%

9.00%

10.00%

11.00%

12.00%

13.00%

14.00%

15.00%

16.00%

17.00%

18.00%

19.00%

20.00%21.00%

22.00%


FIN rolling price return - (1/1987-9/2003)

FIN_avg

FIN_med




As you might have guessed, the SAD effect has been tested (Kamstra et al. (2002, p. 1)) and the

―results strongly support a SAD effect in the seasonal cycle of stock returns that is both

significant and substantial, even after controlling for well-known market seasonal and other

experimental factors143. … higher latitude markets show more pronounced SAD effects and

results in the Southern Hemisphere are six months out of phase, as are the seasons.‖ Thus, at

least one suspected psychological reason for a type of annual seasonality.

Controlling for ‗risk‘, Kamstra et al. (2002) find the following apparent ‗free lunches‘:

143 The Kamstra et al (2002) regression used to control for risk was: , where two lagged returns and , were used to control for residual

autocorrelation (i.e., two business days‘ worth), a Monday dummy variable to control for the ―turn-of-the-week effect‖ (equal to one on the trading day after the week-end), a tax dummy variable to control for tax loss sellingeffects (equal to one on the last trading day of the year and first five trading days of the new year), daylight hours forSAD, a dummy variable to control for the Fall at that latitude, percentage cloud cover, millimeters of precipitation,and temperature in degrees Celsius (all based on daily returns data). The key results are the SAD coefficient and falldummy, which strongly support the hypothesis.




Source: Kamstra et al. (2002, p. 27)

Kamstra et al. (2002, p. 27) - Table 3

Average Annual Percentage Return

Due to SAD and Due to Fall Dummy

Country Annual Return Annual Return Unconditional

(Latitude) Due to SAD Due to Fall Dummy Annual Return

US: S&P 500 9.2*** -5.1** 6.3***

(41° N)

US: NYSE 6.1* -3.5* 9.2***

41° N

US: NASDAQ 17.5*** -11*** 12.5***

41° N

US: AMEX 8.5*** -7.3*** 8.4***

41° N

Sweden 13.5*** -9.7** 17.1***

59° N

Britain 10.2** -3.1 9.6***

51° N

Germany 8.2* -6.1** 6.5**

50° N

Canada 13.2*** -6.0** 6.1***

43° N

New Zealand 10.9** -13*** 3.3

37° S

Japan 7.0* -5.3** 9.7***

36° N

Australia 5.7 0.7 8.8***

34° S

South Africa 17.4* -3.0 14.6***

26° S

Levels of s tatistical si gnificance: *** 1%, ** 5%, and * 10%.




According to Kamstra et al. (2002, p. 15), as an example, after controlling for144

residual

autocorrelation, the ‗weekend effect‘, tax loss selling, cloud cover, precipitation, temperature,

Fall, and SAD related nighttime hours, we could expect to return about 21.1% per year excess

returns by going long Sweden in the Northern Hemisphere‘s Fall and Winter, and then doing the

same in Australia during its Fall and Winter (i.e., twice a year we would need to reallocate 100%

of our portfolio). Obviously, these excess returns (an apparent ‗free lunch‘) could be increased

by shorting the other market at the same time.

In addition, and as mentioned, it bears repeating that the bulk of the world‘s capital is in the

Northern Hemisphere, where the SAD effect is most pronounced. Beyond that, it seems to effect

both large-cap and small-cap stocks, even American Depository Receipts (―ADRs‖), suggesting

that even large Southern Hemisphere firms or large Northern Hemisphere firms located at a

different latitude than New York City experience this effect regardless of the possibility of an

apparent arbitrage.145 Thus, as you may have noticed by now, what looks like an available ‗free

lunch‘ may not exist; and there must be some form or forms of limits to arbitrage across the

various stocks that are cross-listed on more than one stock exchange and located at more than

one latitude. In effect it may turn out the location and/or latitude is a form of limits to

arbitrage.146

144 As always one assumes in normative finance that we correctly control for risk in such a way that the apparentexcess returns or ‗free lunch‘ is in fact what we think it is. This is also a convenient way of ignoring results we don‘twant to admit as evidence (i.e., just say the ‗model‘ was ―wrong‖; and it most likely is wrong anyway, but usuallynot for the normative reason or reasons given).145 ADRs should mean no limits to arbitrage (or close to it), but still there seem to be, which makes the ADRs thingeven more bizarre. Many ‗anomalies‘ impact small-caps, but here we have a case of large-caps also being impacted.146 Then again, maybe not; that is, ADRs may not be able to limit limits to arbitrage because of something, e.g., VanNieuwerburgh and Veldkamp (2009, p. 1202) point out. Specifically, the evidence seems to support the notion thatthere are strong enough information asymmetries between, for example, earnings analysts country to country, that




And most importantly for our purposes, with SAD we have a relatively clean link from pricing in

the equity markets to psychology (the second ‗pillar‘ of behavioral finance), that, incidentally, is

very nearly perfectly predictable. So with SAD it appears we have both ‗pillars‘ of behavioral

finance: (1) apparent limits to arbitrage, and (2) psychology.

I will end the chapter with prescriptive advice with respect to SAD and the financial markets:

• Don‘t change your level of risk aversion according to the number of daylight hours. • If you are thinking about buying stocks in the fall, try to wait until late fall or early winter

(especially the further away from the equator you are).

• In general, try your best not to be sad or contract SAD (you know what I mean).

the relative precision is enough to effectively insulate most markets from outsiders using instruments like ADRs toeliminate pricing inefficiencies, like those likely caused by SAD. Therefore, ADRs may help, but one or moregeographic advantage may affect pricing in such a way as to make outsiders reluctant to move against the pricingsignals given more locally. The specific study showing this advantage is Bae et al. (2008).




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Chapter 6: What do we know about the individuals, agents and institutions

who push financial market prices around (or: Who buys and sells this stuff

anyways?)?

Although limited in empirical detail, now we will look at the financial market agents and what

they know, think, and do that might be important with respect to pricing in the financial markets.

That is, we will look at what is considered ―smart‖ and not so smart money in the financial

markets.147 Depending on various things, each has an impact on pricing in the actual markets. Of

course, the notion that the characteristics and/or organizational setting of market participants

might have an influence on pricing in the financial markets would seem to contradict traditional

normative finance. Under traditional EMH/EMT finance the participants are incidental to the

story of price setting in any financial market (i.e., ultimately, not just the people, but additionally

market microstructure itself was not considered to be very important).

It turns out that who, what, and when are important.148

Nevertheless, let‘s focus mostly on the

who part of that trinity this chapter. For example, Fisher and Statman (2000) find that WSSs‘

sentiment and individual investors‘ sentiment is a negative market timing signal that is

147

Clearly, even though I generalize between ‗smart money‘ (i.e., institutional investors) and not so smart money(i.e., individuals), not all individuals display the biases and somewhat self-destructive financial behavior shown bythe average individual investor. For example, Zheng (1999) shows that some mutual fund investors seem tosystematically enjoy a certain level of short-run market timing, especially with respect to small-cap mutual funds.Therefore, I mean on average, not that all institutional investors display more financial acumen than individualinvestors. Also, see Lewellen et al. (1979) as the original article pointing out the actual heterogeneous nature of individual investors.148 See, for example, Madhavan (2002). Market microstructure not only matters, but it tends to be more complexthan commonly thought (i.e., as assumed by standard finance). Thus, who, what, and when can be critical to pricedevelopment, depending on those details in a particular market at a particular time.




statistically significant (i.e., it can be profitably used for Tactical Asset Allocation – ―TAA‖), but

they are largely unrelated. In addition, newsletter writers and individual investors have sentiment

that tends to covary (although, not perfectly) while the same is not true of WSSs.149

Therefore, at

least for forecasting purposes, some groups‘ opinions seem to matter.

In the actual markets, effort is generally not rewarded per say. Especially with respect to poor

investment process (―IP‖), whether the answer takes five minutes of moderate thought or five

years of extensive research to arrive at may be of no relevance to the outcome (financial

management may be unique in this).150 Of course, as a general rule, learning and focused effort

shouldn‘t hurt the process or outcome, but, again, it may not change the outcome.

With respect to the agents/actors themselves, there are essentially two sets (and possible subsets

for each) individual and institutional (i.e., those that get paid for investing for others –

professional money managers, brokerage related professionals, etc.) investors, which can be

broken down in subsets:

149 Fisher and Statman (2000) is based on:(1) The WSS sentiment indicator was measured monthly by Merrill Lynch‘s average recommended stock allocationof between about 15 to 20 WSSs over the period September 1987 through July 1998 (ML‘s ―QuantitativeViewpoint‖). (2) The newsletter writers sentiment was derived from Chartcraft‘s Investor Intelligence newsletter (a survey of over 130 investment newsletter writers where sentiment is classified into the following three categories: ―bullish‖,

―bearish‖, or ―waiting for a correction‖), and the signal is marketed by them as a contrary indicator (they haveweekly data since 1964, but only overlapping data was used). The study used ―bullish‖ newsletter writer percentagein the last week of the month.(3) The individual investor sentiment indicator was derived from the American Association of Individual Investors‘(―AAII‖) mail survey of about 100 survey questionnaires to members each weekday and collects a little over 200each week (the questions asks the investor to categorize themselves as ―bullish‖, ―bearish‖, or ―neutral‖). The AAIIperforms a weekly tabulation on Thursday of each week (again, the average number of responses is over 200), andhas been doing this since July 1987. The study used the percentage of respondents that were ―bullish‖ the last weekof the month as the sentiment signal.150 I credit Joe Eagleeye and Hilary Till with this insight.




1. Institutional sell- and buy-side (―sell-side‖ include brokers vs. ―buy-side‖ are the money

managers). For our purposes, the ―buy-side‖ are the key institutional actors (given that

they have the greatest potential for moving markets), and they can be further broken

down into analysts and PMs. Of course, it is distinctly possible, for example, that sell side

can impact buy-side and vice versa.

2. Institutional money managers can be broken down into retail and institutional orientated

money managers (one catering to pension funds and the like, the other to individual/retail

investors).

3. Individual investors (they can be further broken down by income, e.g., ―high-net-worth‖,

etc.).

Note, there is overlap between the sets, but it isn‘t as bad as it all seems at first. Furthermore,

most jobs are on the retail side, but the largest potential for moving the markets with the fewest

bodies is on the institutional side. At least for the U.S., the rough asset numbers look something

like this:




Source: Goldstein, M., ―The Future of the Money Management Industry‖, Empirical Research Partners LLC, NewYork, New York, 2008, p. 3.

The U.S. Money Management Industry: Assets ($ billions)Professionally Managed Assets Assets (2007) Percentage

Legally Defined Retirement Assets:

Defined-Benefit Plans ($5,505 total):

Corporate $1,967 3.5%

State and Local $3,152 5.6%

Union $386 0.7%

401(k) Plans $3,047 5.4%

Union DC Plans $112 0.2%

Other Corporate Defined-Contribution Plans $401 0.7%

403(b) Plans $739 1.3%

457 Plans $173 0.3%

Public DC Plans $353 0.6%

IRA Accounts $4,747 8.4%

Legally Defined Retirement Total $15,077 26.7%

Retail Assets:

Retail Mutual Funds $3,741 6.6%

Exchange Traded Funds ("ETFs") $187 0.3%

Variable Annuities $1,028 1.8%

Separate Accounts $2,329 4.1%

Bank Personal Trusts $1,036 1.8%

Hedge Funds Held by Individuals $842 1.5%

Private Equity $585 1.0%

Retail Total $9,748 17.3%

Other Categories:

Endowments $411 0.7%

Foundations $670 1.2%

Insurance Company Outsourcing $800 1.4%

College Savings Plan $130 0.2%

Other Categories Total $2,011 3.6%

Professionally Managed Assets Grand Total $26,836 47.6%

All Financial Assets $56,422 100.0%

Implied Non-Professionally Managed Assets Grand Total $29,586 52.4%




As of 2007, and focusing on the last three numbers, of the approximately $56 trillion of financial

assets reportedly held by Americans, just slightly less than half was ―professionally managed‖151,

the remaining was mostly managed by individuals. Regardless of how it is broken up, it is likely

that both institutions and individuals have the wherewithal to ―move markets‖, whether they

likely move them systematically toward or away from efficiency is the key question.

Is there any reason to think that either one or more sets of institutional investors or individual

investors systematically bias their investment decision making (i.e., their IP) in such a way that

market prices are driven away from efficient pricing? Financial institutions are essentially in the

data management business, and information and/or data is the single most important input into an

optimal IP. Furthermore, most good IP are essentially well organized ―data management

exercises‖.152 ―Information is the vital input into any active management strategy. Information

separates active management from passive management. Information, properly applied, allows

active managers to outperform their informationless benchmarks.‖153Information analysis can

be presented as a two step process:

1. Turn information/predictions into portfolios.

2. Analyze and evaluate the performance of those portfolios.

Short of well structured organizations that consistently act as rational arbitrageurs, and given the

list of psychologically driven decision making biases listed in the previous chapter, it is hard to

151 The largest concentration of assets in the fewest hands is probably on the institutional pension side.152 This quote is attributed to Joe Eagleeye (late 1990s).153

See Grinold and Kahn (1992, p. 14).




imagine that some biases don‘t manifest themselves in the respective IPs of the various agents

and organizations found in the financial markets.

THE ‗SMART MONEY‘ – THE ‗ANALYSTS‘ AND PORTFOLIO MANAGERS (―PMS‖)

Could it be that humans have some impact on pricing in the financial markets? Let‘s rephrase

that, is it possible that humans don‘t have a significant impact on pricing in the financial

markets? Answer: ‗Not bloody likely.‘ Obviously, if there are limits to arbitrage the human part

has the potential to be very important, if not dominant. Again, there being limits to arbitrage is a

necessary condition but not a sufficient condition for the psychology part to matter. Regardless,

as long as there are significant limits to arbitrage, the structure of the markets is likely to be an

important determinant in influencing pricing in the financial markets.

Furthermore, regarding the various agents and organizations that have the capital that would

allow them to have a significant impact on pricing, is it possible that not all agents are created

equal? Of course, even the EMH/EMT supporters would argue that it is effectively only the true




arbitrageurs that constantly correct mispricing that ultimately matter. For our purposes, and

assuming the arbitrageurs don‘t always bail us out, isn‘t it likely that the humans that make the

buy and sell decisions and/or have an influence on them and things like the structure of their

organizations (e.g., compensation, applicable regulations, etc.) can have a significant impact on

pricing in the actual markets? I ask that question, not just because it is likely to be self-evidently

true, but also because the structure of the markets could be structured to primarily reflect

mechanistic considerations (e.g., the evolution of the speed and capability of computers), but is

more likely to be primarily influenced and shaped by the humans (and, if there is a filtering

mechanism, possibly the types of humans), that occupy the critical nodes of decision making in

the financial markets.

Probably most importantly, who are those arbitrageurs anyways? Are they not likely to be among

those institutional and/or individuals with the most capital? Based on the money management

table, clearly the institutional or retail oriented money managers, as well as individual investors,

have plenty of capital to ―move the markets‖ (i.e., assuming little or no ‗cancellation‘).

Therefore, if it turns out all relevant groups can be shown to think or behave in ways that are an

anathema to rational economic arbitrage, then what are odds of them correctly pricing in a

timely, let alone sufficient, manner? Answer: ‗Not bloody likely.‘




THE ‗SMART MONEY‘ – THE ANALYSTS

―Yet, we are becoming increasingly aware that much of this work may be a waste of time and

that surely too much of it is duplicative. In fact, we either have to restructure the analyst‘s tasks

or dispose of analysts altogether.‖

Bernstein (1998, p. 4)154

According to Bernstein security analysts should not be doing the following two things that they

almost exclusively spend their time doing (see Bernstein (1998)):

1. Presenting known facts as new information, especially consensus thoughts.

2. Looking for ‗undervalued‘ stocks (i.e., they should be looking for overvalued stocks

instead).

It seems that some people believe that most analysts spend their time effectively rationalizing

decisions that have already effectively been made. As previously mentioned in the psychology

part, humans tend to spend a great deal of resources justifying decisions as opposed to improving

them, and it is likely many financial market buy- and sell-side analysts are in fact doing just that.

A survey was sent out to a supposed representative sample of AIMR members155

, and the

following were some of the findings (Block (1999)):

1. 84.8% of respondents ―sometimes‖ or ―never‖ used PV techniques (45.7% ―never‖).

154 The Bernstein (1998) piece is an opinion piece and it is kind of silly, but makes two good points about whatanalysts (anyone for that matter) should not be doing in the IP. Otherwise, he confuses stock pickers fromquantitative analysts, risk management with stock selection, etc.155 The survey was sent to Association for Investment Management and Research (―AIMR‖) members (297 out of 880 mailings of about 32,000 AIMR members at the time. This implies about a 1/3rd response rate, and about 2/3rd CFAs and about 54% MBAs, but no PhDs. AIMR is responsible for Chartered Financial Analysts (―CFAs‖)credentialing, an ethical code of conduct, etc. There have essentially tried to set up the finance equivalent of Certified Public Accountants (―CPAs‖) with the associated rules, ethical considerations, and organization.




2. When broken-down by finance industry type, 100% of the investment bankers (18

respondents) ―sometimes‖ or ―never‖ used PV techniques (83.3% ―never‖).

3. Among four ―inputs of importance‖ for valuing stocks, earnings (1st) and cash flow (2

nd)

were ranked above book value (3rd) and dividends (4th) by large margins.

If people matter in the financial markets, and given that these people would be expected to be

some of the most quantitative of the ―profession‖, the results should be especially disturbing to

EMH/EMT proponents. Again, remember finance is all about discounted cash flows (i.e., PV

analysis). A clear majority (and 100% of the investment bankers

156

) ―sometimes‖ or ―never‖used PV techniques. How could that be? Who is able to price, let alone set pricing, in the

financial markets without using PV analysis? Therefore, items #1 and #2 are especially

disturbing from a traditional finance perspective. Regarding the ―inputs of importance‖ (#3),

except for cash flow (which should be ranked 1st not 2nd), the other three should be reversed. The

numbers behind the rankings for the ―inputs of importance‖ are:

Source: Block, S., ―A Study of Financial Analysts: Practice and Theory‖, Financial Analysts Journal, Volume 55,Number 4, July/August 1999, p. 89.

156 Investment bankers in London and Frankfurt have been shown to display high levels of hindsight bias; and thatbias appears to impact their compensation, especially for those few that are the least biased (see Biais and Weber(2009)).

Table 6. Rank of Inputs in ImportanceVariable First Second Third Fourth Avg. Ranking

Earnings 156 118 23 0 1.55

Cash flow 133 140 19 5 1.65

Book value 5 32 133 127 3.29

Dividends 3 7 122 165 3.51




For example, remember, for stocks, dividends and cash flow are largely one in the same, so why

such divergent rankings for the two?

4. Trading ranges seem to drive sales and purchases.


#4 is amazing (i.e., in a normatively bad way), especially given that those answering the

questionnaire tend to be ‗long-term investors‘, and they directly contradict themselves in Table

12. Therefore, from #3 they overwhelmingly think earnings are a critical input yet actual

decisions to ―buy, sell, or hold‖ should be made on basic weak-form market efficiency deviations

(i.e., ―current vs. historical trading range‖) over EPS (and by a wide margin). What is going on

here?

5. Given their other beliefs, responses and opinions, the respondents seem to possess some

bizarre beliefs about portfolio management.

Table 8. Rank of Variables in Determing Buy, Hold, and Sell DecisionsVariable First Second Third Avg. Ranking

Current versus historical trading range 216 67 14 1.32

Long-term outlook for the company 76 171 50 1.91

Next quarter's EPS 5 59 233 2.77





Market timing doesn‘t enhance portfolio return? Who are these people? Furthermore, they expect

a reversion to the mean for yields and P/E (which in 1999 meant prices should likely be expected

to dramatically go down), yet they rely on ―current vs. historical trading range‖ to establish buy,

sell, and hold decisions? I repeat, who are these people?

6. And about market efficiency (note the ―strongly disagree‖ and ―neutral‖ percentages).


Only about 3% agree with their professors, while more than twenty times more strongly disagree.

7. And about the importance of trading, risk, and skill in determining portfolio return

Table 10. Beliefs about Portfolio Management Among those

Belief Number Percent with Opinions

A. Does market timing enhance portoflio return?

Yes 85 28.6 32.7No 175 58.9 67.3

No opinion 37 12.5

Total 297 100 100

C. Will there be a reversion to the mean in the next decade for yields and P/Es?

Yes 171 57.6 71.6

No 68 22.9 28.4

No opinion 58 19.5

Total 297 100 100

Table 11. Opinion of the Efficient Market Hypothesis

Opinion Number Percent

Strongly agree 8 2.7

Neutral 101 34.2

Strongly disagree 186 63.1

Total 295 100





I believe it was Winston Churchill that mentioned something about the triumph of hope over

reality. Is it not at least somewhat contradictory to believe (as shown in Table 8) that ―trading

ranges‖ should determine whether you buy or sell a security, yet also believe that ―the amount of

trading in the portfolio‖ is irrelevant (less than 1% thought it important in ―determining portfolio

return‖)?157

Although many of the opinions are contradictory, and often run counter to basic finance

thought158, they do mesh reasonably well with results by two anthropologists (O‘Barr and Conley

(1992)). In essence, they found that following two things drove investment decision making in

the large institutional money managers they interviewed:

1. The ―most important finding is the extent to which economic and financial analyses do

not dominate investment decision making. Instead, in choosing investment strategies,

evaluating investment options and hiring, firing and retaining external managers, fund

157 Again, especially note the 0.6% who think trading is the most important impact (clearly a problem, especiallygiven most PM‘s think of themselves as ‗traders‘ and trading costs do matter).158 It is important to remember, assuming this is an informed crowd and crowds set the pricing in the markets (i.e.,not just one marginal buyer/seller), there is a real potential for sustained inefficiency.

Table 12. Most Important Variable in Determing Portfolio Return

Variable Number PercentThe skill and training of the portfolio manager 179 60.3

The amount of risk in the portfolio 116 39.1

The amount of trading in the portfolio 2 0.6

Total 297 100




executives appear to be motivated more by the kinds of cultural influences that drive less

consequential decisions.

2. These include the quirks of institutional history and corporate politics, the desire to

displace responsibility, and the demands of maintaining smooth personal relationships.‖

In short, if that is true, then it is potentially an EMH/EMT proponent‘s nightmare. Alright, most

analysts and all investment bankers seem not to be concerned about PV analysis, are not

interested much in cash flows, and appear to laugh at market efficiency, but what about PMs;

PMs can‘t do such things and keep their jobs can they?

THE ‗SMART‘ ANLAYSTS – THE EARNINGS ANALYSTS

Although before proceeding to PMs, in the actual world of finance I would like to point out that

there are analysts and there are analysts. Security analysts that work for buy side firms (e.g.,

mutual fund companies and hedge funds) represent one type of financial analyst. The more

qualitative types of buy side analysts will typically pass judgment on the credit quality of an

issuer, recommend issuers to buy or sell, etc., while the more quantitative types will value or




price more obscure securities and related derivatives. In addition, there are corporate analysts

working in finance or treasury departments of financial or nonfinancial firms or partnerships who

run the gamut. The aforementioned survey likely encompasses all of them.

But as on the Orwell‘s animal farm, in the eyes of normative finance, ―all analysts are equal, but

some are more equal than others.‖ It could be argued that the most famous analysts, and likely

highest paid on average, are the earnings analysts working at sell side firms (i.e., traditional

―Wall Street‖ type firms). These are the people whose job it is to recommend firms and make

estimates/forecasts of their earnings. Given that they have documented impacts on pricing in the

financial markets, one obvious question is: How are they at their job? Answer: Generally not

very good, or at least not as well as a normative economist would expect.

Specifically with respect to earnings forecasts and recommendations, what seems to be the

problem or what is it that they seem to be doing? Even more specifically, how and why do

earnings analysts make the kinds of biased recommendations and forecasts that they do? Keep in

mind: ―Investors pay attention to the pronouncements of executives and analysts, so these

pronouncements affect stock prices. But do the actions of investors lead prices to correctly

reflect fundamental values?‖ Shefrin (2007, p. 257) It isn‘t just earnings analysts that are prone

to biases, but these types of analysts have special power over pricing in the market that most

analysts do not.




One problem is sell side analysts have a conflict between buy side clients (broker dealer clients)

and the firms they are recommending (i.e., they are reluctant to write or say bad things about

firms they cover, even if that ostensibly is the job). Thus, the conflict is ultimately between retail

and institutional stockbrokers and buy-side institutions. Given the investment banking

relationship with the firms they cover, the analysts are encouraged to become more optimistic.

Barber et al. (2004) show that, particularly in the case of buy recommendations during the

NASDAQ/technology bubble, investment bank analysts underperformed independent analysts by

about 22 percent annualized. There is an explicit tie between analyst optimism and investment

banking business. For example, in the U.S. 25 days after an IPO is the ―quiet period‖ when an

underwriter/investment bank cannot issue a forecast or opinion concerning revenues, income, or

earnings per share during the period. Michaely and Womack (1999) analyzed analysts‘ IPO

recommendations and compared underwriters‘ ‗buy‘ recommendations of those nonaffiliated

‗buy‘ recommendations for IPOs in their first year of trading and found:

As expected, firms that are covered but not brought to the market by the firm are more

accurately analyzed (i.e., still biased, just not as much).

The first month after the ‗quiet period‘, 50% more ‗buys‘ are issued by affiliated firms

than from unaffiliated firms (and the ‗buy‘ is issued sooner than normal), and poorly

performing IPOs tend to be propped up.

Investors‘ reaction to a buy recommendation depends on who issued the buy (+2.8% for

affiliated and +4.4% for nonaffiliated). Clearly, investors do some discounting of the

bias, but they do not fully discount the bias.




Poorly performing IPOs did not have a non-underwriter analyst recommendation (i.e.,

during the period 1990 – 1991 that was studied). That is, unaffilated analysts just choose

not to make a recommendation rather than make a negative one.

Finally, not only is it important to note that investors are not fully incorporating known analyst

biases, but it is important to note that analysts are biased for economic (i.e., their paychecks and

bonuses) and behavioral reasons (i.e., they are human and prone to it without training).

Darrough and Russell (2002, p. 132) actually checked to see numerically what analysts do. They

modeled analysts forecasting as a two-stage heuristic for long-range forecasts. In order, the two

stages are:

1. Forecasts – Forecast average company‘s earnings will grow by about 34% more than

current reported earnings of the company (for S&P 500 firms).

2. Revisions – Downward revision is about 1% per month, and forecasts are then adjusted

by about 90 cents per 100 cents as new earnings reports come out (e.g., if earnings go up

100 cents, then they adjust by about 90% toward that change – underreaction).

Therefore, analysts are anchored on a relatively impossibly optimistic growth rate overall, and

they seem to adjust almost exclusively on reported earnings. Overall, their job is forecasting, but

they do it in a nonoptimal way.

In my opinion the most plausible explanation for their behavior is that these types of analysts are

much like WSSs. In short, they are paid to lie and bias their results. Otherwise, ―well calibrated‖

analysts would dominate, but empirically they do not. In addition, they are clearly biased just




like any other human, but there seems no significant selection mechanism (filter) at the corporate

level to emphasize forecasting calibration over lying. Given that they work in the very firms that

WSSs work in, might it be possible that they are exposed to the same filtering and expectations

that WSSs are exposed to? Clearly, the corporate value function emphasizes lying over earnings

forecasting calibration, yet the market must provide some feedback for this (i.e., customers, on

some level, both institutional and individual must demand the lying over calibration)159

. Clearly,

as long as there is a positive relationship between optimistic lying and say IPO business, analysts

will tend to be relatively poor forecasters.

I will finish this sub-part with some prescriptive advice for those who are or are interested in

becoming earnings analysts:

1. Don‘t apply the previous two-stage heuristic for long-range forecasts.

2. Don‘t forecast average company‘s earnings will grow by some overly optimistic fixed

number per year (e.g., try an AR process as a better first guess, and then improve it from

there).

3. Don‘t underreact and don‘t overreact to information (i.e., when in doubt, use logic and

statistics to guide you). For example, use Bayes‘ rule when you can.

159 Companies try to ―guide‖ earnings analysts. Knowing that analysts tend to underreact to new information, it is

possible to induce pessimism in analysts‘ earnings forecasts. Companies may try to ‗guide‘ analysts lower (e.g.,Microsoft & Intel) so they can ‗beat‘ expectations. Degeorge et al. (1999) find several thresholds in earningsmanipulation. They note that there are three benchmarks/reference points or earnings thresholds:

1. ―‘Red ink‘, meaning zero earnings, 2. the previous period‘s earnings, and 3. analysts‘ consensus earnings forecasts.‖

First, try to surpass all three then scale back to two, then to one. Thus, evaluate outcomes relative to abenchmark/reference point. There is very strong evidence on this (also general/anecdotal evidence on CEOs, etc.making easy targets to beat and inducing pessimism). The key is setting a benchmark or reference point that is likelyto be exceeded.




THE ‗SMART MONEY‘ – THE PMS

This group is important for the following reasons:

1. They (combined with individual investors) directly influence pricing in the market (i.e.,

they have a market impact, sometimes even individually).

2. Of those two general sets of actors in the financial markets, they are considered the truely

‗smart money‘. Therefore, if they fail to enforce ‗efficiency‘, what are the odds that the

much maligned individual investors are able to enforce it?

3. Given numbers 1 & 2, and to the extent they do not enforce informational efficiency

and/or generate exploitable inefficiencies, there may be strategies and/or tactics which

can be profitably exploited in the market (i.e., possibly regardless of what individual

investors do).

4. They exhibit odd and sometimes illegal behavior, which should concern society (even if

it seems to be uninteresting to the oversight bodies, e.g., the SEC). That is, suboptimal




behavior, even if unexploitable by other actors in the financial markets, should be of

interest to society from an efficiency standpoint (specifically, resource allocation).

5. Labor market reasons and efficiency associated with #4 (e.g., to the extent certain

characteristics are associated with more optimal performance; then we may by being

selective end up creating a force for economic efficiency).

6. In terms of structuring an IP, who you select to be a PM or the equivalent and how you

set up the PM position or its equivalent is important (especially given what is known).

One finding concerning PMs is that characteristics matter. That is, some details concerning the

people matter, and normatively they shouldn‘t.

―We begin by showing that simple regressions of market excess returns on the managerial

characteristics in our data with no other controls suggest relationships between education, age,

and performance which are so strong as to make it seem unlikely that ‗ability‘ differences could

be the whole story.‖

Chevalier and Ellison (1999, p. 876)160

What they found:

1. Strongest result was that PMs from undergraduate institutions with higher average SAT

scores produce higher returns (e.g., Princeton with a composite 1355 score vs. the mean

school in the sample with a composite 1142 score produces about 100 BPs per year

excess risk-adjusted returns).

160 Chevalier and Ellison (1999) checked a sample of 492 growth or growth and income funds for at least some partof 1988-1994 period (using cross-sectional regressions).




2. Next strongest result was that younger PMs had higher returns than older PMs (about 100

BPs per 12 year differential, e.g., a 25yr. old vs. a 37yr. old). Although, much of this

differential was due to younger managers working for firms with lower expenses and

survivorship bias (due to hiring and firing sensitivity for younger PMs vs. older).161

3. MBAs outperform non-MBAs by about 63 BPs per year, but it is entirely due to their

holding of higher levels of systematic risk (thus, getting an MBA suggests you don‘t

improve risk-adjusted performance, but you do get someone who will tend to try to game

the system).

The lessons are simple, going to higher SAT schools and age seem to matter in predicting

relative better risk-adjusted performance (therefore, you want high SATs and younger

managers), but these results tend to go against very established finance firm advertising

campaigns and conventional wisdom that suggest that age and ‗street smarts‘ are critical PM

characteristics. In short, it is very likely that IQ and age matter, and that observation is not very

EMH friendly.162

Remember, under the EMT both dumb and ‗smart money‘ in equilibrium

receive the same returns.163

161 Costa and Porter (2003) also find that ―tenure‖ and PM job per formance (i.e., as related to relative risk-adjustedreturns) does seem to matter. They focused on analyzing PMs that had been managing the same portfolio for ten ormore years and found that excess returns tended to be concentrated in a few years, and subsequently tended to not berepeated again. Again, this supports the opposite of the common contention that ―experience matters‖ (i.e., regardingat least equity mutual fund PMs).162 Although the authors try their best to support it (e.g., they suggest school networks may be the cause, not IQ,which is unlikely).163 This suggests hiring for an investment manager seems relatively easy (the higher the I.Q. and the younger thebetter, and if you decide for some other reason to hire an MBA watch him or her closely).




In addition to undergraduate institution and age, what other things might be predictive of PM

performance or lack thereof? As it turns out, many PMs seem to spend an inordinate amount of

time trying to game the system. For example, it has been found that PMs often ―boost variance‖

in their attempt to catch the competition. This is called ―boosting variance‖.

Brown et al. (1996) tends to confirm other research and suspicions. Based on ―over 330 growth-

oriented mutual funds‖ (during the 1980-1991 test period), it was found that PMs in the sample

that were behind their competitors tended to increase their portfolio‘s variance, while those that

were ahead tended to decrease their variance.164 Two quotes from the study should put this into

perspective:

―To this end, our goal in this paper is to test the hypothesis that given the profession‘s current

system of assessing and reporting fund performance on an annual basis, managers with either

extremely good or bad relative returns at mid-year have incentives to alter the investment

characteristics of their portfolios. The central testable implication that emerges from our analysis

is that the set of funds most likely to be ‗losers‘ in the final tournament results will see their risk

levels increase relative to the group of probable ‗winners‘.‖

―Perhaps the most important implication of this research is that it is possible that the current

tournament structure of the mutual fund industry truly does provide adverse incentives to fund

managers. That is, by focusing so much attention on relative return performance that is assessed

annually, the industry may be effectively changing managerial objectives for a long-term to

short-term perspective.‖

164 Also, the effect was more pronounced over the last six years of the sample period. Therefore, things have tendedto have only gotten worse (i.e., from an overall variance perspective).




Brown et al. (1996, p. 86, 109)

The understatement here is with respect to ―adverse incentives‖. The incentive structure, and

overall structure, of much of the industry creates incentives for some PMs to increase variance

beyond what is optimal and others to decrease their variance below what is optimal. Maybe this

is some version of ‗cancellation‘, but because it is targeted at variance, and not price, it is likely

to be doubly inefficient. Thus, ‗winner‘ portfolios begin to sub-optimally decrease variance

around the half-way mark of the ‗tournament‘ (about six months into the year) and ‗loser‘portfolios begin to sub-optimally increase variance around the same time. Therefore, not only is

variance sub-optimal but, in addition, PMs have an investment horizon (the average PM‘s

horizon any year is ½ year), which doesn‘t even remotely match their clients‘ investment horizon

(covered in the individual investor section).165 Thus, some PMs are actually causing increased

volatility and others decreased volatility, but both ‗winners‘ and ‗losers‘ ar e focused on too short

an investment time horizon.166

Another example of economically inefficient PM behavior in the financial markets is something

called ‗portfolio pumping‘. PMs will drive up the prices of securities they hold at key reporting

times (especially year-end, also quarter-ends). Effectively PMs buy high and shortly thereafter

sell low; which reverses the notion of ―buy low and sell high‖. Imagine ―buy high and sell low‖

165 In addition, it is also possible that PMs may be simply trying to buy time. In effect, and analogous toHirshleifer‘s (1993) point about corporate investment decisions, PMs may be using other peoples‘ money to stay inpower and cloud the ability of others to judge their performance, at least in the short run.166 Given that most PMs (and possibly most other finance ‗professionals‘) have about one year investment horizons,and that many successful mean reversion strategies take a long horizon to be successful, this would suggest that longhorizon strategies would have a basis for possible success.




as investment advice? ‗Portfolio pumping‘ distorts prices (i.e., is inefficient), increases variance,

and is illegal, yet it commonly happens.

―… managers inflate quarter-end and portfolio prices with last-minute purchases of stocks

already held. The magnitude of price inflation ranges from 0.5 percent per year for large-cap

funds to well over 2 percent for small-cap funds. … and that the inflation is greatest for the

stocks held by funds with the most incentive to inflate‖

Carhart et al. (2002, p. 661)

Other Carhart et al. (2002) findings and thoughts:

• There is a ―surge of transactions‖ in the ―last few minutes‖ at quarter -end, and especially

year-end, with a corresponding abnormal increase in price that day and an abnormal

decrease in price the next day (there is no effect at month-ends that are not quarter-ends).

• This ―is a significant opportunity for potential sellers, and a significant hazard for

everybody else.‖ Carhart et al. (2002, p. 661)

• The ‗benchmark- beating hypothesis‘ is rejected and the ‗leaning-for-the-tape hypothesis‘

is accepted (i.e., due to the nature of flows, equity mutual fund PMs ‗portfolio pump‘ in

order to increase an already high annual ranking, not to beat the S&P 500 index

benchmark).

• Some, ―if not all‖, of the inflation associated with ―best- performing funds‖ at year -end is

explained by ‗portfolio pumping‘.

• There may be a profitable strategy for mutual fund investors, especially for small-stock

funds.




• ‗Portfolio pumping‘ also ―comes from the same source as the incentive to boost variance,

which is the convex relation between net new investment and performance.‖ (Carhart et

al. (2002, p. 690))

• This type of activity is illegal, but not punished (at least not in the U.S.).167

• Unlike ‗window dressing‘, this effect will tend to moderate the ‗January effect‘ (i.e.,

without this type of behavior, the ‗January effect‘ would be more pronounced).

• ―The usual perspective on an investor purchasing a security is that he wants the lowest

price and the least impact.‖ Carhart et al. (2002, p. 691) This is the opposite of what wasfound.

Again, the highest price with the most impact, how‘s that for a strategy? It‘s certainly doable,

and not very challenging, and has the added benefit of helping the annual bonus.

Another perverse (i.e., from a market efficiency standpoint) behavior on the part of PMs is called

‗window dressing‘. It is the practice of temporarily modifying portfolio structure (especially

money market funds) to present the appearance of a less risky portfolio. For example, Musto

(1999) found:

―The evidence presented here indicates that money fund managers do not manage their

claimholders‘ money as they would manage their own.‖

―The analysis shows that funds allocating between government and private issues hold more in

government issues around disclosures than at other times, consistent with the theory that

167 The authors try to rationalize the illegal and immoral aspect of the behavior; although it is ―considered illegal‖they say that ―the aggregate monetary effect of marking up across a fund‘s investors is zero.‖ Carhart et al. (2002, p.690) Also, only in Canada has anyone been penalized for this type of activity (Carhart et al. (2002, p. 691)).




intermediaries prefer to disclose safer portfolios. Cross-sectional comparisons locate the most

intense rebalancing in the worst recent performers.‖

Musto (1999, pp. 950, 935)

Therefore, the ‗window dressing‘ is accentuated for ‗loser‘ portfolios. As with the other effects,

the incentives seem to be most perverted for those with the most to gain.

Therefore, the various mutual fund gaming literature (‗tournaments‘, ‗portfolio pumping‘, and

‗window dressing‘

168

) indicate that:

1. Additional transaction costs are incurred for dysfunctional reasons.

2. PMs do not manage their clients‘ money as they would their own.

3. Extra risk is introduced as a result of the annual investment horizon (i.e., especially for

‗loser‘ portfolios). Therefore, not only is risk for ‗loser‘ funds too high, but the

investment horizon may be too short (and ‗winner‘ funds risk may be too low).

4. Performance takes a back seat to games, or who has time to manage?

5. Pricing is driven by annual tournaments and incentive structure.

6. Where is the SEC or other legal and regulatory authorities?

To summarize:

168 Lakonishok et al. (1991) also find that equity pension fund managers have a strong tendency to ‗window dress‘ atquarter ends and especially the end of the year. This is somewhat unexpected on two levels: (1) in that it iscommonly thought that institutional PMs are not under the same pressures as mutual fund PMs, and (2) this isshown for equity portfolios where the concern should not be the same as say money market mutual funds.




Many EMH/EMT proponents would say that taken as a group ‗anomalies‘ cancel each other out

(e.g., there is as much overreaction as underreaction, etc.). This is nonsense. As can be seen by

this summary graph, most documented economically dysfunctional games tend to push in the

same direction for the simple reason it is easier for most agents to do things like buy high and

sell low than the reverse.169 In addition, and as a sort of perverse compliment to that basic

observation, because it is easier to pay too much than too little, it is easier to achieve greater

volatility than is optimal. This simple logic for both is that if, as a PM, I push prices too far (i.e.,

ignoring pushing them too low) by paying too much (an easy thing to do) then I also have

increased the volatility of those prices beyond that which would have naturally happened without

the action. Therefore, the easy, and inefficient, impacts are: (1) prices being too high, (2)

volumes being too much, and (3) volatility being too high (i.e., from a normative efficiency

standpoint).

169 We will go over other cases where this is shown to be true.

Empirically Identified Portfolio Manager ("PM") Dysfunctional Behavior that Impacts the MarketImpac t on Impac t on Impac t on

Behavior Original Academic Study Asset Class Price Volume Volatility Comment

"boosting variance" Brown et al. (1996) equities ↑ ↑ ↑ & ↓ losers' & 'winners' go in opposite directions

"port fol io pumping" Carhart et al . (2002) small -cap equi ties ↑ ↑ ↑ strictly illegal, yet hard to prove

"window dressing" Musto (1999) short-term securities ↑ & ↓ ↑ ↑ a response to perceived market demand

Note, the one unambiguous result is that there is more trading (i.e., volumes tend to increase in all cases).

Regarding "boosting variance", by definition, there is a larger number of "losers" than "winners", thus, overall, volatility is likely to increase.

Regarding "window dressing", by definition, those securities perceived to be less risky are less liquid and will decrease in price more than the securities

purchased, thus prices are likely to increase.

Thus, overall, there is a general tendency for upward price pressure and vol. (therefore, these types of behavior tend to push markets in the observed directions).




You may be asking yourself, given the issues with PMs, why aren‘t the bad ones just identified

and fired?170 Clearly, the overall structure of the firms and the industry isn‘t conducive to hire,

promote, and retain those that would most likely be averse to pushing prices, volumes, and

volatility beyond efficient levels.171 To the extent PMs are replaced, an older PM would have to

normally underperform severely for several bonus periods in order to be terminated (see, e.g.,

Gallo and Lockwood (1999).172

170 Of course, ultimately if performance is systematically bad enough long enough firing won‘t matter; the fund will just disappear due to outflows of assets (i.e., assuming it isn‘t a closed-end fund). See, for example, Brown andGoetzmann (1995) on this.171 Of course, there are likely exceptions.172 Gallo and Lockwood (1999) found for equity mutual funds:

1. ―Results show that funds experiencing a managerial change performed poorly before the change, primarilyas a result of inferior security selection. Risk-adjusted performance, on average, improved 200 basis points

annually and systematic risk increased significantly after the management change.‖ (p. 44) The t-statisticassociated with the change in alphas was around 3.65, and the betas ―reverted to the mean.‖ (p. 51)

2. Over 65% ―experienced a shift in investment style‖. 3. ―Thus, performance generally was below the benchmark prior to the management change and matched the

benchmark after the management change.‖ (p. 46) 4. In terms of performance attribution, security selection improved to about average, while market timing

stayed about the same (i.e., based on the Treynor-Mazuy method).5. Therefore, most funds changed performance, risk profile, and style by changing the PM.

Given these results, why not change more often? Clearly, the incentives and/or structure aren‘t generally supportiveof performance related change (i.e., ignoring the case of young PMs).




THE PERSONNEL FILTER – A TENDENCY FOR THE ADVERSE SELECTION OF PMS –

OR WHY AREN‘T ONLY RATIONAL ARBITRAGEURS SELECTED TO BE PMS?

It is understandable if the reader is confused as to how such highly paid people (i.e., PMs) can

operate in the markets where some are consistently breaking laws/rules (i.e., at least in the

mutual fund or retail area) and most seem to ignore obvious arbitrage possibilities (i.e., in the

traditional finance sense). In fact, some even purposely buy high and sell low (i.e., the opposite

of arbitrage or call it reverse arbitrage).

Combined with the fact that many non-PM investors are not acting strictly rational, the most

plausible answer is related to the following expression: ―the markets make the manager‖. Dunn

and Theisen (1983) researched the issue of ―how consistently do active managers win?‖173 Their

answer (Dunn and Theisen (1983, p. 47)) was: ―Essentially not at all. Or, perhaps, they lose with

the same degree of consistency.‖ In reference to PMs that were top quartile performers, they

found (Dunn and Theisen (1983, p. 49)) that ―the success of these managers seemed to reflect a

high level of market dependence.‖ In short, they found that if the market doesn‘t make the PM,

it doesn‘t seem to be anything else. Again, the essential finding is that those PMs that did well in

up markets tended to do well in up markets and poorly in down markets, while those that did

well in down markets tended to do well in down markets and poorly in up markets.174

Of course,

this is a general result and especially depending on the degree of underperformance, there is

173 Theirs is one of a limited number of articles on institutional PMs. Most academic research on PMs has beenfocused on mutual fund PMs. This is likely due to data availability.174 Consistent, albeit indirectly so, with this general finding are the results of Bauman and Miller (1995) where theymeasured performance for pension funds and mutual funds over a ―complete stock market cycle‖. Essentially, bymeasuring over a full cycle (i.e., up and down market combined), outside of style differences resulting in significantdifferential performance, no significant differences would be expected to be found (which is what was generallyfound). Interestingly though, they found that pension funds and mutual funds had similar profiles vs. bank pooledfunds and insurance company pension accounts (i.e., with respect to performance consistency).




some evidence to suggest that very poor institutional performers survive and tend to poorly

perform across up and down markets (see, e.g., Christopherson et al. (1998)), which is similar to

mutual funds.175

In the ―markets make managers‖, we have a crude personnel filter.176 Clearly, at least with

respect to institutional active managers, there has been a tendency for momentum or asymmetric

beta (i.e., high in either an up or down market) traders to survive, otherwise you would see a

tendency for markets to unmake managers. Therefore, if there was a filter for consistent PMs,

one should see the opposite of that which has been documented. Instead of observing managers

that are consistent we observe PMs that seem to display high ―betas‖ in increasing and

decreasing markets.177 For example, Ankrim and Ding (2002) document increasing volatility and

dispersion among active institutional managers (worldwide, not just a U.S. phenomenon, and not

just for the current up cycle), which they warned would predictably result in future poor returns

for those ―chasing‖ such portfolio returns. Essentially, they documented increasing betas in an

increasing market. Thus, you have a set of active managers that have high betas for up markets

and relatively low betas for down markets and vice versa, but relatively few in the middle (i.e.,

175 The obvious question is why aren‘t these managers fired? At least with mutual funds there can be a disconnectfrom client to PM and/or management company, but with pension funds and the like there isn‘t suppose to be. 176 There is subtle evidence to suggest to me that this filtering is encouraged by the length and degree of mispricingin the market. For example, an annual survey of institutional investors found that ―there are fewer and fewer trulydisciplined investors.‖ (see Bernstein and Kirschner (2003)) Thus, to the extent stock values continued to deviate

from economic fundamentals, fewer and fewer investors that keyed on those fundamentals are left in the market.177 Odean (1999, pp. 1279-1280) makes proffers argument that ―There are reasons, though, why we might expectthose who actively trade in financial markets to be more overconfident than the general population. People who aremore overconfident in their investment abilities may be more likely to seek jobs as traders or to actively trade ontheir own account. This would result in a selection bias in favor of overconfidence in the population of investors.Survivorship bias may also favor overconfidence. Traders who have been successful in the past may overestimatethe degree to which they were responsible for their own successes — as people do in general (Ellen J. Langer andJane Roth, 1975; Dale T. Miller and Michael Ross, 1975) — and grow increasingly overconfident. These traders willcontinue to trade and will control more wealth, while others may leave the market (e.g., lose their jobs or theirmoney).‖ Of course, this directly applies to active PMs.




―market neutral‖ overall). This should be especially disconcerting to investors that rely on active

managers to avoid market downturns.178 Logically, if institutional investors were truly concerned

with risk-adjusted performance and most worried about downside risk, they should be

encouraging the selection of PMs that do relatively well in down markets. In contrast, in the case

of mutual funds, Howe and Pope (1996, p. 37) find that: ―The ability of Forbes down-market

ratings to predict risk-adjusted performance during future down-markets appears to be much

better than the ability of Forbes up-market ratings to predict risk-adjusted performance during

future up-markets.‖ Therefore, and although not based on a strong statistical result, if the Howe

and Pope (1996) result is true, then there is virtually no risk-reward benefit to keeping up market

retail PMs around, yet not only are they kept around, but they seem to be in the majority.179

Obviously, adverse or negative selection doesn‘t only apply to fields such as insurance, used

cars, and ‗no doc‘ loans. It seems clear that the active PM personnel filter is not keying on

rational arbitrageurs with significant market timing skill.180 In reality, quite the reverse seems to

be hap pening. PMs that expose their clients‘ capital to excessive risk in up and down markets

tend to be filtered. If anything, this would argue that active institutional PMs (as well as retail

178 Commonly, brokers and financial advisors will market that the primary reason an investor should pay extra for

active management is that they will tend to avoid market downturns, not make it worse.179 It is important to keep in mind that mutual fund betas tend to be much more stable than their alphas; and, hence,predicting standard risk-adjusted retail PMs performance can be somewhat of a ‗crap shoot‘ in the best of times.180 For example, if it were, we should expect to see more female PMs (i.e., based on the fact that females tend tohave a better trading record than men (see Barber and Odean (2001)) as well as a better overall demeanor fortransacting in the financial markets. In fact, the majority of past and present PMs are male. Furthermore, of thosefemales selected they perform about the same as the men (see, e.g., Atkinson et. al. (2003)). Therefore, there seemsto be a relative improvement in the gender selection toward men and away from women that seems to somewhatcompensate for men‘s general tendencies to overtrade and underperform (i.e., at least underperform relative towomen).




PMs) display behavior that reinforces market inefficiencies181

, and whatever aggregate personnel

filtering across the industry is happening182, it isn‘t generally weeding out those that display this

behavior.183

Finally, there seems to be a significant difference between retail and institutional fund flows that

is likely to result in at least somewhat different personnel filters in each area. Del Guercio and

Tkac (2002, p. 523) find that: ―In contrast to mutual fund investors, pension clients punish poorly

performing managers by withdrawing assets under management and do not flock

disproportionately to recent winners. … We conclude that pension fund managers have little

incentive to engage in the risk-shifting behavior previously identified among mutual fund

managers.‖ Unlike institutional investors, ―return chasing‖ is in fact very common among mutual

fund investors, and fund companies know this (e.g., see Karceski (2002)). Clearly, given the

―market makes the manager‖ results found for active pension fund managers, the selection

process for institutional PMs is not without issues; but also clear is the fact that retail investors

display much more lax standards, if not downright perverse standards, when evaluating PMs than

181 In addition, confirmation bias itself is likely to play a role in this dynamic. For example, and although not directlyrelated to PMs, Sabourian and Sibert (2009, p. 26) remark concerning the financial services industry: ―people whoserewards are determined by the perceived ability, as well as their long-term performance.‖ Although, because mostPMs are mostly concerned with annual bonuses which in most cases do not have explicit long-term components(i.e., excluding HFs), I disagree with the ―long-term‖ portion of the comment, but the point about perception seems

apt. Therefore, without an explicit mechanism to counter perceptions, then those perceptions will tend to hold and bereinforced (e.g., confirmation and related baises like hindsight bias).182 Furthermore, to the extent that PMs are clustered in certain geographic locales Hong et al. (2005) have found thatthere is a strong tendency to imitate other PMs in that locality (independent of geographic bias). Therefore, to theextent a high level of geographic PM herding occurs, filtering is likely to be subject to a geographic component aswell as a market determined piece. On common strategies more generally, also see, for example, Brown andGoetzmann (1995).183 I would expect that this behavior might be worse for retail PMs than institutional. My reasoning is that theaverage institutional money management firm and those that evaluate them are significantly more rigorous in theirevaluations than the average mutual fund organization and their average clientele.




do purely institutional clients. The result is likely to be a dysfunctional spectrum as we proceed

from passive institutional to more active institutional to finally active retail.

In summary, I would minimally expect to see the active retail PMs to set the dysfunctional

boundary conditions for active institutional PMs. If the basic filter for active institutional PMs is

that the ―market makes the manager‖, I would expect that that would minimally hold true for

active retail PMs. In fact, given their incentives and organizational structure, I would expect a

whole lot more adverse or negative selection to occur in the retail arena. In short, unless

evaluation criteria change greatly in the future, don‘t expect most institutional or retail PMs to be

the rational arbitrageurs required to keep market pricing efficient.




INDIVIDUAL INVESTORS – THE MOST MALIGNED GROUP

So far, the ‗smart money‘ has been a disappointment, and although I haven‘t covered hedge

funds184

, what about individual investors? For example, given that they don‘t have incentives to

buy high and sell low, they should be alright? If you thought that institutional ‗smart money‘

buying and selling securities wasn‘t exactly acting within the purely rational economic animal

paradigm (i.e., unless we emphasize rather tortured agency rationalizations), then I‘m not sure

how to present this group, other than to say that nobody is above suspicion and the reader should

at least begin to see the validity of the behavioral approach (at a minimum as a lens with which

to view the financial markets).

Firstly, how do individuals actually construct their portfolios? Statman (1999, p. 14) has a useful

representation contrasting a standard finance normative representation with something closer to

descriptive reality:

184 Institutionally, due largely to data availability, we have mostly covered retail institutional PMs, for example.




From: Statman, M., ―Foreign Stocks in Behavioral Portfolios‖, Financial Analysts Journal, March/April 1999, 55, 2,12, 16, p.14.

These two diagrams contrast an abstract Markowitz185 (1952) on the left vs. an abstract

behavioral view of individual portfolio construction on the right. As pointed out by Lopes

(1987), for the individual, portfolio construction and ‗risk‘ is much about hope and fear (not fear

and greed), but it almost universally begins with hope and/or fear. The behavioral approach also

explains why people can simultaneously buy lottery tickets and money market funds. Essentially,

they want what seem normatively as contradictory desires (e.g., they hope for large positive

gains from the lottery ticket, and they feel they need to insure against financial calamity with the

money market fund).186 Normative finance, especially mean-variance portfolio optimization,

doesn‘t easily reconcile such a simple combination. Even if normatively nonsensical, we observe

people routinely making such combinations.

More specifically, individuals tend to view their portfolios as a ―layered pyramid‖ (diagram is

from Statman (2007, p.122)).

185

Even though credited with it, Markowitz personally didn‘t believe in pure mean-variance optimization. Inaddition, William Sharpe helped to develop a Website called ―financial engines‖ that performs a mean-varianceanalysis, but presents information on portfolios in terms of fear, hope, and aspiration. In short, even those largelyresponsible for ―modern finance‖ don‘t think of actually forming portfolios for themselves or others based solely onmean-variance analysis.186 Some securities seem especially designed to cater to these kinds of needs. For example, British Premium Bondshave lottery tickets instead of interest/coupons.―Most investors do not choose securities by ascertaining where the risk-return profiles place the securities on themean-variance frontier. Rather, investors‘ choices stem from their emotional reaction to the features promised by thesecurities.‖




Source: Shefrin, H., Beyond Greed and Fear, Oxford University Press, New York, New York, 2007, p. 122.




What is powering this type of construction isn‘t the standard finance definition of risk, or return.

For individuals, risk can span a wide spectrum and is ultimately context dependent.187 For

example, someone who is very worried about losing their job (or just lost it) might be much more

interested in investment safety considerations vs. another who is most hopeful about early

retirement. Security, potential, & aspiration are all critical goals. Examples of specific goals

include, for example, buy a home, fund college, retirement, etc. Financial

planners/advisors/brokers will often suggest that investors earmark particular investments for

specific goals.

188

In addition, ―anticipation has value.‖ For example, Lowenstein (1987) found

that a kiss from a movie star has the most value at three days vs. immediately, three hours, or one

day. This contrasts with anxiety (anxiety is a manifestation of fear). These sorts of considerations

are real, impact portfolio construction, but are hardly mean-variance arguments.189

Of particular importance are hope and fear (see Lopes (1987)). Hope and fear affect the decisions

investors make. Hope encourages an investor to focus on the ―best case scenario‖, whereas fear

encourages an investor to focus on the ―worst case scenario‖. There is a tension between our

hopes for the best investment/gamble outcome, and our fear for the worst investment/gamble

outcome. All the while we wait anticipation either converts our hope into pride (if it works out as

hoped) or our fear, then anxiety, into regret (if it works out as feared). All humans seem to have

187 Statman (2003) makes the point that investors haven‘t changed that much over the years, that conflictingemotions and/or desires often drive investment decisions (e.g., they ―aspire to be rich‖, yet want to ―avoid the painof regret‖). 188 Financial planners/brokers/etc. will often advise people to decrease stocks as you age. But the critical normativeconsideration in that case is investment horizon not age. In reality decreasing stocks is a regret avoidance strategy.189 As Fisher and Statman (1997) point out: ―investors care about more than expected returns and variance as theyconstruct their securities portfolios‖.




these two somewhat equally matched polar extremes, but one tends to dominate (although that

can also be context and experience dependent).

Given their overall importance to people, what is it about hope and fear (with hope and fear

being polar opposites, with hope being positive, fear being negative)? Lopes‘ places four pieces

on the ―emotional time line‖ (and in order of importance) thusly: (1) fear, (2) hope, (3) goals, and

(4) greed. Investors generally evaluate investments this way. First we make investment

decisions, then we wait, then the outcome exposes itself (and along the way, many emotions are

commonly experienced). Polar extremes can quickly be experienced, from one extreme to the

other. For example, hope to fear and back again. Risk tolerance is formed by many based on the

tension between various emotions and even their goals or aspirations. It is important to note that

if there is a change (e.g., the stock market drops substantially, a war, etc.) even the timeline or

investment horizon can get compressed. Almost regardless of the definition, emotions heavily

influence the tolerance for risk, which in turn influences portfolio construction. Therefore, our

hopes, fears, other emotions, goals or aspirations, and the events themselves all combine to

impact portfolio construction (and the context of those emotions and goals). Thus, the tension

between our various emotions and aspirations ultimately determines what we invest in (be it

government bonds, commodities, stocks, etc.).

It may useful to even make a few parting comments upon the emotion of regret and its potential

impact in the financial markets. Firstly, the hiring of financial ―advisors‖, brokers, etc. could be

argued to be largely driven by the need to shift responsibility, but the primary motivating factor




may be largely the need to minimize regret. It is convenient for individuals to avoid self-

attribution for mistakes they might make. This is especially true of areas where the probabilities

are ill-defined before the outcomes are known (e.g., future prices in the financial markets). In

short, by hiring a financial ―professional‖, given what we known descriptively about investors,

isn‘t it likely that what they are hiring is not so much an advisor but a future scapegoat? By

hiring a ―professional‖ I may just be mostly shifting responsibility at some future time from

myself to them. Secondly, as Kahneman & Tversky (1982) pointed out – ―regret is

counterfactual‖. It is especially painful for most people to deviate from the norm. Thirdly,

Gilovich & Husted-Medvec (1995) point out that there is a difference between short-term vs.

long-term regret. Specifically, they found ―that most people regret the things they didn‘t do.

When it comes to the long-term, we regret inaction.‖ Therefore, in the short-run we regret our

actions, but in the long-run we regret our inaction. For example, after you put in the purchase

order and the price goes down the next day, ―I knew I shouldn‘t have purchased IBM!‖;

conversely, if you didn‘t put in the purchase order and you happen to notice the price went up, ―I

knew I should have purchased IBM!‖ Isn‘t it likely that hiring or quoting a financial

―professional‖ is just a psychological call option? Finally and ultimately, any discussion of

investments and regret will involve self-attribution bias. If it goes well you take the credit (chalk

it up to skill), otherwise it‘s someone else‘s fault, or just bad luck. Also as mentioned already,

regret is more than the pain of loss, it is the pain associated with being responsible for the loss,

and its intensity can vary greatly. Thus, many seemingly odd behaviors (i.e., from a normative

perspective) are likely to be driven by issues like minimizing regret and needing to feel good

about yourself and your financial decisions.




We have some idea of how individuals make investment decisions and construct portfolios, but

do we know how that manifests itself in the actual financial markets? Luckily, we have a decent

amount of descriptive research on individual investors in the financial markets. Some of the

observations are the following:

• Their biases and sentiment affect asset prices (see Barber et al. (2003)).

• They ―follow the advice of false experts‖ and ―believe excessively in momentum

strategies‖ (see Avery and Chevalier (1999)). • They have limited attention, fall prey to the representative heuristic, and the disposition

effect (see Barber et al. (2003)).

• They tend to buy and sell stocks with ―strong past returns‖ (this tends to be stronger at

short horizons (one or two quarters) for sales vs. buys and weaker at long horizons (up to

twelve quarters for buys vs. sales). See Barber et al. (2003)

• Their buys are concentrated in fewer stocks than their sales, and ―they are net buyers of

stocks with unusually high trading volume‖ (also, see Barber et al. (2003)).

• They ―are more likely to be net buyers of attention grabbing stocks than institutional

investors‖, and tend to systematically lose when trading against institutions trafficking in

the same ―attention grabbing stocks‖ (Barber and Odean (2006), or, to a lesser degree,

even based on overall trading (Barber et al. (2006)).

• Most don‘t seem to systematically incorporate taxes into their trading activities (Barber

and Odean (2004)). In short, some notice and act upon the effects of taxes, but they could

generally do much better to ―optimally allocate their assets‖.




• ―They do not appear to manage their assets across retir ement and nonretirement accounts

to maximize tax efficiency.‖ (Bodie and Crane (1997, p. 13))

• They tend to buy mutual funds with high loads/brokerage commissions and ―attention

grabbing information‖. (Barber et al. (2006)) There is a strong negative relation between

fund flows and load fess, but not operating expenses (where the marketing and

advertising expenses used to garner attention are imbedded – see, e.g., O‘Neal (1999) on

mutual fund share classes and expenses, and Jones and Smythe (2003) on the lack of

information on risk and fees).

190

• The proportion of assets held in equities declines with age and rises with wealth (Bodie

and Crane (1997)).

• They cause noise and/or excess volatility. For example, ―unusual levels of individual

investor sentiment are associated with greater volatility of closed-end investment funds.

Furthermore, this volatility occurs only when the market is open and is associated with

heightened trading activity. It persists after controlling for market wide volatility and

changes in fund discounts.‖ (Brown (1999, p. 82)

• The true speculators among them feel that ―being in the action is more important than the

financial consequences. … for the majority of the speculators studied, the primary

motivation for continuous trading is the recreational utility derived largely from having a

market position.‖ (Canoles et al. 1997, p. 1)) This is in spite of the fact that the majority

consistently lose money.191

190 For a more comprehensive view of this, see Kihn (1996).191 For example, Linnainmaa (2003) noted that ―day traders‖ in Finland were found to systematically lose relative toa control group, especially after brokerage commissions. Apparently, they will tend to keep trading as long as theyhave sufficient capital.




• Have heterogeneous beliefs; but generally differ from WSSs, and are more in line with

the views of newsletter writers. Although, there is a negative and statistically significant

relationship between WSSs‘ market sentiment and individual investors‘ (i.e., they are

contra-forecasters of the market). See Fisher and Statman (2000).

• Are generally reluctant to realize a loss (i.e., one half of the ―disposition effect‖192).193

See Shefrin and Statman (1985) & Odean (1998)194

for stocks and Heisler (1994)195

for

futures.

• Are generally happy to realize a gain (i.e., the other half of the ―disposition effect‖).Odean (1998) Also, this propensity to realize a gain is likely a driver of excess volume

(and likely volatility as well) during a rising market (Barber et al. (2007)).

• Although tax-motivated selling is most evident in December, the tendency to hold

‗losers‘ and sell ‗winners‘ is ―suboptimal and leads to lower after -tax returns.‖ (Odean

(1998, p. 1775))

• Risk aversion varies with wealth, age, education, and income (Riley and Chow (1992)).

192 Shefrin and Statman (1985) ―coined the term ‗disposition effect‘ as a predisposition toward ‗get-evenitis.‘‖ Get-evenitis - ―difficulty people experience in making peace with their losses.‖ Loss aversion plays a role and people‘stendency to create an investment price reference point (which is central to Prospect Theory – ―PT‖).193 Also, see Barber et al. (2007).194 The Odean (1998) study of about 163,000 customer accounts confirms the ‗disposition effect‘ (see Shefrin andStatman (1985). It largely seems to be a self-control issue. Specifically: ―Investors who are risk averse realize moreof their paper gains than they do their paper losses. … realize gains 1.68 times more frequently than they realize

losses. This means that a stock that is up in value is almost 70% more likely to be sold than a stock that is down.Only in the month of December do investors realize losses more rapidly than gains, though only by 2%.‖ Inaddition, they tend to realize small losses and hold larger losses, and tend to sell the wrong stocks (i.e., ones thattend to do very well after they sell them).195 Heisler‘s (1994) study was on the impact of loss aversion on futures traders (more than 2,000 individual futuresaccount trading histories or over 19,000 trades of Treasury bond futures on the CBOT were analyzed (11/1989through 10/1992). They found (1) traders held losers longer than initial gainers, (2) when losers held, trading activityis non-profitable, and (3) only 24% showed a profit over the period (on average, off-floor traders lose $17 percontract traded).




• Fail to diversify (e.g., Blume et al. (1974)), practice ―naïve diversification‖ (see Benartzi

and Thaler (2001)), and exhibit a ―home bias‖ (French and Poterba (1991) and, e.g.,

Grinblatt and Keloharju (2001)). Overall, diversification seems of distant importance to

them.

• They trade too much (Odean (1999) and Barber and Odean (2000)196), and both relative

to institutions and overall lose great sums doing it (Barber et al. (2006)). Men trade more

than women, and it costs them more (Barber and Odean (2001)197).198 Furthermore:

―The surprising finding is that not only do the securities that these investors buy not

outperform the securities they sell by enough to cover trading costs, but on average the

securities they buy underperform those they sell. This is the case even when trading is not

apparently motivated by liquidity demands, tax-loss selling, portfolio rebalancing, or a move to

lower-risk securities.

While investors‘ overconfidence in the precision of their information may contribute to this

finding, it is not sufficient to explain it. These investors must be systematically

misinterpreting information available to them. They do not simply misconstrue the

precision of their information, but its very meaning.‖

196 And it costs them a great deal to do so (Barber and Odean (2000, pp. 799-800)): ―the average householdunderperforms … by about 9BPs per month (or 1.1 percent annually). … the 20 percent of the households that tradethe most often. … The net returns lag a value-weighted market index by 46BPs per month (or 5.5 percent annually).… After a reasonable accounting … the underperformance averages 86BPs per month (or 10.3 percent annually)."

In short, they ―pay a tremendous performance penalty for active trading.‖ Barber and Odean (2000, p. 773) 197 Barber and Odean (2001, p. 261) found that: ―men trade 45 percent more than women. Trading reduces men‘s netreturns by 2.65 percentage points a year as opposed to 1.72 percentage points for women.‖ 198 Although, based on a study of fixed income mutual funds, Atkinson et al. (2003) find no significant differencebetween women and men PMs. Thus, at least for that particular sample, whatever personnel filtering is going on, itis resulting in selecting women that produce about the same portfolio characteristics as their male counterparts.Therefore, relative to Barber and Odean (2001) sample, which I take to be representative of the population at large,the men selected to be fixed income PMs are relatively better than the women selected for similar positions. As aside note, they did find that funds flows themselves favored men over women, ceteris paribus, especially in theinitial year of managing; which would seem to contribute to sub-optimal gender selection.




Odean (1999, p. 1280)

• Although seemingly responsible for many ‗anomalies‘, they do not appear responsible for

the ‗day-of-the-week anomaly‘. (see Sias and Starks (1995))

The last two are important for two different reasons. Firstly, if it is true (which it seems to be the

case) that investors misinterpret the ―very meaning‖ of the information they are presented with,

then information itself is a problem, let alone embedding it into pricing, and the traditional focus

on information and prices may be largely misguided. Secondly, regarding the ‗week-end effect‘,it seems that individual investors are not completely responsible for all normative market

oddities and inefficiencies (i.e., from an EMT perspective). Therefore, it is entirely possible, if

not likely, different sets of agents/actors in the financial markets not only don‘t cancel each other

out, but indeed cause deviations by their apparent buying and/or selling pressure at the same time

in the same market(s) (the opposite of ‗cancelation‘).

We know descriptively that all the aforementioned behavior and/or biases result in the following

general profile of and stylized facts for individual investors (see the De Bondt (1998)199 study):

1. Investors are excessively optimistic about their own shares.

2. Investors are overconfident.

199 De Bondt (1998) studied a group of 45 investors at the NAIC (National Association of Investment Clubs). Of those studied, 2/3rd were men, the average age was 58, trading stocks for about 18 years, financial portfolios of about$310,000 (72% stocks), and spent about 7 hours per week on investments. Findings (after tracking their forecasts forthe DJIA and their own stocks): (1) Excessively optimistic about their own shares, but not the DJIA (which impliesoverconfidence). (2) Were overconfident (successively surprised by actual changes in prices). (3) Forecasts wereanchored on past performance (and they expected reversals). (4) Underestimated beta (or the degree to which theirown stocks moved with the market). A general description of their attitudes was as follows: (1) Do not believe inthrowing darts (i.e., when picking stocks). (2) Believed a solid understanding of firms is better risk-management toolthan diversification. (3) Reject beta as a measure of risk, and reject that risk/return are positively related.




3. Investors anchor on past performance.

4. Investors underestimate ‗beta‘.

5. Investors discount diversification (and reject ‗beta‘ as a measure of risk).

6. Investors reject the notion of tradeoff between risk and return.

It is fairly safe to say that, individual investors are not exactly the pool from which we would

expect large numbers of rational arbitrageurs to emerge, or find normally using normative tools

and/or models of finance and economics.

Finally, remember that the analysis of actual individual behavior in the financial markets matters

because outside a strong institutional framework/structure most individuals will behave as if

there were no framework/structure in place, by definition. Therefore, even if large institutions

were the only ones determining pricing in the financial markets (which they are not) it might

give some insight into their unconstrained behavior (i.e., a kind of behavioral boundary

condition), whereas the alternative would not be true of individuals, again, by definition.

I will end the chapter with prescriptive advice with respect to investors and prospective investors

in the financial markets:

• Don‘t trade too much.

• Don‘t hold ‗losers‘ too long and sell ‗winners‘ too quickly.

• Don‘t respond too much to noise & saliency and too little to fundamentals.

• Don‘t create excess volume and volatility.

• Don‘t follow the wrong advice.




• Don‘t pay too little attention to taxes.

• At least as a starting point, try a mean-variance optimizer.

Essentially, do the opposite of what most of us are predisposed toward doing.

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Chapter 7: Bubbles

―They soon received the name of Bubbles, the most appropriate that imagination could devise.

The populace are often most happy in the nicknames they employ. None could be more apt than

that of Bubbles.‖

Mackay (1980, p. 57)

I would argue that up until this global ―financial crisis‖, as a rule, finance academics didn‘t use

the term ―bubble‖.200 In fact, even a recent search on the term in academic article databases

returns a relatively de minimus number of economics oriented articles.201

This may be due to the

common practice of many academics to ridicule anyone that uses the term. Also, given that

prices are always and everywhere assumed ‗right‘, of course, EMT proponents generally don‘t

believe bubbles can even exist.

202

Therefore, if we accept the notion that a bubble is an extreme

deviation from fundamental or true economic value, then one can see why EMT proponents

might have trouble with the subject in any form. Much like the $100 bill lying on the street in the

EMH/EMT joke, it must be an illusion. Even, and maybe especially, the former Chairman of the

200 In reference to financial academics and other economists (especially Alan Greenspan), Krugman (2009) statedthat there is: ―a general belief that bubbles just don‘t happen. What‘s striking, when you reread Greenspan‘s

assurances, is that they weren‘t based on evidence — they were based on the a priori assertion that there simplycan‘t be a bubble in housing. And the finance theorists were even more adamant on this point. In a 2007 interview,Eugene Fama, the father of the efficient-market hypothesis, declared that ‗the word ―bubble‖ drives me nuts,‘ andwent on to explain why we can trust the housing market‖. Therefore, if something like a housing ‗bubble‘ is noteven possible, then what is there to discuss, let alone research?201 And many of those use the term ‗rational bubble‘. See, e.g., Treynor (1998) who insists that bubbles can berational, all the while using normative theory as his basis for the argument. In addition, economists have, it seemsunsuccessfully, to create normative models to show that bubbles can be ‗rational‘ (see, e.g., Froot and Obstf eld(1991)).202 Also, see Shiller (2002) on this issue.




Federal Reserve Bank was reluctant to suggest that they could exist.203

For example, even at near

the peak of the U.S. real estate bubble, a Congressman had to coax the following comment from

him:

―Although a 'bubble' in home prices for the nation as a whole does not appear likely, there do

appear to be, at a minimum, signs of froth in some local markets where home prices seem to

have risen to unsustainable levels."

Alan Greenspan (Chairman of the Federal Reserve Board) testifying before the U.S. Congress,

June 9, 2005

Therefore, even at the peak of arguably the largest real estate bubble in the history of mankind

the best you can get is effectively: ―Bubble? What bubble? Oh, you mean that spotty localized

‗froth‘?‖204 Remember, the Federal Reserve has the largest staff of Ph.D. economists and

financial economists on the planet. Clearly, the ultimate leader of that brain trust wasn‘t

concerned, let alone the staff. Suffice it to say that, and as long as the EMH/EMT religion

dominates, financial bubbles will not be a focus of economics or finance.

203 In fact, he was always reluctant to acknowledge their existence, even after the fact. He was almost famous forrepeating the folk wisdom of mainstream economics and finance that: ―it was very difficult to definitely identify abubble until after the fact – that is when bursting confirmed its existence.‖ This sentiment was used to justify theavoidance of popping the stock bubble that appeared to peak out in 2000, but that may have at partially re-inflatedrecently as I write this book (spring and summer of 2009).204 Indeed, his successor wouldn‘t use the term bubble when testifying to Congress during April 2006, when it was

becoming increasingly clear that the peak had been reached in housing prices in the U.S. Bernanke said that: ―Houseprices, which have increased rapidly during the past several years, appear to be in the process of decelerating, whichwill imply slower additions to household wealth and, thereby, less impetus to consumer spending. At this point, theavailable data on the housing market, together with ongoing support for housing demand from factors such as strong job creation and still-low mortgage rates, suggest that this sector will most likely experience a gradual cooling ratherthan a sharp slowdown. However, significant uncertainty attends the outlook for housing, and the risk exists that aslowdown more pronounced than we currently expect could prove a drag on growth this year and next. The FederalReserve will continue to monitor housing markets closely.‖ Therefore, Greenspan‘s successor didn‘t even consider that prices would stop increasing, just that the rate of growth would slow (he mentions ―slower additions tohousehold wealth‖, not losses); and, even if asked directly about it, nowhere did he mention a bubble.




But this book is about behavioral finance, and I would be remiss, at a minimum, not to cover

financial market bubbles, if not bubbles more generally. Therefore, this chapter will cover

financial bubbles and related issues. It will proceed as follows:

1. Define the term bubble.

2. Discuss likely and/or possible cause(s).

3. Discuss bursting effects.

4. Link them to behavioral finance. For example: When can it be ‗rational‘ to be ‗irrational‘ (i.e., if ever)?

Note that all four areas covered are highly debatable within finance and economics, if not

acrimonious. Again, the issue is largely one of the basic assumptions of finance and economics

in that up until recently there has been no place to even discuss the subject of ―bubbles‖ because

market efficiency doesn‘t really allow their existence and/or the ability to recognize them before

popping/bursting. Obviously, I beg to differ, and, as per my norm, I really on descriptive work

and basic logic to work my way through the subjects in question.




WHAT IS A BUBBLE?

Definition(s)

My focus here is to remain as intuitive as possible while moving from the most general to more

specific, then add a functioning mathematical definition.

Most general definition205

:

A bubble is an unsustainable rise in the price of something.206

Finance general definition:

A financial bubble is an unsustainable rise in the price of a financial asset (typically associated

with a large and pervasive deviation from fundamental value)207. For example (and related to a

financial bubble):

―A bubble exists when asset price inflation rises beyond what incomes can sustain.‖

//www.chrismartenson.com/

Therefore, the key term is ‖unsustainable‖. The reader might admit that those are reasonable

definitions, but that begs the question: What do you mean by unsustainable? By ‖unsustainable‖,

I mean just that. Maybe an example is in order. During the 1980s the stock bubble of the time

was arguabley IBM. IBM, a very large company at the time (relative to other companies and the

205 Actually, the most general definition I have seen is Nov and Nov (2008), where they extend the definition toinclude non-monetary things, such as article citations.206 Always keep in mind that basic economics applies. That is, prices are set by supply and demand, and the price isgenerally set by the highest bidder.207 Although it is rare that we can agree on what is truly ―fundamental value‖, there are cases where, by a lmost anydefinition, values are deviating so far from generally agreed fundamentals that we can be highly confident ininferring a bubble and even that its cause is likely behaviorally based (e.g., Chan et al. (2000)).




economy), was valued by standard present value techniques (i.e., for the common stock this

meant present valuing its expected cash flows, which were its expected dividends) to be worth a

trading value that implied a growth rate of distributable cash flows to be somewhere in excess of

a 25% growth rate to infinity. Now that‘s unsustainable, and let me inform you of at least one

way how we know. In effect, and assuming a modern economy could grow at around 4% annual

growth to infinity (which itself is debateable), then based on IBM‘s size at the time, combined

with this implied growth rate relative to the larger economy‘s growth rate, meant that IBM would

take over the U.S economy, then shortly thereafter all economys of the world in a few decades.

The question wasn‘t really so much if that implied growth could be sustained, but when the stock

market would recognize that it couldn‘t, for it was clearly ‖unsustainable‖, and therefore a

‖bubble‖.208

Also, is is important to reconcile this general definition with finance in general. That is, if

finance is concerned with present values (cash flows and associated discount rates), then

shouldn‘t we have a definition based on present values? Answer: Yes, but this, as usual

presupposes financial academics can agree on one, which they don‘t.209 Therefore, even if they

208 Another way to look at this is a heuristic called the ―rule of 72‖. For example, how long does it take to double at8%? Divide the interest rate into 72 and the answer is 9 years (i.e., roughly). Therefore, at 25% it takes about 3 years

to double. As an extension is the ‖rule of 10‖ which applied to 2 is = 1,024 or roughly 1,000. Therefore, if you

double 10 times, 1 turns into about 1,000. Thus, at 25%, after say three decades (30 years), millions turns intobillions, billions turn into trillions, etc. If IBM is worth $100 billion today, and is expected to grow at about 25% forabout 30 years, it will roughly be worth $10 trillion, or more than half of the U.S. GDP.209 Siegel (2002) attempted this, but made several huge assumptions which are difficult to reconcile with logic orempirical reality (e.g., as long as ½ of the future discounted cash flows – discounted at a rate we only know after thefact as well - for the next 30 years cover the price within 2 standard deviations then there is no ‗bubble‘, otherwise abubble). In short, he constructs a measure of a stock bubble which is loosely based on cash flows and almostimpossible to find a bubble, and if one is found it comes at odd times. Thus, for example, he finds that the DowJones at its peak in 1929 is considered fair value, while the 1987 crash stock market is undervalued by the measure.The method seems contrived and illogical, while the results are absurd.




don‘t rely primarily on present value, I find it more productive to rely on definitions that are

intuitive, generally acceptable, and reconcilable with reality.

With that example in mind, let‘s move onto a function mathematical definition of a bubble that

essentially is the math equivalent of our verbal definition(s) and has the added benefit that it

doesn‘t even require a fundamental model for the price level (this example is from Zhou and

Sornette (2006, pp. 299-300)):

―Mathematically, these ideas are captured by the power law

m

c t t B At p )()(ln , (1)

where p(t ) is the house price index, ct is an estimate of the end of a bubble so that t < c

t and A,

B, m are coefficients. If the exponent m is negative, )(ln t p is singular when

ct t and B > 0

ensuring that )(ln t p increases. If 0 < m < 1, )(ln t p is finite but its first derivative d )(ln t p / dt

is singular at ct and B < 0 ensuring that )(ln t p increases. Extension of this power law (1) takes

the form of log-periodic power law (LPPL) for the logarithm of price

)log(cos)()()(ln t t t t C t t B At p c

m

c

m

c, (2)

where is a phase constant and is the angular log-frequency. This first version (2) amounts

to assume that the potential correction or crash at the end of the bubble is proportional to the total

price [3]. In contrast, a second version assumes that the potential correction or crash at the end of

the bubble is proportional to the bubble part of the total price, that is to the total price minus the

fundamental price [3]. This gives the following price evolution:

)log(cos)()()( t t t t C t t B At p c

m

c

m

c. (3)




As explained in [13,6], we diagnose a bubble using these models by demonstrating a faster-

than-exponential increase of p( t), possibly decorated by log- periodic oscillations. …‖

Source: taken from Zhou, W., and D. Sornette, ―Is there a real-estate bubble in the US?‖, Physica A, Volume 361,Issue 1, February 2006, pp. 299-300.

Even though these equations were for a real estate bubble, they apply generally (also, see, e.g.,

Watanabe et al. (2007)). Essentially, almost irrespective of its fundamental value, any financial

asset price that grows at ever increasing rates is mathematically and physically unsustainable.

That is, and ignoring the deviation from fundamental value issue, if the rate of growth becomes

faster than an exponential increase in the price of the financial object of interest to us (e.g., a

stock, a stock market, a house, a real estate market, a commodity like oil, etc.), the probability of

a crash or correction toward fundamental value increases.210

The usefulness of this ―power law‖

approach is that prices can be viewed over time, and if the price begins to outpace its assumed

fundamental value, or even only its absolute value in this case, at an increasing speed it is likely

to crash, or at least stop growing.211 In short, although the formulas may not be obvious, the

results are intuitive.

A simplified application of the power law approach can be applied as follows:212

210 For more on the ―crash‖, see, e.g., Ferguson (1989) on the October 1987 probable dynamics, but note that thesetypes of ―crashes‖ are not the norm. Also, Andersen and Sornette (2004) have a useful method for using volatility tomeasure the likelihood of a ―fearful bubble‖ that is more likely to result in a dramatic movement down than a―fearless bubble‖ where volatility is little changed on the way to the peak.211 Again, we don‘t normally have an idea as to the actual fundamental value. Therefore, we will typically usemarket derived price change as our measure.212 I credit Mike Davis for suggesting this approach.




, where is the price of a financial asset at time t , is its starting price, and k is not

constant (k is what we are trying to measure to evaluate sustainability), and is an increasing

function of t , k (t ) where . Note that the time to double for a given k is log2/k .

The value k can be estimated by looking at a lagged time series. For example,

,

for a monthly series. If the values of k increase every month, then this is indicative of a bubble.

In short, increasing growth is possible (maybe not likely for financial asset prices, but possible),

but an increasing rate of growth every period is clearly unsustainable (actually physically

impossible in a finite world). Therefore, if the price of a financial asset in question continues to

increase at an increasing rate (i.e., k continues increasing), it is a likely bubble and it will

decrease or pop, it is only a question of when.213

The following graph represents one application of this approach to the month-end price of oil ($

price per barrel of West Texas-Okl. crude oil):

213 In addition, Andersen and Sornette (2004) developed an interesting approach where a series‘ stochasticity (andnonlinear feedback) is used to enhance identification of bubbles and to bifurcate categorization into ―fearful singular bubbles‖ (where volatility is generally increasing as the peak is approached) and those that are ―fearless‖ (wherefearlessness is reflected by no significant change in volatility, e.g., the NASDAQ bubble).




The red line is the monthly estimate for

, and the blue line is the six month rolling

percentage of months where k is increasing (called the ―bubble indicator‖). Therefore, if the

estimate for k increases each month for six months in a row, the ―bubble indicator is 100% (6/6),

if three months then 50% (3/6), etc. The only point where k increased for six consecutive months

was the six months ending June 1992. Therefore, that period could be defined as the front side of

a relatively short bubble in the price of West Texas crude oil. Also, as you can see, based on this

‗power law‘ derived definition there are several periods that could be defined as bubbles (e.g.,

one peaking around the end of May 2008).

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u b b l e

i n d i c a t o r

k

Applying the 'Power Law' to the Price of Oil

k

Bubble indicator




In addition, I tried the halving time as another ―bubble indicator‖ (again, the time to double for a

given k is log2/k ).

In this case I filtered the time it takes to double the price of oil to any positive value less than two

years (i.e., the red line). Where it spikes are periods when the price increases in oil are clearly

unsustainable. For example, at the end of May 2008, the price of West Texas crude oil was

doubling every year, clearly unsustainable.214

Indeed, the price peaked at around $140 per barrel

214 Of course, assuming monetary induced price inflation itself isn‘t running rampant.

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Applying the 'Power Law' to the Price of Oil - Halving Time

k

Time to double




at the end of June 2008 and bottomed out around $42 per barrel at the end of January 2008.

Again, this is just one way to identify unsustainable price series.

The primary purpose of this exercise is to show: (1) price bubbles can be mathematically defined

as a function of the rate of price increase, and (2) that definition can be used to detect clearly

unsustainable periods of price increase (e.g., depending on how stringent your ―bubble

indicator‖).215 That noted, even mathematical methods may have a large dose of art when applied

to bubble detection.

216

215 Watanabe et al. (2007, p. 120) actually claim that: ―It is shown that the whole period of a bubble or a crash can bedetermined purely from the past data, and the start of a bubble can be identified even before its burst.‖ Of course, if that is true, then identifying all three major phases of the bubble could be relatively straightforward.216 Of course, and as usual in finance, it would help to know the ‗pricing model‘ for the asset in question, but none isavailable (i.e., that is acceptable to the academic ‗profession‘). Therefore, the analysis here relies on the lack of sustainability of the overall price (i.e., not relative to fundamental or true value).




WHAT DOES A BUBBLE LOOK LIKE?

Source: Wikipedia – ―Anonymous 17th-century watercolor of the Semper Augustus, famous for being the mostexpensive tulip sold during tulip mania.‖

Behold the Semper Augustus! At the peak of the bubble/‖tulip mania‖, one Semper Augustus

bulb sold for a record price of 6,000 florins. This was the equivalent of forty years of income for

the average person at the time (i.e., at 150 florins per year).217

Although I like tulips, and

certainly a Semper Augustus is a fine looking tulip, would you pay essentially a life‘s earnings

for one bulb, and especially knowing that it might not even turn into a flower? Moreover, does

that seem like something that economists and/or finance types would call ‗rational‘?

The ―Tulip Bulb Mania‖ in the Netherlands in the early 1600s is considered by many historians

217 For another method of estimating tulip prices during the peak of that bubble, see Hirschey (1998).




to be the greatest market bubble of all times. Of course, such things depend on your definitions

and perspective, but it seems fair to say that it was a bubble (i.e., unsustainable).

In terms of sustainability, how did or does this U.S. residential housing price series look?

Source: Shiller, R., Irrational Exuberance (second edition), Princeton University Press, Princeton, N.J., 2006.

The chart ends approximately when the chairman of the Federal Reserve made his comments

about the lack of a national housing bubble (i.e., that it was ―unlikely‖), and it coincides

approximately with the bursting of that bubble. If anything, aside from being unprecedented, the




series represents anything but a sustainable looking series. Let‘s see whether incomes could

support it. Basic logic dictates that the average or median house price must be affordable for the

correspondingly average or median wage earner or the price is unsustainable.218

Thus, one rough

and basic measure of sustainability is price to earnings or cash flow. What follows is a graph of

median U.S. house price to median U.S. income.

218 Most appropriate would be the median income of the median homebuyer, but as a rough approximation we‘lllook at median wage earners and median price.

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Ratio of Median House Price to Median Household Income




As you can see, from the beginning to the end of the series the spike that began around 2000 has

no historical precedent in this series (1968 through 2007). The only other significant spike where

affordability (i.e., as defined in this way) was pushed was during the 1970s.219

Normally, the

ratio of price to income has been around 2.5 to 2.6. The rough rule of thumb (heuristic) is that if

the ratio is beyond 2 & ½ that buyers should be wary of purchasing a house. Also, this says

nothing about location specific affordability. During the U.S. real estate bubble this ratio was

over five times in certain areas of California, for example (even around ten times in some

locations).

Additionally, there has been large variation between countries. For example, contrast the

following ratio for the U.K. (although the numbers aren‘t exactly comparable) to the previous

U.S. graph:

219 I am also ignoring the likely case where incomes during the bubble period were inflated and thus unsustainablethemselves.




Source of graph: Nationwide.

In the U.K., the national market has been progressively driven by the London, England; therefore

London was well over five times earnings (actually well over seven). The U.S. residential real

estate bubble was almost contemporaneous with the U.K. real estate bubble and many others.

The degree to which prices bounded away from fundamental drivers of valuation depended on

the country and even the more local market, but bound they did in many areas to clearly

unsustainable levels. The question wasn‘t so much whether certain real estate markets

constituted a bubble, but exactly when they would burst.




Before proceeding, it is important to expand on the fairly general dynamics of the largely

unprecedented (in terms of scope and magnitude) real estate bubbles that occurred in the U.S., in

particular. Here are some of the more important drivers of that market during the buildup in

prices:

Supply & demand220

imbalances – Clearly, any bubble like the U.S. real estate bubble

that peaked out around the spring-summer of 2005 (i.e., nationally, as opposed to locally)

must have had general supply/demand imbalances (predominantly being relatively more

demand than supply in most geographic areas). The fundamental issue is what caused this

general imbalance over many years? One clear driver was that potential buyers kept

entering the market for homes. In particular, the median required income to purchase a

home generally kept going down with mortgage rates (and other drivers as well). Given

that mortgage rates were largely drive by Treasury rates, which in turn were largely

driven by monetary authorities generally pushing rates down over the period. Therefore,

ultimately, a primary cause, if not the primary cause, was Federal Reserve actions. For

example, a $100,000 fixed rate 30-year mortgage at 10% would require $877.57 per

month payments, whereas the same loan at 5% would require $536.82 in payments (an

approximately 39% reduction in monthly payments from the higher rate).221

In fact, the

30-year ―conventional mortgage rate‖ over the period January 1990 through December

2006 (the period encompassing the front-side of the bubble), as reported by the Board of

Governors of the Federal Reserve (the rate is entitled ―MORTG‖), peaked at 10.48% in

220 Normally as prices increase, demand decreases (ceteris paribus), but it would seem for bubbles especially, asprice increases demand seems to increase almost irrespective of changes in supply or no change n supply (i.e., theyappear as ―Giffen goods‖). 221 The calculation was done by the mortgage calculator at http://www.mortgage-calc.com/mortgage/simple_results.html.




May 1990 and bottomed at 5.23% in June 2003. Clearly, lower interest rates had a huge

impact on the supply/demand imbalances that generally resulted in unsustainable housing

prices. Specifically, lower interest rates make it possible for more demand, as well as

larger loans, ceteris paribus.

The terms of mortgage loans were relaxed – As the bubble progressed various loan terms

and types of loans generally encouraged more demand. For example, down payments

generally kept being reduced until they were, in some cases, eliminated altogether. Thus,

if a potential buyer is required to save $20,000 to make a 20% down payment for a

$100,000 loan; now assume only a 10% down payment on the same loan amount (i.e.,

$10,000); clearly, many more potential buyers would tend enter the market.222

Needless

to say, ―no money down‖ loans opened the set of potential homebuyers to virtually

anyone able to breathe. Obviously, these sorts of inducements also had an impact on

speculation, and not just housing speculation. For example, with no money down why not

buy two or three houses? Why not purchase apartments or a shopping mall? As one can

imagine the combination of lower payments (and in some cases no payments, e.g.,

―negative amortizing‖ loans) and relaxed loans terms meant that demand had almost no

limits (i.e., as long as prices were generally perceived to be increasing).

Deferred payments – As the bubble progressed, many new types of loans were

introduced. Not only did payments generally come down, but for some loans the

payments were deferred. For example, for some loans, especially popular near the peak,

debtors didn‘t have to make any payments until two years later. Thus, one could

222 In addition, if prices head south, they are more likely to walk away. This is a current problem as many financialinstitutions are seem somewhat perplexed by the, at least at the time of this writing, high level of defaults they arefacing.




theoretically get a loan where no payment of any kind was required for several years.

Talk about encouraging speculation; if the price goes up sufficiently you make the

payment(s), otherwise default.

I could go on, but as this point it seems clear that the market for residential real estate, and even

the more commercial real estate markets, was driven by a combination of things, but the

common thread was decreasing rates and terms.

Beyond the aforementioned U.S. real estate market dynamics are a host of other more specific

dynamics that were either exacerbated by CB or other government actions, and/or increasing

prices themselves, and/or momentum built up from the first two. Specifically, these contributing

causes impacted residential real estate loans themselves (this is not meant to be an exhaustive

list, but is intended to give an indication of some of the more important specific dynamics that

exacerbated, if not partially caused, the bubble):223

(1) Around 1996 Fannie Mae and Freddie Mac (two quasi-government agencies at the time)

began to push affordable low income loans. They would in fact begin to securitize these

loans (i.e., re-packaging them in pooled structures). Given the nature of geographic

differences in pricing, certain localities (and in some cases much of certain states) would

not qualify for this treatment because the loan amount would typically be over the

―conforming limit‖ (i.e., the maximum amount allowed to be called ―affordable‖). As the

bubble progressed, more and more loans would not be able to meet the ―conforming

limit‖, because generally rising prices drove a higher and higher percentage into the

―nonconforming‖ category.

223 This discussion is primarily based on information provided to me by Mike Davis.




(2) Those loans were primarily funded by banks and Savings & Loans ((―S&Ls‖) – as are

most residential loans in the U.S.). In addition, banks could set up entities that qualified

them to borrow money from the Federal Home Loan Bank system (i.e., banks could

borrow from the ―FHLB‖ system, which was originally established for S&Ls only). All

these types of loans used a standardized Housing and Urban Development (―HUD‖ – a

federal government agency) application form. Thus, there were uniform national

guidelines for lending. This was a result of at least two things. First, the banks held these

assets on their books (i.e., they are stated on their balance sheets). The banks would look

at income, job stability, assets, a property appraisal, credit outstanding, and use their own

credit scoring guidelines. Second, there were regulations against discrimination and

―redlining‖ (the alleged practice of discriminating against certain groups for non-

economic reasons, which turned out to be wrong).224

(3) As a result of low nominal interest rates on Treasury and corporate bonds, there was an

increased demand for investment grade fixed-income products. Wall Street rose to the

occasion by providing a stream of securitized non-conforming mortgage backed

securities. Now, the loan originators no longer held the loans in their own portfolios.

Therefore, they had no incentive to do anything more than provide the rating agencies

with the information to score these loans. In many cases this was a standardized credit

score using Fair Isaac COrporation (―FICO‖ – a private rating agency for rating

individuals) and typically a formulaic appraisal (e.g., typically using the Case-Shiller-

224 Although, this government driven mistake was clearly a major contributing factor to the bubble and subsequentcrash, I will focus on a bit more general dynamics.




Weiss resale index – ―CSW‖ – a housing price index).225

Hence the term ―NINJA loans‖

(i.e., No Income, No Job and no Assets) was born during this period. CSW gives a single

number for a zip-code (a general U.S. geographic reference for the postal service) and

does not take into account the individual characteristics and location of the property.

(4) The market did adjust to consumer behavior. With virtually zero cost financing (and in

some cases literally zero cost financing), lenders were anxious to hang on to their

borrowers. Prepayment was seen as the major driver of profitability. As house prices kept

rising and interest rate kept falling, increasing numbers of people with good credit would

do a ―cash-out refi‖ (i.e., they would borrow more money on the same mortgaged

property) at almost every opportunity. Mortgage brokers would call them at regular

intervals. ―Low-doc‖ (i.e., low documentation) was often the key to getting paid a

commission. The brokers steered their customers to the easiest lenders. Not only were

monthly payments lowered by interest only loans, but by negative amortization loans.

Down payments dropped from the customary 20% to get a good rate, to 10% then to 5%

then to 0%. In some cases, you could even borrow more that the property was worth! The

rationale was that if you don‘t like the loan-to-value ratio, wait a year! After all,

everybody knows that property prices increase by at least 5% a year, and 1% a month in a

―good‖ economy like California.

(5) The final irony occurred when Fannie and Freddie started using their own capital to buy

the AAA rated tranches off of other people‘s securitized ―jumbo‖ mortgages. That is, we

225 CSW gives a single number for a zip-code (a general U.S. geographic reference for the postal service) and doesnot take into account the individual characteristics and location of the property. However, they probably did as gooda job as a drive-by appraisal did during that time. The most accurate would be a local real estate broker‘s opinion,since they know all the quirks and trends of the local market. However, using a broker would be a conflict of interest. Therefore, independent appraisers are usually used for a full appraisal.




have come full circle in that the government agencies that originally ignored

nonconforming loans (i.e., ―jumbo‖ mortgages are mortgages that are nonconforming to

original Fannie and Freddie size limitations) ended up being the primary buyers of those

loans, and, in addition, buying them at the highest credit rating (amazingly, and

somewhat ironically, much higher than they could have achieved at the beginning of the

process just detailed).

So there you have it, a cycle that began with nominal mortgage rates above 10%, banks and

S&Ls holding real estate loans on their balance sheets, 20% typical down payments, and other

practices of limiting downside to the lender; then it mutated in fits and starts to one where most

institutions making the loans no longer expected to hold the loans on their balance sheets, rates

kept generally going down, down payments became unusual, NINJA loans, cash refis, etc. In

short, we went from somewhat limited credit to a situation where essentially breathing got you a

loan. The effect on demand and prices over time seemed almost constant, that is, until the

summer of 2007 when it began to be apparent that not only couldn‘t demand be dramatically

increased anymore, but the odds of many debtors paying back their loans was lower than

expected and getting lower with time.

That gives us some insight into the general and somewhat more specific dynamics of the U.S.

real estate bubble that is still unwinding. What about other famous bubbles? Some of the more

famous bubbles have been:




Source: Wikipedia, 03-30-2009.

Based on our definition, it is important to remember that a bubble doesn‘t need to be a class of

assets, collectibles, etc., that it can be anything where the price inflation is unsustainable.

Therefore, by our definition, there have been many, many more bubbles than shown by the

Examples of economic bubbles include:

Tulip mania (top 1637)

The South Sea Company (1720) Mississippi Company (1720)

Railway Mania (1840s)

Florida speculative building bubble (1926)

1920s American Economic Bubble (circa 1922-1929)

The Nifty Fifty American stocks of the late 1960s and early 1970s

Poseidon bubble (1970)

Sports cards and comic books in the 1980s and early 1990s

TY Beanie Babies (1996)

The Dot-com bubble (circa 1995 – 2001)

Japanese asset price bubble (1980s)

1997 Asian Financial Crisis (1997) Real estate bubble

o British property bubble (as of 2006)

o Irish property bubble (as of 2006)

o United States housing bubble (as of 2007)

(The former Florida swampland real estate bubble)

o Spanish property bubble (as of 2006)

o China stock and property bubble (as of 2007)

o Romanian property bubble (as of 2008)

Commodity bubble (As of 2008)

Exotic Livestock production in North America (i.e. llamas, white tail deer, elk , wild boar,

and to a lesser extent bison)[citation needed ]

Other goods which have produced bubbles include postage stamps and coin collecting.




preceding table. In fact, as of this writing there are many aggregate asset bubbles occurring (e.g.,

Chinese property markets, U.S. government debt, etc.), and even more individual ones.226

Particularly with respect to those that take some time to build up227, one seemingly odd property

of bubbles is that they seem to display a relatively consistent pattern. This typical pattern seems

in direct contradiction to EMT proponents‘ arguments. Specifically, if a popped price tends to

take roughly (actually very roughly) about as much time to return to fundamentally based levels

than it took to drift away from fundamental or intrinsic value, in the traditional sense of

arbitrage, then it should be arbitrageable. And if it‘s arbitrageable, why does it normally take so

long to drift back to fundamental or intrinsic value? A case in point is the now burst U.S. real

estate bubble. U.S. real estate prices should take at least until 2012 until they return roughly to a

value more in line with at least fundamental reality. Surely, rational arbitrageurs know this and

will speed the process up? Furthermore, to the extent academics (e.g., Greenspan) claim that a

bubble (i.e., assuming they could be proven to exist at all) can be identified, it is only after it has

burst. If that were true, and it was also true that many (if not most) are relatively symmetric

about the bursting point, then a rational arbitrageur might be reluctant to short on the way up,

because of identification problems228

, would certainly do so on the way down (i.e., after it was

226

In the extreme one could define even a single day IPO as a bubble. For example, Lowry and Schwert (2002)define individual company mispricings on the day of issuance as bubbles.227 It seems that it is more typical, for example, in laboratory settings for a ―bubble-and-crash‖ pattern to occur. See,for example Caginalp et al. (2000) where they additionally note that this pattern seems ―robust‖ to a number of variables (brokerage fees, short-selling constraints, etc.).228 Of course, by a similar rationale, at some point during the period when price was moving away from trueeconomic value one would expect that some more rational traders would be able to profit arbitraging by going longon the upward portion of the bubble process. In fact, this seems to be what happens (as we will see at the end of thischapter). Therefore, instead of arbitrageurs correcting mispricing they will tend to encourage it (i.e., by going longonce the price has clearly entered a bubble cycle, and possibly short once the peak has clearly been reached, and




more clearly identified as a bubble). Yet if that were true, then why the roughly symmetric

pattern that shows up again and again?

As an example of a classic bubble pattern (i.e., to the extent a general form or shape, if not

pattern, can be identified), here is a South Sea Bubble229 graph:

assuming they can). If this indeed happens, which is likely, it is the opposite of the rational arbitrageur story givenby EMT proponents.229 From Wikipedia April 1, 2009: ―The South Sea Company was a British joint stock company that traded inSouth America during the 18th century. Founded in 1711, the company was granted a monopoly to trade in Spain'sSouth American colonies as part of a treaty during the War of Spanish Succession. In return, the company assumedthe national debt England had incurred during the war. Speculation in the company's stock led to a great economicbubble known as the South Sea Bubble in 1720, which caused financial ruin for many. In spite of this it wasrestructured and continued to operate for more than a century after the Bubble.‖

http://en.wikipedia.org/wiki/Kingdom_of_Great_Britain


http://en.wikipedia.org/wiki/Joint_stock_company



http://en.wikipedia.org/wiki/South_America


http://en.wikipedia.org/wiki/Spain



http://en.wikipedia.org/wiki/Spanish_colonization_of_the_Americas


http://en.wikipedia.org/wiki/War_of_Spanish_Succession



http://en.wikipedia.org/wiki/Speculation



http://en.wikipedia.org/wiki/Economic_bubble
















Source: Wikipedia, 04-01-2009, attributed to Larry Neil.

Clearly, this seems a relatively, albeit rough, symmetrical pattern. Now what about the U.S.

residential real estate bubble?

Source: Wikipedia, 04-01-2009.

It likely won‘t be perfectly symmetrical, but it seems to be pointing to a return to more

fundamentally based levels around 2012 or beyond (i.e., three or so years from now). Again, I

ask is there a classic bubble pattern? There probably isn‘t an exact repeatable pattern to every




bubble, but there may be a somewhat systematic and more realistic psychological sequence of

events or pattern.

I like Nov and Nov‘s (2008) approach and most general conceptualization of bubbles (whether or

not financial, or economic, in nature):

Source: Nov, Y., and O. Nov, ―Living in a bubble? Toward a unified bubble theory‖, International Journal of General Systems, Volume 37, Issue 5, October 2008, p. 629.

Of course, again, the problem here is that most financial economists and economists admit to no

readily definable fundamental pricing ‗model‘. Therefore, we rarely, if ever, know when prices

Object’s Driving Value Object’s External Value

Behavior

The Information Feedback Loop Generating Bubbles




deviate from fundamental value. That noted, it is the general process that is intuitively appealing,

for example:

―A bubble develops when a positive feedback loop is formed between an object‘s external and

driving values, mediated by people‘s behaviour and accompanied by little or no change to its

fundamental value (see ‗The Information Feedback Loop Generating Bubbles‘ diagram). The

tulip example definitely demonstrated this pattern: people‘s demand for tulip bulbs (behaviour)

caused the bulb prices (external value) to soar; the growing prices caused the bulbs to seem more

attractive as an investment (driving value), generating further demand, and so on. Crucially,

while this loop was taking place, the pleasure the tulips gave people while looking at them

(fundamental value) was not changing.‖

Nov and Nov (2008, p. 629)

Alternatively, but still mostly in line with this general approach, is the time line of the bubble, as

the following example provides:




Source: http://www.oftwominds.com/blogapr09/housing04-09.html?ref=patrick.net, April 20, 2009.

It is the interplay between group psychology and the collapse (whether fast, slow or moderate)

that can be horribly fascinating. Think framing, but imagine the frame changes as the price

corrects toward fundamental value and beyond (what follows is a somewhat stylized version).

The sequence presented is somewhat of a restatement of the sequence and description made on

the oftwominds.com website, with my own behavioral finance bias.

The model of a financial bubble should be generic, that is, it should apply to all bubbles

regardless of whether a security or asset class, or the time period/era. The basic assumption

underling a theory of financial bubbles is that predicting the precise timing of the peak may be

difficult, if not impossible (and probably not for reasons given by central bankers), but bubbles

tend to follow a similar pattern because they are likely largely driven by psychology and limits to

arbitrage. The general four stages are as follows:

http://www.oftwominds.com/blogapr09/housing04-09.html?ref=patrick.net







1. The front side of the bubble & building euphoria – price(s) increases at times relatively

steeply, and many believe there is ―no end to the trend‖.230 Along the way, more and more

irrational (and possibly rational)231

traders jump on board and create a generally sustained

imbalance between supply and demand (i.e., on the side of demand).232 Classically, the

psychological ―positive feedback‖ mechanism is critical (see, e.g., Shiller (2002)).

2. Peak – Most arbitrageurs have given up trying to push price(s) back toward fundamental

value(s) and/or have joined the irrational traders, and the irrational traders themselves mentally

strongly believe there is no reason why price(s) cannot continue to increase. What often causes

the peak is that price(s) are shown to be unsustainable. This is the cognitive equivalent of yelling

―fire!‖233For example, in the current financial crisis the peak happened around the last week of

July 2007 when the market for certain U.S. residential mortgages hit a wall (i.e., it was

recognized that the large swaths of loans would not be paid back, and, therefore the values of

packaged mortgage products were physically unsustainable). After the peak, the rational

arbitrageurs begin to either remove themselves from supporting price increases or begin placing

230 To complicate matters, investors tend to expect a reversal after any run-up or rundown in prices (see, e.g., Shilleret al. (1991), Shiller (1990), and Shiller (1999)). Therefore, unless short and idiosyncratic, it is predictable that bubbles don‘t increase or decrease in a straight line. 231 In reality, there is a continuum between rational and irrational traders.232

This isn‘t to say that investors‘/speculators‘ expectations don‘t change as the bubble builds. For example, Shiller(1999) showed that, based on periodic surveys during the buildup phase of the last major U.S. stock bubble (thesurvey covered 1989 through 1998, and the market peaked around March 2000), investor expectations (bothindividual and institutional) changed significantly as the bubble progressed. In a sense, the expectations need tochange in order for those agents/actors involved to continue to push prices ever higher (i.e., if they thought priceshad peaked they might be inclined to sell).233 One classic anecdotal story is about how Joe Kennedy made a timely exit from the stock market before the Crashof 1929 after a shoeshine boy gave him some stock tips. Essentially, when the bottom rungs of the economic laddershow an overwhelming interest in a bubble market, then the base of the pyramid has expanded as far as it can possibly go and is ripe for a collapse. In short, it is a sign there are no more ―suckers‖ to be found.




downward pressure on prices by shorting, for example. On the other hand, most irrational traders

do not stop believing in the upward trend (i.e., even when it stops).234

3. Back side of the bubble – Anchoring & bouncing interacting with the ‗disposition effect‘ –

Irrational traders will use almost any excuse to re-inflate the bubble, but especially important is

the relative price they bought in. For example, as long as the price is above their buy in price,

they will tend to be net buyers (i.e., as a group), but as prices drift or bounce below the price they

bought in, they tend to be holders. The pattern on the backside of a bubble, especially for asset

classes, tends to follow a more erratic pattern (i.e., relative to the front side of the bubble, and

with the possible exception of individual securities, commodities, etc.). Thus, there are periods,

sometimes extending up to a year or even more where a re-inflation appears possible to mostly

irrational traders. In essence, there is now a tension between supply and demand as rational

arbitrageurs and irrational traders ebb and flow, while fundamentals try to assert themselves

(with varying degrees of success). But things like anchoring and ―playing with the house‘s

money‖ tend to assert themselves much more so on the way down than on the way up (i.e., by

definition). For example, currently this is the situation for the ―global financial crisis‖.

Fundamentally huge amounts of debt will be written off, but they have yet to be. As the

market(s) recognize this prices of those financial assets will increase or decrease, but

fundamentally they will be forced to generally decrease and many, if not most, irrational traders

will hold well past the point they bought in.

234 One curiosity is why not more outright crashes? For example, why don‘t more suddenly realize the game is overearlier? Even the crash of October 1987 required an ―unusual confluence of events‖ (see Wigmore (1998, p. 47)). Inaddition, Shiller (1987) found that investors generally believed the stock market was overvalued and that i t wasn‘tso much news that drove the selling pressure but their belief that other investors‘ psychology was paramount (i.e.,not their own, because they generally felt they could intuitively predict the market). In short, investors expressedsomewhat complicated and contradictory views about the ―crash‖.




4. Fundamentals reassert themselves – At some point the fundamentals reassert themselves and,

subsequently supply and demand become more balanced. Most irrational traders that are still

holding are holding losing investments and more rational traders are no longer overly influenced

by the threat of waves of irrational traders buying. The supposed truisms that fed the bubble and

even the earlier portion of post-bubble decline(s) and recovery(ies) are discredited. Cognitive

dissonance now rules supreme, and most of those who irrationally bought in believe they saw the

end (even if they are still holding their initial investments).

As a quick summary, a bubble has three critical parts: (1) front side, (2) peak, and (3) back side.

Essentially the process goes from a drift away from fundamentals and back again. It is

characterized by shifting supply and demand235, but mostly by generally demand imbalances on

the front side and a general lack thereof on the back side. Simple, but as usual, the ―devil is in the

details‖, and it tends to have links to limits to arbitrage and psychology.

Given the above generic description, it is unlikely that a decade long bubble will reach its bottom

where fundamentals reassert themselves in two days, let alone two years. It is more likely it will

take roughly ten years (thus roughly twenty years, or two decades from beginning to end).

Especially with respect to an asset class, a bubble is a process. So imagine what group

psychology it takes to pump it up and conversely how that must change over time to bring it

down. That is, it is likely that some of the same people who helped to inflate the bubble will

participate in deflating it, and this tends for drawn out processes to be a drawn out process.

235 Again, it is important to note that especially during the front side of a bubble, the object of the bubble tends todisplay the basic perverse ―Giffen good‖ attribute of increasing prices tending to increase demand (i.e., perverse bynormal standards and standard logic).




Mathematically driven economics has generally assumed that market partcipants have perfect

information and knowledge and act on that basis; yet we know that agents/actors do not possess

perfect knowledge/information, nor do they even act strictly ‘rationally‘ on the basis on what

little information and knowledge they do have. Accordingly, it has been pointed out (see, e.g.,

Hirschey (1998, p. 16)) that there is a cicular feedback loop between investors‘ perceptions of

the market(s) and market prices themselves. Hence, it is difficult to separate perceptions from

pricing, because agents/actors ‖cannot obtain perfect information of the markets because their

thinking is always affecting the market and the market is affecting their thinking.‖ Clearly this

issue is enhanced during bubbles, where feedback loops and circular reasoning affect the prices

and particpants at each step up and back down.

Regarding economic ‖models‖ in general, and his, in particular, Hayek may have had the best

answer to the form and substance of a prediction in economics:

‖ still adequately explained by my theory — but not adequately to the statisticians, because,

again, all I can explain is that a certain pattern will appear. I cannot specify how the

pattern will look in particular, because that would require much more information than

anyone has. So, again, I limit the possible achievement of economics to the explanation of a

type … Just as you have a formula for, say, a hyperbola; if you haven‘t got the constants set in,

you don‘t know what the shape of the hyperbola is — all you know is it‘s a hyperbola. So I can

say it will be a certain type of pattern, but what specific quantitative dimensions it will have, I




cannot predict, because for that I would have to have more information than anybody actually

has.‖236

Again, in finance (largely because it is a subset of economics), we can, at best, give a general

outline of the pattern, but specifics will tend to be lacking. Therefore, and especially for asset

class bubbles, the pattern will typically look like a triangle (i.e., somewhat rounded and spiky,

but a triangle nevertheless), but can certainly look rather jagged in both the upside and downside

from the peak, but especially on the downside as the process that built the price(s) up to

unsustainable levels unwinds itself.

Now that we have adequate working definitions of bubbles, have a sense of what they look like,

and even have a list of some of the more famous ones, the question should naturally arise as to

what might cause a price to increase at such a rate that it becomes unsustainable?

236 From http://hayekcenter.org/?p=882#more-882 on April 20, 2009. This is a quote from an interview withFriedrich Hayek on statistics and macroeconomics and the post was written by Greg Ransom on April 19, 2009 andposted under: Biography, Explanation, Probability Theory, Statistics The interviewer was Leo Rosen, and theinterview itself took place in 1978 ―in which Hayek presents some of his conclusions about the relationshipsbetween statistics, probability theory, and macroeconomic explanation.‖ At one point in the interview he points out that a primary issue in especially macroeconomics is that the ―law of large numbers‖ does not apply, while in say physics it can.

http://hayekcenter.org/?cat=20



















WHAT IS/ARE THE LIKELY CAUSE(S) OF FINANCIAL BUBBLES?

Unfortunately given the sorry state of macroeconomics, this is one of the more debatble topics in

this book. In the previous section we went from the most general to more specific, not so in this

section. My focus in this section is on cause or causes and will be more macro than micro. For

example, I am more concerned with the primary driver or drivers for asset class type bubbles

than more idiosycratic types (e.g., short bubbles in the price of oil or the share price of IBM

stock). Thus, although clearly the price of for example oil can have a macroeconomic impact, I

am more concerned with identifying the causes of those bubbles that are most likley to have

economywide impacts (including effects on more idiosyncratic things like oil, for example).

My reasoning for this focus is primarily because I do not wish to be sidetracked into writing

about the importance of, for example, the growing season for tulips in the Netherlands. My

attempt is to accept that in some more idiosyncratic cases (e.g., the price of IBM common stock

during the 1980s) idiosnycratic causes can dominate, but my concern are those more general

causes that are of concern across temporal and geographic coordinates. In short, I accept that, by

definition, idiosyncratic causes can be critical in some cases, but given that bubbles keep

popping up, there is likley to be one or more more universal causes.

Outside of the classic ‖Extraordinary Popular Delusions and the Madness of Crowds‖ by Charles

Mackay (first published in 1841), which is more of a straight history (i.e., noneconomic history)




of three financial bubbles and a sequence of seemingly unrelated ‖popular follies‖237, only two

relatively well known economists have directly taken up the task of trying to explain financial

bubbles: (1) Charles Kindleberger, and (2) Robert Shiller. Kindleberger is an economic historian,

and Shiller could be considered to be a financial behavioralist. Again, to date, academic articles

in the field that directly deal with finacial bubbles are rare (e.g., Bernanke and Gertler (1999)238),

or nonexistent. It should be noted that Kindleberger actually focused on ‖financial crises‖, not

bubbles per say, but many of these crisies were percipitated by the bursting of financial bubbles.

Kindleberger‘s thesis for causation can be reduced to the folowing (see Kindleberger (2005)):

easy money or credit especially and capital market institutions (and the interaction between of

the two) cause bubbles (which tend to turn into, i.e., after bursting, ‖financial crises).239 My

impression is that Kindleberger reduces the importance of human irrationality, because it may be

safe to assume that humans are not signifcantly more irrational now than they were during say

‖Tulipmania‖. The constant theme is expansionary or easy monetary policy and who and how

that easy money or liquidty impacts the market or markets in question.

‖The failure of banks, the overshooting and undershooting of exchange rates around their long-

run equilibrium values, and the bubbles in real estate and stock markets were systematically

237

The three financial bubbles covered are (1) ―the Mississippi Scheme‖, (2) ―the South-Sea Bubble‖, and (3) ―theTulipmania‖. These three are covered in the first 101 pages of a 740 page book that mostly dealt with subjects suchas alchymists, the Crusades, witches, etc.238 Even though the article is about financial asset bubbles they cannot bring themselves to use the word ―bubble‖ inthe title of the article. Bernanke and Gertler (1999, p. 24) define a bubble as a ―temporary deviation of asset pricesfrom fundamental values, for example, because of ‗bubbles‘ or ‗fads‘.‖ Thus, a ‗bubble‘ in their world is caused by abubble. Finally, the article is purely normative with no empirical data or analysis, but loaded with opinions on whatmakes good CB policy.239 Again, he posits that the general cause is: An accommodative monetary authority that in turn causes debt/credit tosurge (e.g., the South Sea Bubble in London, the Mississippi Bubble in Paris, etc., etc. …).




related and resulted from various shocks that led to changes in the scope and driection of cross-

border money flows.‖

Kindleberger (2005, p. 243)

Under ‖The causes of financial tumult‖, ‖The financial tumult since the early 1970s resulted

from the impacts of monetary shocks on the driection and scope of the flows across national

borders.‖

Kindleberger (2005, p. 244)

Kindleberger noted that in some cases it was ‖the relaxation or elimination of financial

regulations‖ that enahanced the impact of monetary policies (i.e., especially loose money or

credit). My interpretation is that loose monetary policy is a necessary and possibly suffcient

condition for a large asset class level bubble, but often some changes with respect to regulatory

changes interact to heighten the impact (i.e., as it runs through the affected institutions and

ultimately individuals). It seems that the interaction of, first and foremost, increasing money

supply, with credit & capital market conditions and reactions (including exchange rates, and

working through affected institutions and ultimately individuals) both feeds and causes bubbles

and is the popping mechanism (i.e., if money supply decreases and/or interest rates increase in a

way that appears to ‖shock‖ the market(s)). Also, there appears to be a sequencing where

economy wide bubbles often begin as stock bubbles and end two or so years later as real estate

bubbles (as financial capital runs away from a collapsing stock market and seeks shelter in other

assets, typically real estate, i.e., assuming the CB allows it to happen that way).240

240 This appears to even be the case in Holland after tulip bulb prices crashed.




For Shiller (at least with respect to the recent U.S. stock and real estate bubbles):

―some factors that have seemed, at certain times, to ‗explain‘ movements in the stock market,

notably long-term interest rates; these might help explain home prices as well. But one of the

first lessons of economics should be that there are many factors that seem sometimes to ‗explain‘

speculative price prices, too many for us to analyze comfortably. We have to resist the

temptation to oversimplify by singling out only one. Besides, long-term interest rates are really

exogenous factors. ... We have to try to understand the origins of market psychology itself.‖

Shiller (2005, pp. 31-32)

Firstly, I agree with both Shiller and Kindleberger, and also disagree with part of Shiller‘s

statement. I agree with Kindleberger that the empirical evidence and most plausible single cause

of larger asset class level bubbles is likely monetary in nature, but also agree with Shiller that

other factors surely must be involved (and possibly some on a case by case basis). Where I

vehemently disagree is the Shiller comment about interest rates being purely ―exogenous

factors‖, especially under the current monetary and currency regime, but I will deal more directly

with this critical issue in another chapter.241

241 In fact, Shiller (1979) has shown his own comment to be incorrect (see, e.g., Shiller (1979)). Actually, it is evenworse, he has shown that it is highly probable that the U.S. central bank endogenously influences not only nominalrates, but real rates as well. Furthermore, it doesn‘t take exogenous influences to create a bubble (see e.g., Smith etal. (1988)).




With respect to Shiller‘s other factors242, he lists the following twelve as his short list (i.e.,

specifically with respect to the recent U.S. stock market and real estate bubbles) (see Shiller

(2005, pp. 33-55)):

1. The capitalist explosion and the ―ownership society‖ (speculative activity is viewed

generally positively).

2. Cultural and political changes favoring business success.

3. New information technology impresses people as never before (e.g., the Internet, cell

phones, etc.).

4. Supportive monetary policy and the ―Greenspan put‖. Obviously this may be the key

factor. The ―Greenspan put‖ is reference to the fact that the U.S. central bank

systematically lowered interest rates (increased money supply) whenever financial asset

prices seemed to falter. Therefore, it is an extreme form of monetary easing.

5. The ―baby boom‖ and bust and their perceived effects on the markets. The ―baby boom‖

is a reference to a U.S. demographic bulge that occurred after the end of WWII. Common

wisdom has it that that group had altered demand for stocks and real estate in such a way

that encouraged higher prices, and when they retire it may cause the reverse.

6. An expansion in media reporting of business news. Given that humans do respond to

saliency and there was more reporting of business news (and positively biased reporting),

it is likely this helped inflate prices.

7. Analysts‘ optimistic forecast. Shiller is referring to earnings analysts. He specifically

notes that at the peak of the U.S. stock bubble (see Shiller (2005, pp. 44-45)) out of about

242 He places more emphasis on the ―herd‖ aspects and social psychology, feedback loops, etc.




6,000 stocks around 1% were ―sell‖ recommendations, 69.5% ―buys‖, and 29.9%

―holds‖. Ten years before the fraction of ―sells‖ was nine times higher. Therefore,

analysts were always very biased against making ―sell‖ recommendations, but that bias

moved dramatically in the biased direction as the bubble progressed.

8. The expansion of defined contribution plans. Again, like the ―boomers‖, this is an

asymmetric demand side boost argument that as retirement plans increased, demand

increased for financial assets, especially in this case stocks.

9. The growth of mutual funds. Even though mutual funds had been around since 1924, they

seemed to grow as the market grew, and people seemed to forget the conflicts of interest

inherent in such structures.

10. The decline of inflation and the effects of money illusion. People are often confused and

conflicted over inflation. In addition, the reported Consumer Price Index (―CPI‖) doesn‘t

effectively pick up asset price inflation, thus confusing matters and average perceptions

even more.

11. Expansion of the volume of trade: discount brokers, ―day traders‖, and twenty-four-hour

trading. Essentially it became easier to trade.

12. The rise of gambling opportunities.

Even though the list applies to a geographic specific asset bubble in stocks and real estate, it‘s a

good general list (although, it would require some adjustments for other assets and time

periods/eras, e.g., the growth of mutual funds probably wouldn‘t probably matter much to an oil

price bubble; and recent modern technology wouldn‘t matter for ―Tulip mania‖, etc.).

Regardless, note that many of Shiller‘s factors still lead us back to loose monetary policy (e.g.,




the ―Greenspan put‖, mutual fund growth, retirement plan growth, inflation illusion, etc. feed

back into money supply growth and/or are caused directly by it).

Shiller also stresses the process nature of bubbles. He lists off several ―amplification

mechanisms‖: (1) ―Ponzi‖ processes, (2) feedback243, and (3) outright fraud. Try to keep in mind

that speculation is a symptom and not the cause of bubbles. Agents/actors drive the price up

largely in search of a kind of irrational arbitrage (which seems an oxymoron, but it nevertheless

seems to be the case). The ―Ponzi scheme‖ or ―pyramid scheme‖ nature is a reference to the factthat the front side of bubbles tend to use current profits to create expected future profits.244

Clearly, it is likely that feedback (e.g., between bubble investors) and outright fraud (e.g., it is

physically impossible to have infinite price growth in a finite entity like a company or even an

economy) interact with these irrational expectations to keep the game going.

Clearly, bubbles can be rather complicated phenomenon. The combined process of buildup,

peak, and breakdown can be confusing and dependent on a host of factors. Although each case

can be argued to depend on many things, I find that the single greatest cause, especially of asset

class level bubbles like stocks and real estate recently, is monetary in nature (money supply or

credit245

).

243 I would include in this things such as social conformity and fads & fashions, as well as ―informational cascades‖(see, e.g., Bikhchandani et al. (1992)). In addition, even experimental settings generate bubbles (e.g., Smith et al.1988)).244 This also works well with the ―greater fool‖ theory. That is, often people rationalize overpaying f or somethingbecause they are convinced they will be able to sell to someone else at a greater price than their purchase price (i.e.,the ―greater fool‖). 245 Obviously, leverage can be part of the credit portion of the causal equation. In addition, it is hard to dismissMinsky‘s three finance unit classifications as being unimportant, especially as they relate to economy wide asset bubbles: (1) ―Hedge financial units‖ are the most stable and self -funding. (2) ―Speculative finance units‖ are capable




As mentioned, mainstream economics, particularly macroeconomics largely ignores bubbles.

Although, there is one school that does seem to have both theory and predictive content that

seems to match well with economy wide bubbles, it‘s called the ‗Austrian school‘ of economics.

Even though ‗Austrians‘ have been ignored or marginalized by the academic profession, they

seem to outline the primary cause of economy wide asset class bubbles quit well, and it works

well with Kindleberger‘s thesis.

The ‗Austrians‘ take on bubbles is as follows:

―Austrian economists focus on the amplifying, ‗wave-like‘ effects of the credit cycle as the

primary cause of most business cycles. Austrian economists assert that inherently damaging and

ineffective central bank policies are the predominant cause of most business cycles, as they

tend to set ‗artificial‘ interest rates too low for too long, resulting in excessive credit creation,

speculative ‗bubbles‘ and ‗artificially‘ low savings.246

According to the Austrian business cycle theory, the business cycle unfolds in the following way.

Low interest rates tend to stimulate borrowing from the banking system. This expansion of

credit causes an expansion of the supply of money, through the money creation process in a

fractional reserve banking system. This in turn leads to an unsustainable ‗monetary boom‘

of funding running or interest payments, but not principal; and thus are less stable than ‗hedge financing units‖. (3)―‘Ponzi‘ units‖ are incapable of paying principal or interest payments; and thus are the least stable of the three.Therefore, one can see the usefulness of monitoring these respective groupings as an economy wide bubble progresses toward a peak (i.e., as the ―speculative‖ and ‗Ponzi‘ units grow relative to the ―hedge units‖ we areclearly nearing the peak – i.e., the ―Minsky moment‖). 246 For example, interest rates matter, and shouldn‘t be pushed below what the market(s) would set. If they are, thenit will result in the misallocation of capital, which they call ―malinvestment‖. After a period of ―malinvestment‖, theeconomy will need to be restructured in such a way that investment will need to be reallocated away from themalinvestments, not toward them.




during which the ‗artificially stimulated‘ borrowing seeks out diminishing investment

opportunities. This boom results in widespread malinvestments, causing capital resources to be

misallocated into areas which would not attract investment if the money supply remained stable.

The global economic crisis of 2008 represents, according to some pundits, an example of the

Austrian business cycle theory's dependability.‖ (See Wikipedia)

Additionally, it isn‘t that I think that psychology and limits to arbitrage are unimportant with

respect to bubbles, they are. It is somewhat tautological to note that, however the psychology and

limits to arbitrage play out, the larger causal connection seems to run through easy credit and

financial intermediaries themselves (and associated regulations, etc.). Therefore, the makings

that largely cause the macroeconomic bubble in the first place need to be there in order for the

limits to arbitrage and psychology to matter. For example, without the ability for a financial

institution (government, private, or mix) or individual to lever up 10 to 1, 20 to 1, etc., there

wouldn‘t necessarily be as large a deviation from fundamental value in the first place. Thus,

psychology and limits to arbitrage are critical, but they are attached to the bubble as necessary

but not sufficient conditions (again, in the case of a more macroeconomic bubble), whereas the

monetary policy piece is required for things to truly get out of hand on a macro scale.247

247

The Economist magazine just previous to the U.S. stock market peak (September 23, 1999) had an article arguingthat ―pricking‖ asset bubbles is an essential job of central banks. This, of course, begs the question of who actuallycreated the bubble(s). Therefore, if the central bank was largely responsible, or a large contributor, why would theydestroy exactly that which they created, or helped to create?Of course CBs, or at least CB economists in the last few decades have tended to indicate that CBs believe that theyshould not pop bubbles unless certain conditions are met that are unlikely to be met, e.g., two conditions are met: (1)only if the asset boom is driven only by non-fundamentals, and (2) they know exactly when it will burst (seeBernanke and Gertler (2001) on this one). Of course, much like ―global warming‖ arguments these CB bubblerelated arguments and beliefs are driven almost soley by ‗models‘ of their own construction. Thus, actual evidence,at best, has a secondary role. Although, basic logic would suggest that if a bubble is bad, one twice as big would be




Again, besides relatively accurately describing what happened in the latest U.S. stock and real

estate bubbles, the Austrian theory seems to overlap with Kindleberger‘s research and thoughts.

Regardless, that which is fundamentally unsustainable will not be sustained, and eventually will

end, by definition. In turn, when a bubble ends it tends to have considerable negative

consequences, especially for economy wide bubbles.

BURSTING EFFECTS OF FINANCIAL BUBBLES (ESPECIALLY MACROECONOMIC

ONES)

What happens after a bubble bursts?248

Consider this, if central bankers receive a

disproportionate share of the blame for causing them, then they must have good reason for aiding

at least twice as bad. Furthermore, if bubbles are bad for the economy on balance, then popping one would be agood policy; although I would think it might be easier just not to fuel the bubble in the first place.248 Note that even standard normative economic models can show that bubbles are destructive to the overalleconomy (e.g., Wang and Wen‘s (2009) ―general equilibrium model with ―speculative bubbles‖).




and abetting them as they grow, or do they?249

They must judge the benefits to outweigh the

costs (i.e., assuming there are tangible benefits and costs), or do they? More specifically, is the

size of a bubble indicative of the size of its economic impact? If it is, then why do central

bankers allow it in the first place?250 Anyways we have several questions and some descriptive

work to analyze, but let‘s begin with some more insight from those crazy ‗Austrians‘251.

―There is no means of avoiding the final collapse of a boom brought about by credit

expansion. The alternative is only whether the crisis should come sooner as a result of a

voluntary abandonment of further credit expansion, or later as a final and total

catastrophe of the currency system involved. …

The credit expansion boom is built on the sands of banknotes and deposits. It must collapse.‖

249 See, e.g., Bernanke and Kuttner (2005) for an empirical estimate of the stock market impact of ―unexpected‖changes in the Federal Funds rate on U.S. stock prices. Their estimate is a 1% increase in stock prices for every25BPs reduction in the Fed Funds rate (this number does not include anticipated increases, by their ‗model‘ design).Furthermore, they find (i.e., based on their econometric ‗model‘) that it is excess returns not changes in the ―realrate‖ of interest that drive the result (they expected it would be the ―real rate‖ driving the result). Clearly, they didnot, and do not, understand exactly how and why monetary policy impacts financial asset prices. In fact, theyconclude by noting that it may be the case that the stock market overreacts to monetary policy (i.e., market―overreaction‖, see Bernanke and Kuttner (2005, p. 1254)). In summary, and ironically, their work would seem verysupports Austrian theory.250 Two Riksbank economists Dillen and Sellin (2003, p. 119) state that: ―Our main concern, however, is a central bank‘s approach to such price developments: should it try to identify and counter the bubble at an early stage or waituntil the bubble has burst before taking measures to limit its harmful effects? We consider that a largely preventivestrategy is ruled out by the lack of knowledge about how a price bubble can be countered with measures of monetarypolicy. Still, there are grounds for continuing to analyse financial asset markets and identifying different types of imbalances ...‖ Therefore they express the common opinion that central banks should just let them blow up, and thendo something. In addition, they indicate bubbles should be monitored? To me this seemingly common CB attitude

seems like cynical full-employment for CB economists. First, cause a problem, second monitor its growth, then stepback and wait for the blowup, whereupon you step in to clean it up and expand your mandate. Clearly, if these typesof bubbles are so bad and you can identify them, then logically you should burst them as soon as possible, yet this isthe opposite of what today‘s CBs seem to advocate. 251 The Austrians really are the only ‗economists‘ with a theory of cause and effect on this (at least as of today). Also, I would argue that Minsky (see Minsky (1993)) may also have a theory (that is coincidently also non-mathematically based), but I find his approach stresses more the increasing levels of implicit, if not outright, fraudthat seem to manifest themselves as the credit expansion runs out of places to stuff the credit. Hence, debt tends totake on an increasing resemblance to ―‘Ponzi‘ units‖ with increasingly smaller and smaller probability of interest, letalone principal, payments being made.




Ludwid von Mises

Needless to say, the Austrians are not big fans of the macro bubble, not to mention that

―collapse‖ is a strong word. They don‘t generally think they are a good idea because they cause

costly dislocations of resources (―malinvestments‖). Although a bit simplified, I would sum up

their motto on this as ―it‘s the credit/debt stupid‖. If they are correct, then minimally the U.S. is

in predictable economic trouble.

Figure 1: This chart compares total debt (or “credit”) in the U.S. to GDP (or Gross Domestic Product) on a

percentage basis. Source: Graph was taken from http://www.chrismartenson.com/blog/crisis-explained-one-chart-debt-gdp/11570(entitled Figure 1).Values from the Federal Reserve and do not include unfunded liabilities like Medicaid/Medicare and Social Security(also, exclude any shortfalls that may occur from overly opt imistic projection/assumptions, e.g., government orprivate pensions, state unemployment benefit shortfalls, etc.).




The last time the U.S. was carrying a debt load comparable to the current one (incidentally, that

is still growing) was during the early stages of the ―Great Depression‖. If the ‗Austrians‘ are

correct and debt/credit matters as much as they think it can, then the future looks bleak. For

example, Hebling et al. (2002, pp. 61, 74) state that average stock market asset bubble bursts

between 1960 and 2002 cost affected economies about 4% of GDP, and that comparable real

estate bubble bursts cost about 8% of GDP (i.e., they can be much worse, especially if real estate

displays the same peak to trough relative price drop as stocks). They also noted ―spillover‖across other asset classes, and a slowdown in investment growth. Given that the current real

estate and stock bubbles are unprecedented in scale (both relative, and, obviously, on an absolute

level), scope (I can‘t think of a country that hasn‘t been touched by them), and duration (i.e., the

length of price buildup), it is likely the costs will be commensurate with their size, scope, and

length.252

In fact, if one includes other liabilities, the numbers are much worse.

252 In Japan, after their massive financial bubbles and stocks and real estate burst, the debt levels assumed during the period are now credited with disastrous effects on businesses‘ employment demand, as well as significantly reducedfixed and R&D investments; for the individual it is credited with dramatically reduced consumption as well as adramatic shift in the makeup of consumption (see, e.g., Ogawa and Wen (2007)). Importantly, these effects appear tobe beyond the incremental addition of debt on the front side of the bubble. That is, and assuming it is viewed aspositive, whatever good happened on the front side was more than offset by what happened on the back side of thebubble. It is unlikely this will be significantly different in the U.S. or anywhere else that tries to use debt to pullforward consumption.




Source: Graphs were taken from the ―Grandfather Economic Report‖.

As can been inferred from the previous two graphs, once additional debt and the lack of savings

are factored in, it is worse than the previously presented graph . One rather ―Austrian‖ way to

look at this is something called the ―Marginal Productivity of Debt‖ (―MPD‖).253 In the U.S.,

prior to the 1970s an increase of $1 in debt likely saw a greater than $1 increase in income or

GDP. Shortly thereafter it went to less than 1 to 1, and more recently 5 or 6 to 1, and most

recently possibly negative.

253 The idea behind the MPD is that debt should generally be taken on for reasons at least related to being able to payit back, or it is unsustainable, by definition. For example, borrowing $1 million to set up a factory producing pingpong balls makes fundamental long-term economic sense as long as it can make enough to pay off all its expenses(including interest and principal on the debt). On the other hand, if one takes out a $1 million loan and subsequentlyuses it to buy expensive meals and other purely consumable goods and services for oneself, then one would notexpect that debt to be contributing to the productivity of society, and the overall MPD will go down.




―In actual fact, there used to be a very stable relationship between money or credit growth and

GDP or income growth until the early 1980s. Growth of aggregate outstanding indebtedness of

all nonfinancial borrowers … had narrowly hovered around $1.40 for each $1 of the economy‘s

gross national product. Debt growth of the financial sector was minimal.

The breakdown of this relationship started in the early 1980s. … But the most important

change definitely occurred in the link between money and credit growth to asset markets. Money

and credit began to pour into asset markets, boosting their prices, while the traditional inflation

rates of goods and services declined. …‖

Richebacher, March 20, 2007 (The Daily Reckoning)

Richebacher was noting that the MPD has been eroding, and around the time of the U.S. real

estate bubble peak has deteriorated even more dramatically. Also, this is shown in the following

two graphs:




Source: Graphs were taken from the ―Grandfather Economic Report‖.

The trend is clear. The first graph shows debt per unit of income, and the second graph shows the

reciprocal of that (e.g., we now need more than $5 debt per $1 of income). Therefore, around the

early 1980s the MPD deteriorated and has continued to do so. We must be careful though in our

interpretation, to some extent this may be misleading. That is, to the extent we implicitly and/or

explicitly assume there is a direct causal relationship only between these two variables (i.e.,

running from debt to income and/or GDP). In reality, it may end up effectively a two variable

race, but in the beginning it is clearly more than that (i.e., it may in the end be the case the

collapse is all about too much debt, but clearly the earlier success is not due to debt, but things

like entrepreneurs, real capital, useful education/real labor capital, etc.). Regardless, a MPD of

below one is problematic. Furthermore, it is clearly now unstable and this has recently become

self-reinforcing if it continues to climb (as it has over the last two or three decades).




Regarding the bursting effect, the reader may be asking themselves why something like the MPD

even matters. It matters to the extent that macroeconomic scale bubbles are merely a symptom of

a larger disease (e.g., as the ‗Austrians‘ seem to think). If indeed the 4% loss of GDP (in the case

of equity bubbles) and 8% loss of GDP (in the case of real estate bubbles) are indicative of much

smaller bubbles within a larger credit bubble, then the current unwinding will likely be multiples

of the approximate macroeconomic loss numbers quoted by Hebling et al. (2002). Thus, you

have a continuum of unsustainable bubbles: (1) idiosyncratic financial bubbles (e.g., IBM stock

in the 1980s) at one end, (2) asset class specific bubbles (e.g., Dot.com stocks during the 1990s,

and peaking March 2000), and (3) macroeconomic bubbles ala the ‗Austrian school‘ at the other

end. If the ‗Austrians‘ are largely correct, then it is likely that all three are influenced by easy

credit, but the last two are almost certainly largely caused by it. Finally, the costs and associated

effects are largely driven by what type of bubble it is and whether it is a bubble of a larger

macroeconomic credit cycle or not (mostly, if not totally caused by human intervention).

Therefore, the effects and associated costs of a bubble will have a heavy dependence on the type

of bubble. Is it an idiosyncratic bubble or a macroeconomic bubble? The more idiosyncratic, for

example the price of IBM common stock during a two year period in the 1980s, the smaller the

macroeconomic costs, by definition, yet its specific impact on IBM might be great. At the other

end of the bubble scale, an ‗Austrian‘ type CB enhanced credit cycle is, by definition, the most

costly event that can happen to society overall (i.e., outside of war). Kindleberger is worried

about ―financial crises‖ associated with the macroeconomic credit events, while the Austrians are




worried about the collapse.254

At the other extreme are people that jumped into an idiosyncratic

bubble at the peak and were forced to declare bankruptcy as a result. In either extreme you can

get insolvency for one or more individuals or for virtually the whole economy.

WHEN IS IT ‗RATIONAL‘ TO BE ‗IRRATIONAL‘?

―If you can keep your head while others are losing theirs, perhaps you have misjudged the

situation.‖255

Joker, The Short Timers (by Gustav Hasford)

254

Related to this is a quote from Ludwig von Mises: ―The boom produces impoverishment. But still moredisastrous are its moral ravages. It makes people despondent and dispirited. The more optimistic they were under theillusory prosperity of the boom, the greater is their despair and their feeling of frustration. The individual is alwaysready to ascribe his good luck to his own efficiency and to take it as a well-deserved reward for his talent,application, and probity. But reverses of fortune he always charges to other people, and most of all to the absurdityof social and political institutions. He does not blame the authorities for having fostered the boom. He reviles

them for the inevitable collapse. In the opinion of the public, more inflation and more credit expansion arethe only remedy against the evils which inflation and credit expansion have brought about.‖ 255 My intention was to make this quote more understandable by the end of this chapter, but it may take the wholechapter to accomplish that.




Before going full out on this topic, I would like to introduce an institutional agent/actor that I

might have introduced in the previous chapter, ―hedge funds‖, but held off on so doing. The

reason I waited until now is twofold: (1) that we do have some empirical work linking them to a

commonly accepted financial bubble, and (2) of all the agents/actors in the financial markets

they could be argued to have the best change of representing a close approximation to traditional

rational arbitrageurs. Therefore, we can analyze the impact of what are considered to be

arbitrageurs/hedgers at a market and time when we would most expect them to drive prices back

toward fundamental value. If it turns out that this group fails to correct mispricing during a

―bubble‖, the obvious next questions are who would be expected to do so and under what

conditions? Answers: With respect to who, likely nobody; and with respect to conditions, most

likely practically never, and likely nowhere.




OUR LAST BEST HOPE – HEDGE FUNDS (―HFS‖)256

Recall, especially from the previous chapter, that analysts, PMs, and individual investors, etc. all

seem to systematically fall prey to their very human biases and the classic rational trader doesn‘t

seem ready to fill the gap and correct prices for all asset classes at all times. In contrast to the

WSSs, PMs, analysts, etc. reviewed, HFs may be the one institutional investor that could fill the

role of rational arbitrageur.

The key to HFs is that their incentives differ from the majority of institutional investors (retail or

institutionally oriented). The following table highlights some differences on three levels: (1)

compensation, (2) investment flexibility, and (3) other.

From: Kao, D., ―Battle for Alphas: Hedge Funds versus Long-Only Portfolios‖, Financial Analysts Journal, Volume52, Number 2, March/April 2002, p.24.

As a general rule their compensation is more in line with their clients (although still somewhat

asymmetric), their IP can be more flexible, and other factors are in their favor for acting as an

arbitrageur/hedger. For example, HFs neither display the risk/reward profiles of mutual funds nor

256 I would also include in this group Commodity Trading Advisors (―CTAs‖), which could be another section.

Table 1. Structural Factors in Favor of Hedge FundsCompensation Investment Flexibility Other Factors

Management fees (not unique) Leverage Lockup period**

Incentive fees Short selling Nil disclosure requirements

Hurdle rates Use of derivatives Fund size

High-water marks* Concentrated positions Simple benchmarks

Management capital Few investment guidelines

* High-water marks are used to ensure that incentive fees are earned only if cumulative

performance recovers any past shortfalls.

** The lockup period is a time restriction for redeeming hedge-fund investments.




do they have similar incentives (coincidence?). Liang (1999) documents some of the more

noteworthy differences:

1. Hedge funds have a special fee structure that is designed to align managers‘ incentives

with their clients‘. For example, ―hurdle rates‖, incentive fees (averaged about 16%, and

the median was 20%), and ―high watermarks‖.

2. Funds with high watermarks significantly outperform those without high watermarks.

Also, typically, hurdle rates are combined with high watermarks.

3. The incentive fee structure does indeed align manager and client interests.

4. Average hedge fund returns are positively related to fund size/assets and lockup period,

and negatively related to fund age.257

5. Onshore funds with offshore equivalents outperform onshore only and offshore only

funds.258

6. Hedge funds have relatively low correlations with traditional asset classes, thus providing

diversification potential for asset allocation.

7. Overall, hedge funds provided superior performance when compared to mutual funds

(i.e., on a risk-adjusted basis), and this difference is not due to survivorship bias.259

Based on this evidence, and basic incentives logic, at least some of these agents/actors are the

most likely candidates to keep the market honest in a traditional market efficiency sense. In

257It is interesting to speculate that the fund age may be related to manager age for mutual funds. Remember from

Chevalier and Ellison (1998) that, ceteris paribus, you want a younger manager to manage your money; and for HFsthe age of the fund and manager age tend to be one in the same.258 This is probably a selection bias (i.e., if the fund has done well onshore, it is marketed offshore).259 For what it‘s worth, I am not completely convinced of the lack of relative survivorship bias in the HFs performance numbers relative to mutual funds. For example, there is a unique ‗self -delisting bias‘ among HFs, andCTAs (e.g., some very successful ones are not listed, others are yanked to avoid data errors, etc.).




addition, we know the other agents/actors don‘t seem to be doing the job, hence our ―last, best

hope‖.

Given this is the bubbles chapter, what, the reader might ask, do HFs have to do with bubbles?

This question brings us to the point of answering the question: When and under what

circumstances might it appear ‗rational‘ to act ‗irrational‘ (of course, I mean in the normative

sense of the words)? Answer: Possibly during a bubble?

Whether or not a bubble is the time to act rationally irrational minimally depends on the

following:

1) Your ability to evaluate the mispricing or more specifically the deviation from true economic

value (therefore, you need at least two models, the one used by those irrational enough to

misvalue, although there are probably many, and a model of the true economic or fundamental

value).260

2) Your ability to forecast when the irrationnal traders will no longer value according to their

irriational valuation model (the peak or turning point).

3) Also, probably, things like the distribution and impact of the various parts of market demand

and when they will remove themselves from the demand function as price begins to drift toward

fundamental value, etc.

260 Also, this implicitly assumes that fraud isn‘t influencing pricing or agents can correctly account for it and modifytheir pricing accordingly. For a discussion on one typical aspect of fraud in the financial markets (called―overpromotion‖ by the author) see Kane (2005).




This is likely not easy, nor is it assured to be risk-free (i.e., in the traditional finance sense). And,

as always, we have very few cases were we know the ―pricing model‖.261 Perhaps, an easier

method, and ignoring the pricing method for the moment, is to focus on cases where most of us

‗rational‘ finance people can agree the pricing was way out of line (we just don‘t know how far).

One case of this must be the technology craze or mania of the mid- to late-1990s (the ―Dot.com

boom‖, peaking around March 2000)262

. This affected not just pricing of U.S. technology shares,

but affected stocks markets internationally as well. Two academics looked into this very question

by analyzing hedge fund holdings of technology shares up to and through the peak of the

technology craze around March 2000.

―Overall, our findings cast doubt on the classical view that it is always optimal for rational

arbitragers to attack a bubble. While the exact implications of our results for the mechanism

limiting arbitrage may be open to different interpretations, one point seems clear: There is no

evidence that hedge funds as a whole exerted a correcting force on prices during the

technology bubble. Among the few large hedge funds that did, the manager with the least

exposure to technology stocks – Tiger Management – did not survive until the bubble burst. …

Nevertheless, we add much needed empirical evidence to the predominantly theoretical work on

limits of arbitrage.‖

261

The problem is that EMT/EMH proponents cannot admit that there are financial bubbles. If they do, the ― jig isup‖. In addition, but related, is how to define them. We must agree on what constitutes fundamental value, then wecan say that economic/fundamental values are diverging enough to say this or that is a ―bubble‖ (i.e., unless weapply an ―unsustainable‖ vs. ―sustainable‖ type definition). But, of course, the instant you do that, the ― jig is up‖ for EMT proponents. What a vicious circle for the field of finance. For example, things like ―time varying risk premiums‖ get you out of saying there is a hard and fast economic valuation (hence, EMT proponents not being tooconcerned that the CAPM is largely dead, since it made hard and fast interpretations of these things). Therefore, bymaking the right assumptions, you can get any price at any time (and we end up where we started, i.e., witheffectively no definition possible, so no possibility of being proved wrong, or right).262 See, for example, Cooper et al. (2001) as an example of this.




Brunnermeier and Nagel (2002, p. 3)

Here is a graphic representation of the above comment:

From: Brunnermeier, M., and S. Nagel, ―Arbitrage at its Limits: Hedge Funds and the Technology Bubble‖,Working Paper, November 2002, p. 41.

You can see that far from trying to prick the bubble, they contributed to it! As the NASDAQ (a

primarily technology stock oriented index) increased and peaked on March 2000, HFs roughly

mimicked its ascent with their holdings of technology stocks (the blue bars roughly represent the




weight of technology stocks in HFs‘ portfolios).263What the graph shows is how the weight of

technology shares in the ―market portfolio‖ (the relative market value weight of technology

shares over all measured shares in the CRSP database) roughly mimics the technology holdings

of HFs. Normative theory would suggest that if the prices of technology shares were drifting

away from fundamental values (as they were over most of the period shown, i.e., at least until

March 2000) then one or more arbitrageurs will enter to drive prices back to correct

valuations.264 Where is our ―rational‖ arbitrageur? In fact, we just eliminated our final private

institutional savior.

Note that besides the normal limits to arbitrage (transaction costs, no exact substitutes, etc.):

• Many/most institutional and retail, and even HF, PMs have short horizons due to the fact

that investors will pull money at some point and they know it (see the literature on

portfolio flows). Hence, bucking the trend of a bubble on the front side can be hazardous

if you don‘t get the timing right.

• Idiosyncratic risk also limits arbitrage (see Wurgler and Zhuravskaya (2002)). Also, if

you make an idiosyncratic trade during an asset class bubble, you may be overwhelmed

more by the systemic bubble risk 265

than the idiosyncratic risk.

263 Apparently, this is not influenced by a size effect, as, e.g., Schwert (2002) shows that during the technology stock

bubble the driving force weren‘t small firms in the NASDAQ index but technology itself. 264 It is likely that the drift from fundamental value was of historic or at least near historic proportions (see, e.g.,Hirschey (2001) where he compares ―NASDAQ 100‖ valuations at their peak to the ―Nifty Fifty‖ at their peak in1929). Specifically, the finding (see Hirschey (2001, p. 58)) of ―conventional expectations of 20-50 percent long-term EPS growth for giant tech companies‖ is not only unsustainable, but absurd. Therefore, if there was a time andasset group that arbitrageurs should have been shorting, but didn‘t, this was it.265 This type of risk is not necessarily of the systemic kind (see, e.g., Baker and Wurgler (2003) where they find that,for example, ‗sentiment‘s‘ effects on stock returns are not likely ―to reflect an alternative explanation based oncompensation for systematic risk.‖). Therefore, what I call ―bubble risk‖ is probably not a standard normativefinancial market ‗risk‘, that is, in the traditional sense of the term.




• It is risky to attach a bubble without coordination (e.g., Julian Robertson of Tiger

Management).266

The key is that if you have some timing ability (or perceive that you do) then it may or may not

be optimal to ride the bubble. In short, the strategic or long term bet may indicate the likelihood

that the bubble will burst, but it may make tactical sense to hang on and ride it for a time (i.e., in

the short-run). Either way, and ignored by the EMT, there are clearly times when it can be

―rational to be irrational‖267, and the technology bubble is one example of that (clearly, given the

mechanism uncovered in this example, there must be many others).

268

I will end the chapter with prescriptive advice with respect to investors and prospective investors

in the financial markets during bubbles:

• It is never a good idea to be irrational (i.e., in the dictionary sense, not the EMT sense).

Therefore, when I write there are times when it may be ―‘rational‘ to be ‗irrational‘‖, I

mean in the normative economics and finance sense of the terms.

• Be especially careful of bubbles, in that they can represent some of the more extreme

pricing deviations from true economic value. Thus, be especially wary of the peak. It is

the dividing line between when an investor, or potential investor, will switch from

favoring a net long to a net short position.

266 Even normative ‗models‘ can get the result that it makes sense to contribute to a bubble (e.g., Wang and Wen(2009), where the key assumption is merely heterogeneous agents).267 Even Dillen and Sellin (2003, p. 123) note: ―for the individual, it may not be irrational to invest in an asset with a price bubble.‖ 268 Thus, in those situations where a true ‗fad‘ is in play, it can be a ‗rational‘ tactic (i.e., ‗fad‘ in the Bikhchandani etal. (1992) sense), but clearly not an advisable strategy.




• Distinguish between what type of bubble it is. For example, is it a purely idiosyncratic

short-term bubble, or is it one of those ‗Austrian‘ macroeconomic monsters? The

differences can make or break your tactics and/or strategies.

In short, be wary of bubbles, and understand why many suggest they cannot exist (although, they

obviously do).

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Siegel, J., ―What is an Asset Price Bubble? An Operational Definition‖, Working Paper,

September 2002, 1-16.




Smith, V., Suchanek, G., and A. Williams, ―Bubbles, Crashes, and Endogenous Expectations in

Experimental Spot Asset Markets‖, Econometrica, Volume 56, Number 5, September 1988,

1119-1151.

Treynor, J., ―Bulls, Bears, and Market Bubbles‖, Financial Analysts Journal, Volume 54,


Wang, P., and Y. Wen, ―Speculative Bubbles and Financial Crisis‖, Research Division: FederalReserve Bank of St. Louis Working Paper Series, Working Paper 2009-029B, June 2009, 1-23.

Watanabe, K., Takayasu, H., and M. Takayasu, ―A mathematical def inition of the financial

bubbles and crashes‖, Physica A, Volume 383, Issue 1, September 2007, 120-124.

Wigmore, B., ―Revisiting the October 1987 Crash‖, Financial Analysts Journal, Volume 54,

Number 1, January/February 1998, 36-48.

Zhou, W., and D. Sornette, ―Is there a real-estate bubble in the US?‖, Physica A, Volume 361,

Issue 1, February 2006, 297-308.




Chapter 8: When does EMT seem to apply? – The Iceberg

Regarding the EMT, thus far we have no real world descriptive cases where it strictly applies;

and by now the reader should be aware that every empirically known case where we know the

‗model‘, the relative price is wrong most of the time in every market that was reviewed

(specifically, ―twin shares‖, equity ―carve-outs‖269, and CEFs). Traditionally, the EMT

description of the markets has focused on explaining the reaction of stock prices to trading

activity and especially arbitrage (the classic reference being Scholes (1972)). Is there any market

were the traditional arbitrageur forces price back to true value seems to apply? Answer: A very

qualified yes; and the primary qualification is that it doesn‘t work according to traditional theory.

That is, the descriptive reality of markets where certain relative prices (not absolute levels) trend

strongly toward market efficiency is relatively unique and contrived.

The market where it seems to work (i.e., and only for relative prices)

First, it is necessary to distinguish between three types of funds:

Closed-End Funds (―CEFs‖),

Open-End Funds (―OEFs‖), and

Exchange Traded Funds (―ETFs‖).

269 As mentioned, with ―carve-outs‖, we can only rationally conclude that a ―stub‖ with negative value must bewrong, all other cases we cannot know whether the price is wrong or correct, we just cannot say one way or theother definitively without an acceptable pricing ‗model‘.




The most common attribute that each shares, is that each typically represents a portfolio of

securities (i.e., they represent some interest in a pooled set of assets/securities). Among their key

differences (and ignoring that taxes, fees, and level of transparency tend to differ):

CEFs are typically a one time issuance (but then there is also in some cases a leverage

issue). Most important is that the market, not the fund company, is responsible for the

trading price.

OEFs are priced daily (i.e., at the end of the ‖trading day‖). Most important is that

redemption occurs at NAV at the end of each trading day. Also, note there is no bid/ask

spread, but there may be a penalty for redemption.

ETFs are traded on an exchange like CEFs, but have at least one crucial difference from

CEFs trading on an exchange. Most important is the price and method of redemption

(they are not redeemed at NAV).

ETFs are unique in that holdings are published daily.

Also, common to all three is that we have a relative pricing ‗model‘ that is most accurate for

tradeable securities, such as those in most OEFs, CEFs, and ETFs. Many are composed only of

exchange traded common stocks. Again, like CEFs, that ‗model‘ is the LOOP and the notion that

the price of the portfolio must equal the sum of its parts.

Now for the more critical distinctions between OEFs, CEFs, and ETFs:

Most retail ETF investors can buy or sell ETF shares in transactions on the exchange they

are traded on (e.g., the American Stock Exchange – the ―Amex‖) through a broker -dealer;

but certain institutional investors can create or redeem blocks of shares of ETFs (known




as ―creation units‖).270Therefore, those institutions are able to arbitrage differentials

between the trading value of the ETF and the NAV of the underlying assets or securities

by either creating new ETF shares or redeeming them. This is a critical difference, and it

is typically written into the ETF prospectus as a contractual obligation of the manager of

the ETF.

Unlike OEFs, CEFs and ETFs do not stand ready to redeem individual shares at NAV.

But ETFs, unlike CEFs, are typically contractually obligated to create or extinguish

creation units if traded values diverge from the NAV of the ETF (typically at the end of

each trading day). Like the arbitrage mechanism itself, the size of these blocks of creation

units are also typically stipulated contractually (i.e., in the ETF prospectus). Therefore,

the block size (currently, typically 50,000 shares) and other characteristics of the

arbitrage mechanism can vary from ETF to ETF, and typically from institution to

institution. In some cases institutions may trade securities ―in kind‖ (e.g., for the S&P

500 an investor may need all 500 stocks in the correct proportions) for creation units, in

other cases cash is used or an adequate substitute, again depending on the legal

stipulations.

Therefore, unlike deviations from NAV for CEFs, ETFs have a contractually stipulated

arbitrage mechanism whenever share values deviate from NAVs. The intent is obvious, to

minimize deviations of market prices from the NAVs of the ETF. In short, the intent is to

minimize relative price inefficiencies, and that is unique not just compared to CEFs, but

in the financial markets in general.

270 Actually, depending on the ETF, some individual investors may also redeem creation units, but as a general rulethey do not.




In the U.S., most ETFs are structured as OEFs, while some are structured as unit

investment trusts (including the most popular ETF, the SPDR). A new ETF must receive

a S.E.C. exemption from the Investment Company Act of 1940 (that rule may change in

the near future). Therefore, functionally an ETF is typically a cross between an OEF (in

the sense it redeems shares, albeit in a different manner than an OEF), and a CEF (in the

sense that shares trade on an exchange rather than the management company redeeming

or issuing at the end of the trading day at NAV).

So those are the key functional differences that lead to the question: Does the creation unit

arbitrage mechanism actually eliminate the arbitrage between the traded value of an ETF and its

NAV? Answer: Yes and no. In many cases it doesn‘t seem to have the intended effect, but

especially for a few of the larger ETFs it seems to do the trick most of the time. For example in

the following case it seems to work most of the time (daily data for the S&P 500 ETF – SPDR):




Data source: American Stock Exchange website (http://www.amex.com/amextrader/tradingData/ETFData/index.jsp) September 25, 2009.

Most of the time, there is almost no deviation from relative fair value (i.e., the dots oscillate

around zero). The average over the period is a 0.04% discount to NAV, the largest discount to

NAV occurred on May 14, 2009 (-22.51%); and the largest premium occurred on November 20,

2008 (4.82%). But, as you can see, these are truly outliers and not the norm. Therefore, for at

least the largest and arguably the most liquid ETF, and ignoring some outliers, it seems we have

found a case where relative (again, not absolute) efficiency reins most of the time!

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S&P 500 ETF (SPDR) Premium/Discount to NAVMarch 26, 2007 through September 24, 2009

http://www.amex.com/amextrader/tradingData/ETFData/index.jsp







In contrast to the SPDR and some other ETFs, many ETFs display similar deviations from NAV

that CEFs display271; but there seems little doubt that the ―creation unit‖ mechanism works to

largely eliminate deviations from NAV for some ETFs. Thus, the key is that ETFs can ―open‖

when prices deviate from NAV (i.e., the true fundamental value). Also, recall that I am not

saying the prices of the underlying shares, securities, commodities, etc. in the portfolio are

correct, only that the relative price changes are correct for certain ETFs most of the time.

Therefore, I do believe those prices are normally ―wrong‖ (i.e., the level of prices themselves),

but the price changes, at least in the case of ETFs, are generally ―right‖ for certain ETFs (mostly

the larger more liquid ETFs, like the SPDR).

It is important to note why we now have faith again in relative price changes, because:

1. There are effectively no limits to arbitrage (i.e., contractually).

2. We know the pricing formula (i.e., NAV). In fact, it is not really much of a formula at all,

but a tautology.

3. Depending on the ETF, one or more institutional investors is/are the rational

arbitrageur(s) making this happen at the margin. Therefore, while individual trades

matter, their impact on pricing can be completely offset by this arbitrageur, and therefore

the average is largely irrelevant.

4. Thus, based on 1-3, investor psychology (whether individual or institution) doesn‟t have

to matter and relative pricing efficiency can rule.

271 For example, Jares and Lavin (2004) find that discounts and premiums for Japan and Hong Kong ETFs are solarge and so predictable, based on changes in U.S. discounts and premiums for the same ETFs, that they were able toconstruct a trading strategy that generated cumulative returns of 542.25% and 12,119% for Japan and Hong ETFs,respectively. This clearly indicates that ETFs display many of the same apparent relative pricing inefficiencies thatCEFs possess.




5. No learning is required (i.e., the one marginal investor/market maker can be the primary

price setter).272

Again, the driver here is the contractual obligation of the ―creation units‖ (of course, being able

to both create ETF shares – in the case of traded price being less than NAV – and redeem ETF

shares – in the case of traded price being more than NAV). In a sense, arbitrage is not only

possible, but encouraged.

Ignoring the absolute price level issue, how many markets can the reader think of that are

structured like most ETF markets? I can only think of one, ETFs, and, again, it doesn‘t seem to

work for all, or even most, ETF markets.

In summary, ETFs are a truly anomalous market, a solitary iceberg. No other market where we

have the valuation formula (in this case relative valuation), or can infer one, do we see such

relatively well behaved pricing behavior, and all because of a contractual obligation to allow

shares to be increased or decreased through the creation or redemption of ―creation units‖273,

which doesn‘t exist in any other market as of the writing of this book.

272 This last point is important, for if the market is set up correctly, we don‘t need to worry about irrational traders(which are most traders), because money flows from the many irrational traders to the rational arbitrageur (just likethe EMH/EMT story), but in this case it is driven by the rules and laws of the specific ETF market that allow a BGIor State Street type investor to do this, otherwise it doesn‘t seem to happen.273 See the SPDR prospectus (pp. 32-41).




REFERENCES

Jares, T., and A. Lavin, ―Japan and Hong Kong Exchange-Traded Funds (ETFs): Discounts,

Returns, and Trading Strategies‖, Journal of Financial Services Research, Volume 25, Issue 1,

February 2004, 57-69.

Scholes, M., ―The Market for Securities: Substitution versus Price Pressure and Effects of

Information on Share Prices‖, Journal of Business, Volume 45, Number 2, April 1972, 179-211.

Standard & Poor‘s Depository Receipts (―SPDRs‖) Prospectus, SPDR Trust, Series 1, Sponsor:

PDR Services LLC, Prospectus Dated: February 24, 2009, 1-75.




Chapter 9: What could go wrong with financial market prices?

I have repeatedly noted that all the cases where we know or are highly confident of a pricing

‗model‘ we have not directly addressed the issue of whether, for example, the price of IBM

should be $10 per share or $1,000. It is time to shed some light on my contention that it is likely

they can be far away from the efficient market price. I will argue that under current conditions I

would expect them to be generally very far from market efficiency.

THE PRICING MODEL – DISCOUNTED PRESENT VALUE

I have mentioned that finance is ―simple‖; it is all about discounted present values. Again, we

only need to know cash flows and their associated discount rates. The difficulty is in identifying

the cash flows and selecting the correctly adjusted discount rates; yet I will argue we can greatly

simplify the issue and infer a basic premise concerning the pricing of most, if not all, financial

asset prices.




Historically, most, if not all, normative financial ―models‖ ignored specific market details and

implicitly assumed that they were irrelevant (e.g., the CAPM). This is highly unlikely. For

example, in most financial markets, often or largely due to limits to arbitrage, details matter (e.g.,

transactions costs, taxes, the influence of largely irrational traders with capital, etc.). In short, by

definition, market details not only matter, but they can be crucial to understanding a particular

market.

Historically, most empirical studies in finance analyze price changes (typically returns) and

implicitly assume that the absolute level of prices are correct (i.e., efficient). This is highly

unlikely. For example, what few examples we have of known financial market LOOP cases are

disappointing from that viewpoint (e.g., NAV vs. market price, ‗internet carve-outs‘, and ―twin

shares‖). In addition, the overwhelming majority of finance empirical studies concern themselves

with stocks/equities. But given that discount rates can and do have such a large impact on any

discounted present value calculation, I find that a more close examination of what drives

―riskless‖ bond pricing is likely to shed insight into the issue of the absolute level of prices.

Specifically, if ‗riskless‘ discount rates are found to be inefficient, then all discount rates are

likely inefficient, and thus all price levels are likely inefficient or wrong (i.e., from a market

efficiency standpoint).




Therefore, what about the market(s) for ‗riskless‘ bills, bonds, and notes? Given its size, scope,

and influence on virtually all other markets, let‘s focus on the market for U.S. Treasury

securities.

Question(s):

1. Do the details of the market matter?

2. Is it really ‗riskless‘?

3. Do relative changes differ from absolute levels?

4. Does the answer to #3 matter

Answers: (1) Yes, (2) no, (3) yes, and (4) very likely yes.

Let‘s now focus on questions #s 3 & 4.

First off, who are/is the marginal buyer(s)? Answer: One CB or multiple CBs are often the

marginal price setters. Are CBs ‗rational‘ arbitrageurs? As the reader will see, that is very, very,

unlikely, and it matters.

In order to address this issue the reader will need to be somewhat familiar with the following

foundational finance terms: duration, modified duration, yield curve, ‗term structure of interest

rates‘ (the ―term structure‖), spot rates, forward rates, and the ―expectations hypothesis‖ (―EH‖).

If unfamiliar with these terms, I encourage the reader to either read Appendix A of this chapter,

or some other brief review of the terms in possibly a textbook.




With presumed background knowledge, I will construct my argument for why I strongly believe

absolute prices are very likely to deviate substantially from market efficiency most, if not all, of

the time (i.e., at least for the last few decades). Ultimately, my case rests on both some form of

the EH and the Fisher equation (named after Irving Fisher274). Thus, if you generally buy into the

EH and Fisher equation very roughly holding, then I will argue it will be difficult to conclude

that the level of almost all prices in the financial markets are correct. In fact, it is likely that the

level of prices seriously deviate from fundamental or true economic values by substantial

amounts most of the time. There, you have been warned, now it‘s time to present the case.

In order to present my case I need to use two traditional mainstays of finance: (1) the

approximate Fisher equation, and (2) the real rate of interest. The Fisher equation typically

estimates the relationship between real and nominal rates with inflation (and is typically applied

as an approximation), and is defined as:

Actual: 1 + i = (1 + r ) (1 + π )

Approximation275

: i ≈ r + π or r ≈ i - π

where i = the nominal rate, r = the real rate, and π = expected inflation276. Therefore, the nominal

rate of interest is approximately equal to the real rate plus expected inflation; conversely, the real

274 See Fisher (1930). Besides being the first person to receive a Ph.D. in economics from Yale in 1891, he is

probably most famous for stating the following just before the stock market crash of 1929: ―Stock prices havereached what looks like a permanently high plateau. I do not feel there will be soon if ever a 50 or 60 point break from present levels, such as (bears) have predicted. I expect to see the stock market a good deal higher within a fewmonths." Irving Fisher, October 17, 1929 Not to be outdone, Keynes said in 1927: ―"We will not have any more crashes in our time." John Maynard Keynes1927275 Typically cross products are dropped. Short of very high inflation expectations and/or a very high real rate, theytend to be small.276 Note, that it is common for central bankers to claim, or even to have shown, that the CB can influence inflationexpectations (see, e.g., Bernanke et al. (2004)). I don‘t even as far in my assumptions as the current head of the




rate is approximately equal to the nominal rate minus expected inflation. The equation is simple,

intuitive, and has lasted the test of time.277

One practical problem with the Fisher equation is that it refers to a ―real rate‖ and a ―nominal

rate‖. In reality, and only considering government debt, we know there are a multitude of

nominal rates. By extension, there may be a multiple of real rates as well; and it is likely there is

some term structure of inflation expectations. For example, investors might generally think

inflation will be 2% this year and 5% the year after that, etc. Given the potentially complicating

impact of a term structure for nominal rates, real rates, and inflation expectations, one

historically useful academic simplification has been the EH. If, as the EH posits, the term

structure of nominal rates (or even real rates, and possibly inflation expectations) is determined

by the consensus forecast of future nominal interest rates, then a focus on some short nominal

rate is not unwarranted. That is, to the extent longer nominal (or possibly real and/or inflation

expectations) are largely determined by a sequence of shorter nominal rates, then a focus on

short rates should be sufficient as time precedes from one period to the next (i.e., by definition).

In fact, because arbitrage is more assured for short term government debt than long term debt,

empirically it seems to be the case that short nominal rates (and even derived real rates) appear to

Federal Reserve. If it is true that the CB has both nominal rates and inflation expectations largely under theirinfluence or control, and the Fisher equation is largely true, then they effectively control all three variables (nominal,real, and inflation expectations). Bernanke et al. (2004, p. 81) state: ―Shaping investor expectations throughcommunication does appear to be a viable strategy‖. If they truly believe that investors can be influenced merely bywords, even if misleading words, then even the basic tenants of the EMH/EMT are doomed (without even my basicargument even only roughly holding.277 As with most, if not all, textbook finance models, the Fisher equation is problematic for several reasons: (1) it islargely impossible to reject it (at least due to the ‗joint hypothesis problem‘), but (2) it is wrong (at least due tounderlying assumptions being wrong). See for example Nelson and Schwert (1977) on these and related issues.




drive longer term rates.278

Thus causality, at least over the last few decades, seems to run from

short to longer rates and not the other way around. Furthermore, it is not a stretch to imagine that

what is true for nominal rates is true of real rates and possibly inflation expectations.

By using the Fisher equation and replacing inflation expectations with realized inflation

(ironically much like a rational expectations economist279

might), one can infer what various

theoretical real rates have been (i.e., the one unknown is the real rate).

278 For example, Romer and Romer (2000, p. 429) state that their ―findings may explain why long-term interest rates

typically rise in response to shifts in monetary policy.‖ Their key finding is that the Federal Reserve seems topossess far superior inflation information that the evidenced by commercial inflation forecasts. In short, I wouldcontend that most macroeconomists believe that shorter maturity rate changes generally cause longer maturity ratechanges. That doesn‘t make it true, but it does seem macroeconomists as a group credit monetary policy with atremendous amount of power over short and long rates.279 Although, we are not really assuming this, as essentially we only assume that investors generally follow what theFederal Reserve is doing to the short rate, which is not nearly the equivalent of assuming they get all relevanteconomic theories and ‗models‘ exactly right (see Muth (1961)). The irony here is that we use a kind of rationalexpectations argument to show it results in pricing inefficiency because the CB is manipulating the short rate, whichin turn affects the other rates as we move out along the term structure.




Source of data: Federal Reserve Bank of St, Louis (FRED) – September 27, 2009 download.

The graph represents the approximate month-end real rates/yields for the following four generic

nominal government rates: (1) effective Federal Funds rate, (2) 3-month Treasury bill secondary

market rate, (3) 1-year constant maturity Treasury rate, and (4) the 10-year constant maturity

Treasury rate over a 55 year period. Essentially each was calculated using a rough approximation

of the Fisher equation: r ≈ i – π , where i (the nominal rate) is replaced by each of the four rates

reported by the Federal Reserve Bank, and in all cases π (expected inflation) is replaced by

actual inflation as reported by the government‘s CPI (Consumer Price Index for all urban

consumers: all items – CPIAUCNS). What should be clear is that, with few exceptions, over the

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Real Rates/Yields (July 1954 through August 2009)

Real_FF

Real_3mthTrs

Real_1yrTrs

Real_10yrTrs




55 years examined, all four ―real‖ (and nominal) rates tended to move closely together. The big

question for my purposes is whether the CB (i.e., the Federal Reserve Bank) is driving the U.S.

government yield curve (i.e., the 3-month T-bill, 1-year Treasury, and 10-year Treasury) through

the rate that they consider themselves to control (i.e., the Federal Funds rate)? At least most

academics seem to think that the CB has an overwhelming influence on shorter nominal rates

and minimally at least a large influence on longer nominal rates (e.g., Shiller (1979))280

, and

those working at the CB itself (e.g., Bernanke et al. (2004)).

The next graph should shed some light on causality (i.e., at least for one rate). Essentially I have

taken the nominal daily Federal Funds rate (DFF) and compared it to what is called the ―Federal

Funds target rate‖ (DFEDTAR). The effective Federal Funds rate sometimes spikes markedly

relative to the target rate. Clearly, the target rate is not a natural looking pattern, but it also seems

clear that the effective rate follows it no matter how far it might spike away. Clearly, the

effective rate may deviate, even substantially, for a few days but it always has returned to the

target rate‘s level. Therefore, it seems abundantly clear that the Federal Reserve target causes the

effective rate and not the other way around.

280 The primary question in this article was not so much the CB‘s impact on nominal rates (that was more or lesstaken as a given), but its influence on real rates.





It would seem that over time, if anything, the deviation from the target rate has lessened. Thus, it

is no exaggeration to state that the Federal Reserve effectively determines the ―short end‖ of the

yield curve and by extension the term structure of interest rates. The remaining question is

whether the Federal Reserve determines the ―long end‖ of the yield curve and the term

structure?281 The previous graph to this graph would strongly suggest they have a large and

persistent impact on long rates as well as the clear impact on shorter ones.

281 Again, e.g., see Romer and Romer (2000) as to the view that the Federal Reserve has an overwhelming impact onlong rates.

0

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Federal Funds Target Rate vs. Effective Federal Funds Rate(daily - 1982-09-27 through 2009-09-23)

DFEDTAR

DFF




If it is true, if not axiomatic, that the Federal Reserve/U.S. CB has an overwhelming influence in

determining both short and long rates, then one more look at the real rates graph with only the

real Federal Funds rate estimate shown might clarify the evolution of roughly measured real rates

over the last 55 years.


Given that the Federal Funds rate is the cost of member bank borrowing from the Federal

Reserve, at a minimum, there seem to have been times when the Federal Reserve paid member

banks to borrow (e.g., at least all of 1975). Again, all this hangs on the notion that one can

approximate the real rate (i.e., since it is unobservable), and that the Federal Reserve largely

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1

M a r - 9 3

J u l - 9 4

N o v - 9

5

M a r - 9 7

J u l - 9 8

N o v - 9

9

M a r - 0 1

J u l - 0 2

N o v - 0

3

M a r - 0 5

J u l - 0 6

N o v - 0

7

M a r - 0 9

Real Rates/Yields (July 1954 through August 2009) - only Real_FF




determines the nominal Federal Funds rate (which they claim to do). If both of these contentions

are accepted, then it is very likely the Federal Reserve has influenced rates in such a way that at

times people were paid to borrow. Does that sound like an efficient market? In essence, if it‘s

true that CBs, like the Federal Reserve, determine the ‗risk-free‘ discount rates, and they don‘t

do it from an financial market efficiency standpoint (which they clearly do not), then we are in

trouble (i.e., from a market efficiency perspective). More specifically, does paying people to

borrow sound like a good method for setting pricing efficiency in the financial markets? Answer:

‗Not bloody likely.‘

What I have failed to mention thus far is that the CPI used is not a consistent series (see

Appendix B entitled ―What happened to the CPI?‖). In fact, especially during the early to mid-

1980s it has been altered in such a way that it is difficult to compare consumer inflation now

with consumer inflation pre-1980.282 Therefore, the next graph I present will correct for that.

282 In fact, even the people responsible for the CPI contend that the current CPI reports significantly lower inflationthan the earlier version (see, e.g., Stewart and Reed (1999)). As they state (Stewart and Reed (1999, p. 37): ―theCPI-U does not incorporate changes retroactively.‖ Therefore, earlier CPI values are not remotely comparable tocurrent values. Furthermore, I would contend the changes have been systematically biased the CPI downward, withthe result being that the CPI itself is of little use as anything other than a rough measure of directional changes inconsumer inflation.




Source of data: Federal Reserve Bank of St, Louis (FRED) – September 27, 2009 download, and Shadow Statistics – September 27, 2009 download of adjusted CPI series.

The red line is the corrected series. That is, the consistent series is a combination of the blue line

until the early 1980s and the red line thereafter. Thus, a more consistent methodology would

show something historically unprecedented with respect to the real Federal Funds rate.

Specifically, the length and depth of the extent to which the Federal Reserve has influenced

nominal rates in such a way as to effectively pay people to borrow since the early 1990s is

-12.0%-11.5%-11.0%-10.5%-10.0%

-9.5%-9.0%-8.5%-8.0%-7.5%-7.0%-6.5%-6.0%-5.5%-5.0%-4.5%-4.0%-3.5%

-3.0%-2.5%-2.0%-1.5%-1.0%-0.5%0.0%0.5%1.0%1.5%2.0%2.5%3.0%3.5%4.0%4.5%5.0%5.5%6.0%6.5%7.0%7.5%8.0%8.5%9.0%9.5%

10.0%

J u l - 5 4

O c t - 5 5

J a n - 5

7

A p r - 5 8

J u l - 5 9

O c t - 6 0

J a n - 6

2

A p r - 6 3

J u l - 6 4

O c t - 6 5

J a n - 6

7

A p r - 6 8

J u l - 6 9

O c t - 7 0

J a n - 7

2

A p r - 7 3

J u l - 7 4

O c t - 7 5

J a n - 7

7

A p r - 7 8

J u l - 7 9

O c t - 8 0

J a n - 8

2

A p r - 8 3

J u l - 8 4

O c t - 8 5

J a n - 8

7

A p r - 8 8

J u l - 8 9

O c t - 9 0

J a n - 9

2

A p r - 9 3

J u l - 9 4

O c t - 9 5

J a n - 9

7

A p r - 9 8

J u l - 9 9

O c t - 0 0

J a n - 0

2

A p r - 0 3

J u l - 0 4

O c t - 0 5

J a n - 0

7

A p r - 0 8

J u l - 0 9

Real Rates/Yields (July 1954 through August 2009) - Which is the real "real" FF?

Real_FF

Real_FF_SGS-alt




unprecedented.283

Assuming my methodology is only roughly correct it is still quit shocking.

Again, does paying people to borrow seem efficient?

Keep in mind that all discounted financial assets (which are virtually all financial assets) are

impacted by discounts rates, by definition. Therefore, to the extent the basic ‗risk-free‘ or

government rates are impacted by what the Federal Reserve does (which is highly likely, and

more a question of degree), and the Federal Reserve chooses to push, for example, the real rate

below zero, how likely is it that any financial asset price is efficiently se t? Answer: ‗Not bloodylikely.‘ In short, prices are discounted present values, and all discount rates (and most cash

flows) are impacted directly by the government yields and/or spot rates. To the extent those rates

are not set efficiently, then we are in a world of trouble with respect to efficiency. In fact, to the

extent one accepts the notion the CB can push real effective rates below zero, then we likely end

up in a very inefficient world indeed (whether or not it‘s due to limits to arbitrage and/or

psychology).

Finally, given the above I tried to quantify the possible extent to which the Federal Reserve has

in the past pushed ‗risk-free‘ present values away from market efficiency.

283 See Bernanke et al. (2004) on monetary policy and ‗quantitative easing‘. Bernanke et al. (2004) clearly believethat pushing effective interest below zero is not only possible, but can be desirable.




Consider this to be a cautionary tale concerning the behavioral boundary condition for the

Federal Reserve. My intent is to roughly measure the theoretical maximum actual deviation in

real rates caused by the Federal Reserve and use that deviation to form present values (i.e.,

theoretical price levels). Note that both the minimum and maximum ‗real‘ Federal Funds rate

occurred after the final remaining behavioral constraint was lifted around the early 1970s (i.e.,

since the last link to gold was removed, and the move to purely fiat based money established).284

Thus, there is no longer a real limit to monetary policy looseness or tightness since the early

1970s.285 As to the actual values, the maximum theoretical real Federal Funds rate value was

284 It is important to keep in mind that, outside of major wars, including the Revolutionary War and the Civil War,inflation has not been a big issue in the U.S. until the last few decades (coinciding with the creation of the FederalReserve and especially the delinking the dollar from gold). For a decent review of this and going back about 150more years than I do, see Arnott and Bernstein (2002, pp. 76-77).285 With respect to things like nominal rates and inflation expectations, it is important that analyzing differentmonetary regimes can give rise to very different answers to effectively the same question. For example, Shiller andSiegel (1977, p. 891) found that ―prior to World War I nominal long and short rates of interest can be regarded as

Range of 'real rate' (Jul y-1954 through July-2008)

High Low Increment

9.55% -11.35% 1.05%

Discount rate -10.31% -9.26% -8.22% -7.17% -6.13% -5.08% -4.04% -2.99% -1.95% -0.90% 0.15% 1.19% 2.24% 3.28% 4.33% 5.37% 6.42% 7.46% 8.51% 9.55%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Ratio

Year PV PV PV PV PV PV PV PV PV PV PV PV PV PV PV PV PV PV PV PV min to max

0.083333333 $1.01 $1.01 $1.01 $1.01 $1.01 $1.00 $1.00 $1.00 $1.00 $1.00 $1.00 $1.00 $1.00 $1.00 $1.00 $1.00 $0.99 $0.99 $0.99 $0.99 1.021 $1.11 $1.10 $1.09 $1.08 $1.07 $1.05 $1.04 $1.03 $1.02 $1.01 $1.00 $0.99 $0.98 $0.97 $0.96 $0.95 $0.94 $0.93 $0.92 $0.91 1.22

2 $1.24 $1.21 $1.19 $1.16 $1.13 $1.11 $1.09 $1.06 $1.04 $1.02 $1.00 $0.98 $0.96 $0.94 $0.92 $0.90 $0.88 $0.87 $0.85 $0.83 1.49

3 $1.39 $1.34 $1.29 $1.25 $1.21 $1.17 $1.13 $1.10 $1.06 $1.03 $1.00 $0.97 $0.94 $0.91 $0.88 $0.85 $0.83 $0.81 $0.78 $0.76 1.82

4 $1.54 $1.48 $1.41 $1.35 $1.29 $1.23 $1.18 $1.13 $1.08 $1.04 $0.99 $0.95 $0.92 $0.88 $0.84 $0.81 $0.78 $0.75 $0.72 $0.69 2.23

5 $1.72 $1.63 $1.54 $1.45 $1.37 $1.30 $1.23 $1.16 $1.10 $1.05 $0.99 $0.94 $0.90 $0.85 $0.81 $0.77 $0.73 $0.70 $0.66 $0.63 2.72

6 $1.92 $1.79 $1.67 $1.56 $1.46 $1.37 $1.28 $1.20 $1.13 $1.06 $0.99 $0.93 $0.88 $0.82 $0.78 $0.73 $0.69 $0.65 $0.61 $0.58 3.32

7 $2.14 $1.97 $1.82 $1.68 $1.56 $1.44 $1.33 $1.24 $1.15 $1.07 $0.99 $0.92 $0.86 $0.80 $0.74 $0.69 $0.65 $0.60 $0.56 $0.53 4.05

8 $2.39 $2.18 $1.99 $1.81 $1.66 $1.52 $1.39 $1.27 $1.17 $1.08 $0.99 $0.91 $0.84 $0.77 $0.71 $0.66 $0.61 $0.56 $0.52 $0.48 4.95

9 $2.66 $2.40 $2.16 $1.95 $1.77 $1.60 $1.45 $1.31 $1.19 $1.08 $0.99 $0.90 $0.82 $0.75 $0.68 $0.62 $0.57 $0.52 $0.48 $0.44 6.05

10 $2.97 $2.64 $2.36 $2.10 $1.88 $1.68 $1.51 $1.35 $1.22 $1.09 $0.99 $0.89 $0.80 $0.72 $0.65 $0.59 $0.54 $0.49 $0.44 $0.40 7.39

11 $3.31 $2.91 $2.57 $2.27 $2.00 $1.77 $1.57 $1.40 $1.24 $1.10 $0.98 $0.88 $0.78 $0.70 $0.63 $0.56 $0.50 $0.45 $0.41 $0.37 9.02

12 $3.69 $3.21 $2.80 $2.44 $2.14 $1.87 $1.64 $1.44 $1.27 $1.11 $0.98 $0.87 $0.77 $0.68 $0.60 $0.53 $0.47 $0.42 $0.38 $0.33 11.02

13 $4.11 $3.54 $3.05 $2.63 $2.27 $1.97 $1.71 $1.48 $1.29 $1.12 $0.98 $0.86 $0.75 $0.66 $0.58 $0.51 $0.45 $0.39 $0.35 $0.31 13.46

14 $4.58 $3.90 $3.32 $2.83 $2.42 $2.07 $1.78 $1.53 $1.32 $1.13 $0.98 $0.85 $0.73 $0.64 $0.55 $0.48 $0.42 $0.37 $0.32 $0.28 16.44

15 $5.11 $4.30 $3.62 $3.05 $2.58 $2.19 $1.85 $1.58 $1.34 $1.15 $0.98 $0.84 $0.72 $0.62 $0.53 $0.46 $0.39 $0.34 $0.29 $0.25 20.08

16 $5.70 $4.73 $3.94 $3.29 $2.75 $2.30 $1.93 $1.63 $1.37 $1.16 $0.98 $0.83 $0.70 $0.60 $0.51 $0.43 $0.37 $0.32 $0.27 $0.23 24.52

17 $6.35 $5.22 $4.29 $3.54 $2.93 $2.43 $2.01 $1.68 $1.40 $1.17 $0.98 $0.82 $0.69 $0.58 $0.49 $0.41 $0.35 $0.29 $0.25 $0.21 29.95

18 $7.08 $5.75 $4.68 $3.82 $3.12 $2.56 $2.10 $1.73 $1.42 $1.18 $0.97 $0.81 $0.67 $0.56 $0.47 $0.39 $0.33 $0.27 $0.23 $0.19 36.58

19 $7.90 $6.34 $5.10 $4.11 $3.32 $2.69 $2.19 $1.78 $1.45 $1.19 $0.97 $0.80 $0.66 $0.54 $0.45 $0.37 $0.31 $0.25 $0.21 $0.18 44.67

20 $8.80 $6.98 $5.55 $4.43 $3.54 $2.84 $2.28 $1.84 $1.48 $1.20 $0.97 $0.79 $0.64 $0.52 $0.43 $0.35 $0.29 $0.24 $0.20 $0.16 54.56

21 $9.81 $7.70 $6.05 $4.77 $3.77 $2.99 $2.37 $1.89 $1.51 $1.21 $0.97 $0.78 $0.63 $0.51 $0.41 $0.33 $0.27 $0.22 $0.18 $0.15 66.64

22 $10.94 $8.48 $6.59 $5.14 $4.02 $3.15 $2.47 $1.95 $1.54 $1.22 $0.97 $0.77 $0.61 $0.49 $0.39 $0.32 $0.25 $0.21 $0.17 $0.13 81.39

23 $12.20 $9.35 $7.18 $5.54 $4.28 $3.32 $2.58 $2.01 $1.57 $1.23 $0.97 $0.76 $0.60 $0.48 $0.38 $0.30 $0.24 $0.19 $0.15 $0.12 99.41

24 $13.60 $10.30 $7.82 $5.96 $4.56 $3.49 $2.69 $2.07 $1.60 $1.24 $0.97 $0.75 $0.59 $0.46 $0.36 $0.28 $0.22 $0.18 $0.14 $0.11 121.41

25 $15.16 $11.35 $8.53 $6.42 $4.86 $3.68 $2.80 $2.14 $1.63 $1.25 $0.96 $0.74 $0.58 $0.45 $0.35 $0.27 $0.21 $0.17 $0.13 $0.10 148.29

26 $16.91 $12.51 $9.29 $6.92 $5.17 $3.88 $2.92 $2.20 $1.67 $1.26 $0.96 $0.74 $0.56 $0.43 $0.33 $0.26 $0.20 $0.15 $0.12 $0.09 181.11

27 $18.85 $13.79 $10.12 $7.45 $5.51 $4.09 $3.04 $2.27 $1.70 $1.28 $0.96 $0.73 $0.55 $0.42 $0.32 $0.24 $0.19 $0.14 $0.11 $0.09 221.20

28 $21.01 $15.19 $11.03 $8.03 $5.87 $4.31 $3.17 $2.34 $1.73 $1.29 $0.96 $0.72 $0.54 $0.41 $0.31 $0.23 $0.18 $0.13 $0.10 $0.08 270.17

29 $23.43 $16.74 $12.01 $8.65 $6.25 $4.54 $3.30 $2.41 $1.77 $1.30 $0.96 $0.71 $0.53 $0.39 $0.29 $0.22 $0.16 $0.12 $0.09 $0.07 329.97

30 $26.12 $18.45 $13.09 $9.32 $6.66 $4.78 $3.44 $2.49 $1.80 $1.31 $0.96 $0.70 $0.52 $0.38 $0.28 $0.21 $0.15 $0.12 $0.09 $0.06 403.02




established at 9.55% in June 1981 (about a year before CPI methodology began to systematically

bias estimates downward); whereas the minimum real Federal Funds rate was established at -

11.35% in July 2008 (well after most major CPI methodology changes). Of course, the Federal

Reserve may still have time to push this approximately 20.9% absolute spread in the real Federal

Funds rate further, but then again maybe not.

What is shown, and assuming the method is even roughly correct, is a two dimensional

representation of theoretical price levels where the discount rate is varied by 20 increments from

the maximum real effective Federal Funds rate almost to its minimum (a spread of about 20%)

and maturities are varied from 3-months to 30 years. Keep in mind that most stocks today have

durations much closer to 30-year bonds than probably any other theoretical bond on this table.

For example, at a discount rate of +6.42% (PV column # 17) a twenty year $1 non-coupon

paying bond‘s present value or theoretical price is $0.29 cents, while the same bond at -6.13%

(PV column # 5) is $3.54. It should begin to dawn on the reader how a price level can be

dramatically altered by monetary policy.

The key column for this table is last column on the right hand side (i.e., the farthest right column

entitled ―Ratio – min to max‖). That column shows the theoretically maximum deviation from

the lowest to highest that monetary policy has theoretically influenced price levels for each

maturity. For example, due to its duration, the most extreme example is the longest maturity

security (the 30-year bond with no embedded options or coupons) at about 403. What this means

real rates.‖ Therefore, prior to WWI there is no Fisher equation, because it reduces to i ≈ r . Clearly, the current fiatmoney regime needs to account for inflation.




is that theoretically, and assuming the reader accepts the methodology, the real value of a 30-

year risk-free cash flow can vary by as much as roughly 40,300% from intrinsic or fundamental

value based on Federal Reserve policy alone.286

Therefore, in answer to the question what can go

wrong with financial market prices; a great deal can go wrong. In fact, for example, the share

price of IBM could be pushed from say $10 to $4,030 or vice versa depending on irrational

Federal Reserve policy at the time it is present valued (i.e., at least based on their record for the

last fifty or so years, and especially since the early 1970s). Thus, in the future, and given the

current currency regime, it could be worse.

Therefore, if all PVs (i.e., all past and current prices) in all financial markets are at least in part

based on the ‗risk-free‘ rate(s) of discount/interest, and if CBs largely determine the ‗risk-free‘

discount rate(s), then, why would you expect any price to be ‗efficiently priced‘ (i.e., in the true

fundamental sense)? Answer: You wouldn‘t! Which is why it is much safer to assume that

absolute prices are ‗right‘ (when we know, in reality, that descriptively they are not) and

henceforth focus on price changes (i.e., rather than price levels). Thus, as with the proverbial tree

falling in the forest and nobody sees it, surely it never fell (or in this case the whole forest

falling)? Again, the two questions we generally concern ourselves with in finance are:

1 What is/are the cash flow(s)?

2 What is/are the discount rate(s)?

286 Of course, this does not include the possibility, even expressed by the current Fed Chairman that equities, and if equities then likley other asset classes, ‗overreact‘ to monetary policy. Bernanke and Kuttner (2005, p. 1254)suggest that ―further exploration of the link between monetary policy and the excess return on equities is anintriguing topic‖.




And if the ‗risk-free‘ rate is of questionable value (i.e., from a market efficiency standpoint);

then what chance do we have of any present value analysis that is wholly dependent (like the

government bond market) or significantly dependent (like all others) on such rate(s) being

efficiently priced?

Answer: You don‟t!

Which brings us to the final question of this chapter, if the price level of financial assets can be

off by say 40,300% or more287 due to the actions of one CB, what are the odds that relative

prices (i.e., outside of the ones empirically documented) are not also off by large amounts?

Therefore, given demonstrated monetary policy and the conditions that allowed it to push the

government term structure around, combined with our descriptive knowledge and understanding

of actual relative prices and related price changes, both relative and absolute prices are in fact

likely to be way off the mark from anything even approaching market efficiency of the

traditional informational sort, except if largely by accident, but certainly not likely by the intent

of government sponsored entities like CBs.

287 Therefore, there is certainly the possibility of a deviation beyond the aforementioned 400+ at thirty yearsmaturity (i.e., beyond 40,000%). Thus, for example, if an equity didn‘t pay a dividend (i.e., no cash flow) thedifference could be almost infinite (remember that some of the Dot.com companies paid no positive cash flow ordividend). Thu, it is certainly in the realm of the theoretically possible that differentials could easily trend toward theinfinite. Again, this wouldn‘t account for LOOP violations, etc., just the ―risk-free‖ rate impact on absolute pricelevels.




REFERENCES

Arnott, R., and P. Bernstein, ―What Risk Premium is ‗Normal‘?‖, Financial Analysts Journal,

Volume 58, Number 2, March/April 2002, 64-85.



Bernanke, B., Reinhart, V., and B. Stack, ―Monetary Policy Alternatives at the Zero Bound: AnEmpirical Assessment‖, Finance and Economics Discussion Series, Divisions of Research &

Statistics and Monetary Affairs, Federal Reserve Board, Washington, D.C., Staff Working Paper

No. 2004-48, September 2004, 1-86.

Chance, D., and D. Rich, ―The False Teachings of the Unbiased Expectations Hypothesis‖,

Journal of Portfolio Management, Volume 27, Issue 4, Summer 2001, 83-95.

Fabozzi, F., Pitts, M., and R. Dattatreya, R., ―Chapter 5: Price Volatility Characteristics of Fixed

Income Securities‖, 77-105, in The Handbook of Fixed Income Securities (Fifth Edition), Frank

Fabozzi (Editor), McGraw Hill Companies, New York, New York, 1997.

Fisher, Irving, The Theory of Interest, Macmillan, New York, New York, 1930.




Gultekin, N., and R. Rogalski, ―Alternative Duration Specifications and the Measurement of

Basis Risk: Empirical Tests‖, Journal of Business, Volume 57, Number 2, April 1984, 241-264.

Kritzman, M., ―What Practitioners Need to Know … … About Duration and Convexity‖,

Financial Analysts Journal, Volume 48, Issue 6, November/December 1992, 17-20.

Kritzman, M., ―What Practitioners Need to Know … … About the Ter m Structure of Interest

Rates‖, Financial Analysts Journal, Volume 49, Issue 4, July/August 1993, 14-18.

Lintner, J., ―Inflation and Security Returns‖, Journal of Finance, Volume 30, Number 2, Papers

and Proceedings of the Thirty-Third Annual Meeting of the American Finance Association, San

Francisco, California, December 28-30, 1974, May 1975, 259-280.

Macaulay, Frederick., Some Theoretical Problems Suggested by the Movements of Interest

Rates, Bond Yields and Stock Prices in the United States since 1865, No. 33, National Bureau of

Economic Research, Inc., New York, New York, 1938.

Mishkin, Frederic, The Economics of Money, Banking, and Financial Markets (Sixth Edition

Update), Addison Wesley, New York, 2003.

Muth, J., ―Rational Expectations and the Theory of Price Movements‖, Econometrica, Volume

29, Number 3, July 1961, 315-335.




Nelson, C., and W. Schwert, ―Short-Term Interest rates as Predictors of Inflation: On Testing the

Hypothesis that the Real Rate of Interest is Constant‖, American Economic Review. Volume 63,

Number 3, June 1977, 478-486.

Romer, C., and D. Romer, ―Federal Reserve Information and the Behavior of Interest Rates‖,


Shafir, E., Diamond, P., and A. Tversky, ―Money Illusion‖, Quarterly Journal of Economics,

Volume 112, Number 2, May 1997, 341-374.

Sargent, T., ―Rational Expectations and the Term Structure of Interest Rates‖, Volume 4,

Number 1, Part 1, February 1972, 74-97.

Shiller, R., ―Rational Expectations and the Term Structure of Interest Rates: Comment‖, Journal

of Money, Credit and Banking, Volume 5, Issue 3, August 1973, 856-860.

Shiller, R., ―Can the Fed Control Real Interest Rates?‖, NBER Working Paper Series, NBER

Working Paper No. 348, National Bureau of Economic Research, Cambridge, Massachusetts,

May 1979, 1-66..




Shiller, R., and J. Siegel, ―The Gibson Paradox and Historical Movements in Real Interest

Rates‖, Journal of Political Economy, Volume 85, Issue 5, October 1977, 891-908.

Stewart, K., an S. Reed, ―CPI research series using current methods, 1978-98 (Inflation would

have been lower from 1978 t the present if the current methods of calculating the CPI had been

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Williams, W., ―Government Economic Reports: Things You've Probably Suspected But Perhaps

Were Afraid to Ask!", August 24, 2004 ( http://www.gillespieresearch.com/cgi-

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Williams‘ Shadow Government Statistics, Special Comment, September 10, 2008.




APPENDIX A: SOME USEFUL TERMS TO KNOW, ESPECIALLY FOR THIS CHAPTER

Duration and convexity are very useful concepts for describing certain risk related attributes of

financial securities. They both measure sensitivity to changes in interest rates. Duration is a

linear measure of the sensitivity of the price of a security (for typically a bond) in response to a

change in interest rates. Convexity is a measure of curvature of the price change for a move in

interest rates. Mathematically, duration and convexity represent the first and second derivatives

of price with respect to changes in interest rates (hence, duration is a linear measure and

convexity a measure of curvature), respectively. It is important to note that when calculating

duration and convexity it is common practice to use a securities yield and not interest rates.

The original definition of duration is Macaulay‟s Duration (named after Frederick Macaulay,

who wrote the original academic reference on it – Macaulay (1938)).288 Because maturity seems

an inadequate measure of the sensitivity of a bond‘s price to changes in interest rates (e.g., it

ignores the effects of coupon or cash flow payments) and weighting by the time to receipt of

each cash flow ignores the time value of money, he weighted each cash flow by the present value

of its relative magnitude. The equation is:

, where n = number of cash flows, t = time

to receipt of cash flow, C = cash flow amount, and r = yield to maturity (or ―YTM‖). Therefore,

D depends on maturity, cash flows (i.e., in the case of a bond, typically coupon payments;

whereas for stocks dividends), and YTM. The effects of each are as follows: ),,(

r C t f D .

Thus, Macaulay duration is the weighted average time until receipt of the cash flows. An

288 Somewhat in line with Bachelier being forgotten for a long period, Macaulay‘s duration was rediscovered and putto use in finance around the 1970s.




increase in the time until receipt of a cash flow increases overall duration, an increase in the size

of the cash flow itself decreases duration, and an increase in YTM decreases duration.

As an example of a $1,000 priced bond possessing a 10 years to maturity, a 10% annual coupon

(thus paying $100 per year), with a 10% YTM (thus priced to ‗par‘):

Modified f rom: Kritzman, M., ―What Practitioners Need to Know … … About Duration and Convexity‘, FinancialAnalysts Journal, Nov/Dec 1992, p. 18.

Therefore, the time to maturity is 10 years, but its Macaulay duration is about 6.8 years (i.e.,

based on the effects of t , C , and r or YTM). Macaulay duration is a useful measure (i.e., as a

measure of security risk) and seems an improvement over time to maturity.289 Therefore,

289 In actuality, the effective difference between Macaulay duration, time to maturity, and other duration calculationsis typically not very great (see e.g., Gultekin and Rogalski (1984), where they test the effectiveness of sevenduration measures, including Macaulay duration, in predicting price movements given interest rate movements, andfound all wanting). Therefore, although useful, duration measures are not actually very accurate at predicting thatwhich they are designed to predict. In addition, to the extent a duration measure is desirable to use, which durationmeasure is selected is not usually critical.

Macaulay duration for a 10% annual coupon bond with a 10% YTM

Time to

Receipt of Present Weighted-

Cash flow Value of Value time

Period Cash flow (C ) (in years) Cash flow Weight to receipt

1 100 1 90.90909 0.0909 0.09090909

2 100 2 82.64463 0.0826 0.16528926

3 100 3 75.13148 0.0751 0.22539444

4 100 4 68.30135 0.0683 0.27320538

5 100 5 62.09213 0.0621 0.31046066

6 100 6 56.44739 0.0564 0.33868436

7 100 7 51.31581 0.0513 0.35921068

8 100 8 46.65074 0.0467 0.3732059

9 100 9 42.40976 0.0424 0.38168786

10 1100 10 424.0976 0.4241 4.24097618

Total 2000 1000 1 6.75902382




Macaulay duration can never be greater than maturity, but for a one cash flow security they are

the same. The following table takes the same 10% annual coupon bond and varies the maturity

date (i.e., time-to-maturity) and YTM.

Duration (in years) for a 10% annual coupon bond as t & YTM are varied

Time to maturity Yield-to-maturity (YTM)

(in years) 0% 2% 4% 6% 8% 10% 15% 20% 40% 60% 80% 100%

1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

2 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.8

3 2.8 2.8 2.8 2.7 2.7 2.7 2.7 2.7 2.6 2.6 2.5 2.4

4 3.6 3.6 3.5 3.5 3.5 3.5 3.4 3.4 3.2 3.0 2.8 2.6

5 4.3 4.3 4.3 4.2 4.2 4.2 4.1 4.0 3.6 3.2 2.9 2.6

6 5.1 5.0 5.0 4.9 4.8 4.8 4.6 4.5 3.9 3.3 2.8 2.5

7 5.8 5.7 5.6 5.5 5.4 5.4 5.1 4.9 4.0 3.3 2.7 2.3

8 6.4 6.3 6.2 6.1 6.0 5.9 5.6 5.2 4.1 3.2 2.6 2.2

9 7.1 7.0 6.8 6.7 6.5 6.3 5.9 5.5 4.0 3.1 2.5 2.1

10 7.8 7.6 7.4 7.2 7.0 6.8 6.2 5.7 4.0 3.0 2.4 2.1

11 8.4 8.1 7.9 7.7 7.4 7.1 6.5 5.9 3.9 2.9 2.3 2.0

12 9.0 8.7 8.4 8.1 7.8 7.5 6.7 6.0 3.9 2.8 2.3 2.0

13 9.6 9.3 8.9 8.5 8.2 7.8 6.9 6.1 3.8 2.8 2.3 2.0

14 10.2 9.8 9.4 9.0 8.5 8.1 7.1 6.1 3.7 2.7 2.3 2.0

15 10.8 10.3 9.8 9.4 8.9 8.4 7.2 6.2 3.7 2.7 2.3 2.0

16 11.4 10.8 10.3 9.7 9.2 8.6 7.3 6.2 3.7 2.7 2.3 2.0

17 12.0 11.3 10.7 10.1 9.4 8.8 7.4 6.2 3.6 2.7 2.3 2.0

18 12.5 11.8 11.1 10.4 9.7 9.0 7.5 6.2 3.6 2.7 2.3 2.0

19 13.1 12.3 11.5 10.7 10.0 9.2 7.5 6.2 3.6 2.7 2.3 2.0

20 13.7 12.8 11.9 11.0 10.2 9.4 7.6 6.2 3.6 2.7 2.3 2.0

21 14.2 13.3 12.3 11.3 10.4 9.5 7.6 6.2 3.5 2.7 2.3 2.0

22 14.8 13.7 12.7 11.6 10.6 9.6 7.6 6.2 3.5 2.7 2.3 2.0

23 15.3 14.2 13.0 11.9 10.8 9.8 7.7 6.2 3.5 2.7 2.3 2.0

24 15.9 14.6 13.4 12.1 11.0 9.9 7.7 6.1 3.5 2.7 2.3 2.0

25 16.4 15.1 13.7 12.4 11.1 10.0 7.7 6.1 3.5 2.7 2.3 2.0

26 17.0 15.5 14.0 12.6 11.3 10.1 7.7 6.1 3.5 2.7 2.3 2.0

27 17.5 15.9 14.4 12.8 11.4 10.2 7.7 6.1 3.5 2.7 2.3 2.0

28 18.1 16.4 14.7 13.0 11.6 10.2 7.7 6.1 3.5 2.7 2.3 2.0

29 18.6 16.8 15.0 13.2 11.7 10.3 7.7 6.1 3.5 2.7 2.3 2.0

30 19.1 17.2 15.3 13.4 11.8 10.4 7.7 6.1 3.5 2.7 2.3 2.0




For the one cash flow security (the one year or ‗zero coupon‘ bond), the time to maturity and

duration are equivalent, regardless of YTM. Otherwise, as YTM increases the duration

decreases; and as maturity increases the duration increases (neither in a linear fashion).

In order to estimate the price change associated with a change in interest rates, a slight change in

D (i.e., Macaulay‘s duration) is required (i.e., ‗modified duration‘ or )290

:

, and , where B = the price of the bond. Note, that r is typically

divided by the number discounting periods in a year (i.e., normally two). But, tends to

overestimate price declines and underestimate price increases with respect to changes in r (e.g.,

with YTM rising, the longest cash flow out will decrease more than the next furthest out and so

on, therefore the larger the change in YTM, the larger the asymmetric impact on discounted cash

flows and the less indicative this type of duration is). The larger the increase in YTM, the greater

the magnitude of the error by which

will overestimate the price decline; conversely, the

larger the decrease in YTM, the greater the magnitude of the error by which will

underestimate the price increase (this effect is also called convexity).

In addition to duration and convexity, it is useful to have a notion of the ‗term structure of

interest rates‘, forward rates, and their relationship to the ―expectations hypothesis‖. The ―term

structure of interest rates‖ is the relationship between the interest rates of securities at different

maturities (i.e., securities that differ by maturity only, hence, the usual focus on government debt

290 For a more thorough review of this and duration and convexity more generally, see, for example, Fabozzi et al.(1997).




that generally has no other features/embedded options).291

In practice, the term ‗yield curve‘ is

used synonymously with the term ‗term structure‘, but also remember we used the term ‗interest

rate‘ and not ‗yield‘.

Firstly, YTM is not an ideal yardstick (it is an Internal Rate of Return – ―IRR‖ – calculation that

assumes all cash flows are reinvested at that rate, which may be fine in a ‗flat‘ term structure

environment). Secondly, YTM varies as a bond‘s coupon rate varies (due to the tax

consequences of discounts and premiums). Therefore, the yields on a pure non-coupon paying

bonds (or pure discount bonds, e.g., ‗zero coupon‘ bonds) without any embedded options are

typically used as ‗spot rates‘ of interest. Furthermore, the calculation for the price of a coupon

paying bond using YTM and spot interest rates are (respectively):

n

n

y

F C

y

C

y

C P

)1(...

)1()1(2

2

1

1

andn

n

n

r

F C

r

C

r

C P

)1(...

)1()1(2

2

2

1

1

1

, where

P = current price,

nC C C ,...,,21 = coupon payment in periods 1 through n,

F = face value,

y = yield to maturity (YTM),

n = number of discounting periods, and

nr r r ,...,,

21 = spot rates of interest of pure discount bonds maturing in periods 1 through n.

One problem with using spot rates is that there may not be enough of them at all points along the

curve to fill out a full term structure (this is especially true at longer maturities).

291 The related review in this appendix essentially follows Kritzman‘s (1993) practical review.




We can also describe the term structure of interest rates with ‗forward rates‘.292For example, the

forward rate on a one year instrument one year hence is defined as (i.e., a no arbitrage

definition):

)1()1()1( 1,11

2

2 f r r , where

2r = spot rate for a two year instrument,

1r = spot rate for a one year instrument, and

1,1 f = one year forward rate for a one year instrument.

The general formula for determining the forward rate is:

1])1 /()1[() /(1

,

t nt

t

n

nt nt r r f , where

t nt f ,

= t -year forward rate for an n minus t year instrument,

nr = spot rate for an n-year/maturity instrument, and

t r = spot rate for a t -year/maturity instrument.

As if that wasn‘t enough, we can also describe the term structure of interest rates by relating

‗discount factors‘ to maturity (i.e., the reciprocal of 1 plus the spot rate raised to the maturity of

the instrument):

n

nr nd )1 /(1)( , where

)(nd = discount factor for n periods (where n is the maturity of the pure discount instrument).

Note that the discount factor must fall between 0 and 1.

292 Based on Sargent (1972), Shiller (1973) points out that in the ‗rational expectation‘s‘ version of the termstructure, forward rates are optimal forecasts of spot rates; and that even in theory the PEH itself is ―untenable‖.




The concepts of the term structure and forward rates are important concepts in order to

understand the functional workings of the expectations hypothesis and its two other commonly

accepted academic hypotheses.

The classic three hypotheses for explaining the term structure of interest rates are:

1. Expectations Hypothesis (―EH‖) – Is essentially the rational expectations view of life,

where the current term structure is determined by the consensus forecast of future interest

rates (e.g., forward rates are forecasts and should not be biased). Furthermore, an upward

sloping term structure indicates that investors expect interest rates to rise, a downward

sloping term structure indicates that investors expect interest rates to fall, and a flat term

structure indicates that investors expect interest rates to remain unchanged.

2. Liquidity Premium Hypothesis (―LPH‖) – Because historically the term structure has had

an upward slope most of the time and it is unlikely investors actually believe all pure

bonds will generate the riskless return (more specifically, it is unlikely long-term bonds

aren‘t more risky than short-term bonds and investors won‘t demand some risk premium

for this); the LPH posits that investors will demand a premium which will increase with

maturity, but at a decreasing rate.

3. Segmented Markets Hypothesis (―SMH‖) – The SMH posits that various groups of

investors (e.g., insurance companies) favor certain segments of the term structure, thus

creating their own demand/supply dynamics.




In reality, all three tend to be combined to explain the term structure of interest rates. In my

opinion, the EH tends to be the most useful for fixed income strategies/tactics (i.e., even if

strictly wrong – see, e.g., Chance and Rich (2001) on its being normatively incorrect).

Those three are standard textbook economics and finance explanations of the term structure. In

additional, there is a more mathematical and rational expectations based version of the EH called

the ―Pure Expectations Hypothesis‖ (―PEH‖). Under the EH (Mishkin (2003, p. 138)): ―The

interest rate on a long-term bond will equal an average of short-term of short-term interest rates

that people expect to occur over the life of the long-term bond.‖ Therefore, longer rates are

dependent on what shorter rates are, and how they might evolve through time. Clearly if a CB

has complete or even partial control over shorter rates they, by this hypothesis, it will have

substantial, if not complete, control over longer rates. Under the PEH, we get the following

identity for a two period example: , where:

= today‘s (time t ) interest rate on a two-period bond, = today‘s (time t ) interest rate on a

one-period bond, = expected future one-period interest rate, and = forward one-period

rate. Therefore, the forward rate is equal to the market consensus expected future short-term (i.e.,

). This assumes bonds of different maturities are ―perfect substitutes‖, and, therefore,

the expected returns on bonds with various maturities are equal. In addition, assuming that

current short-term rates are as likely are as likely to rise as fall, it implies that the yield curve will

tend to have a flat shape over time (i.e., on average).293

That is, an upward sloping yield curve

293 Under the PEH: the ―observed long-term rate is a geometric average of today‘s short-term rate and expectedfuture short-term rates. … Further, forward rates calculated from long-term yields are market consensus expectedfuture short-term rates‖. Obviously, like the more general EH, this contrasts with the Segmentation Theory (i.e.,




implies rising short-term rates, while, conversely a downward sloping yield curve implies

declining short-term rates.

Finally, one very useful technique for adjusting yields is called cubic spline smoothing (where

the discount factors are regressed on term to maturity, term to maturity squared, and term to

maturity cubed, then the estimated coefficients are used to adjust the discount factors):

3

3

2

21)( nbnbnbnd , where

)(nd = discount factor for maturity n, and

n = term to maturity.

In general, splines are useful methods for adjusting yields (especially if you have holes in your

data and/or you need some convergence forecast); and there are many ways to smooth and fill

gaps in yield data.

buyers for certain securities are divided into certain market segments) and the Liquidity Premium Theory (investorsneed to be compensated for bearing the increased risk of holding long-term securities).




APPENDIX B: WHAT HAPPENED TO THE U.S. CPI?

The CPI (Consumer Price Index) in the U.S. has been significantly modified starting around the

early to mid-1980s. S pecifically two large sets of modifications have taken place (―hedonic

adjustments‖ and ―substitution‖294). Ignoring changes in methodology, what would the

inflation295 rate have looked like?

294 If substitution occurs over long periods and/or periods of extreme economic hardship the products and services

used may end up representing a kind of survival basket.295 See Wikipedia on inflation: ―In economics, inflation or price inflation refers to a general rise in the level of prices of goods and services over a period of time. The term "inflation" originally referred to increases in the moneysupply (monetary inflation); however, debates regarding cause and effect have led to its primary use today indescribing price inflation. Inflation can also be described as a decline in the real value of money — a loss of purchasing power. When the general level of prices rises, each unit of currency buys fewer goods and services.Price inflation is usually measured by calculating the inflation rate, which is the percentage change in a price index,such as the consumer price index.‖ I bring this up because the term itself can be confusing if one mixes cause andeffect (e.g., the ultimate fundamental cause of price inflation is monetary).

-3.0%

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

5.5%

6.0%

6.5%

7.0%

7.5%

8.0%

8.5%

9.0%

9.5%

10.0%

10.5%

11.0%

11.5%

12.0%

12.5%

13.0%

13.5%

14.0%

14.5%

15.0%

J u l - 5 4

O c t - 5 5

J a n - 5

7

A p r - 5 8

J u l - 5 9

O c t - 6 0

J a n - 6

2

A p r - 6 3

J u l - 6 4

O c t - 6 5

J a n - 6

7

A p r - 6 8

J u l - 6 9

O c t - 7 0

J a n - 7

2

A p r - 7 3

J u l - 7 4

O c t - 7 5

J a n - 7

7

A p r - 7 8

J u l - 7 9

O c t - 8 0

J a n - 8

2

A p r - 8 3

J u l - 8 4

O c t - 8 5

J a n - 8

7

A p r - 8 8

J u l - 8 9

O c t - 9 0

J a n - 9

2

A p r - 9 3

J u l - 9 4

O c t - 9 5

J a n - 9

7

A p r - 9 8

J u l - 9 9

O c t - 0 0

J a n - 0

2

A p r - 0 3

J u l - 0 4

O c t - 0 5

J a n - 0

7

A p r - 0 8

J u l - 0 9

CPI Inflation and Corrected CPI Inflation (July 1954 through August 2009)

SGS-alt_CPI CPIAUCNS




Source of data: Federal Reserve Bank of St, Louis (FRED) – September 27, 2009 download, and Shadow Statistics – September 27, 2009 download of adjusted CPI series.

The lines shown represent year-over-year CPI inflation rates. Prior to the early 1980s, both

methodologies resulted in the same inflation values. Thus, the red line (which represents the

―corrected CPI‖) overlaps the blue line (which represents the amalgamation of more than two

decades of efforts to bias the rate downward). Again, there is a large and seemingly increasing

disconnect between the old methodology and the cumulative effects of the changes made since

the early 1980s on the U.S. CPI. Although the lines shadow each other even after key changes,

they maintain a large distance, especially during the 1990s and beyond. The question for my

purposes is primarily one of consistency, not conspiracy. I do not want to base analysis on two

different series, or in this case a series that represents one thing spliced with one that shows

something else. The reported U.S. CPI is just such a spliced series now. Therefore, the CPI must

be corrected to give a consistent series whether biased downward or not; but preferably unbiased.

According to the agency in charge of the CPI, most of the major changes made were:




Source: Stewart and Reed (1999, p. 31).

The two major categories of changes made were (1) ‗hedonic adjustments‘, and (2)

‘substitution‘. Hedonic adjustments are essentially quality adjustments (e.g., the speed of a

computer model increases, but its stated price doesn‘t change). Substitution adjustments are

Changes made to the Consumer Price Index for All Urban Consumers (CPI-U) since 1978 and their effect on the CPI research series

Year

Change Description implemented

Use of rental equivalence to measure changes in

homeowner costs

Changed homeowners’ component from cost of purchase to value

of rental services 1983

Quality adjustment of used-car pricesAdjusted prices of used cars for differences in quality after

changeovers to new models 1987

Quality adjustment of sampled housing units to reflect

aging of the unitsAdjusted rental values in CPI sample to refle ct aging

1988

Quality adjustment of apparel pricesUsed regression models to adjust apparel prices for changes in

quality when new clothing lines are introduced 1991

Treating shifts between brand-name and generic drugs as

price changes

Introduced new procedures that all ow generic drugs to be priced

when a brand-name drug loses its patent 1995

Change in shelter formula to el iminate composite

estimator

Replaced composite estimator with a 6-month chain estimator.

Underreporting of 1-month rent charges had resulted in missing

price changes in residential rent and homeowners ' equivalent rent 1995

Change in shelter formula to improve rental equivalence

estimator

Modified imputation of homeowners’ implicit rent to eliminate

upward-drift property of previous estimator 1995

Elimination of functional form bias for CPI food-at-home

categories

Introduced seasoning procedures to el iminate upward bias derived

by setting base-period prices of newly initiated items 1995

Elimination of functional form bias for other CPI

commodity and service categories

Extended food-at-home seasoning procedures to remainder of

commodities and services. Base-period prices were left unchanged

in most noncomparable substitutions 1996

Quality adjustment of personal-computer pricesUsed regression models to adjust personal-computer prices for

changes in quality 1998

Elimination of automobile finance charges Deemed out of scope of definition of CPI 1998

Quality adjustment of television pricesUsed regression models to adjust television prices for changes in

quality 1999

Accounting for consumer substitution within CPI item

categories

Introduced a geometric-mean formula that assumes a modest

degre e of consume r substi tuti on wi thi n most CPI ite m cate gori es 1999

Treating mandated pollution control measures as price

increases

Adjustments are no longer made to changes in poll ution control

regulations, which are now viewed as price changes and not quality

changes 1999




essentially a form of quality drift as prices rise (e.g., as the price of fillet mignon rises, people

will tend to substitute less expensive cuts of beef)296.

Regarding a substitution adjustment example, consider ‘geometric average‘ adjustments.

Williams (2008) states: ‖ Geometric weighting is a mathematical adjustment, not a model of

consumer behavior. The BLS touts the use of geometric weighting in the narrow CPI categories

as a way of measuring shifting consumer preferences based on changes in prices in related

items. The weights that shift based upon price changes (relatively higher price changes end up

with relatively lower weightings) do so by straight mathematical adjustment that the BLS once

described as ‗mimicking‘ substitution effects. The shifts are not calculated based on any

consumer surveying done, for example, as to how candy bar consumption would vary given

relative price changes. The BLS claims support for using geometric weightings in the CPI,

because everyone else does it. One also could argue that other sovereign statistical agencies, by

their nature, have a tendency to want to reduce reported inflation as much as possible‖.

In short, the BLS applies a version of geometric weighting that is not based on trying to ‗model‘

consumer behavior, but rather to reduce price inflation.

Regarding a hedonic adjustment example, in the above table, there is an item for ‖quality

adjustment of personal-computer prices‖. Specifically, computing power (speed and memory) is

296 See Williams (2008) on this specific issue. In reality, substitution seems only to occur within a category.Therefore, a substitution from filet mignon to eventually chicken is only possible if the two are in the same category,which they are not at this time, for example.




now used to adjust prices. Not only does this have an impact on the CPI, but in addition it

impacts GDP (apparently real GDP is what essentially remains after adjusting for inflation,

which in turn now makes several key hedonic adjustments). An example should prove

illustrative.

The following description is from Jim Willie (January 2004,

http://www.financialsense.com/fsu/editorials/willie/2004/0130.html):

―GDP manipulation takes place through a measure called the ‗Hedonic Price Index‘. This is a

statistical maneuver employed by government statisticians to measure computer output and

investment. It is meant to capture the increase of computer power in terms of speed and memory.

The government takes the actual increase in spending on computer investment and applies a

statistical wand which changes the actual number into a higher number reflecting the

hypothetical benefits of soaring computer power. Like corporations, which keep two sets of

books, one for financial reporting and another set of books for taxes, the government also keeps

two different sets of books. One set is the actual dollars spent on the output of goods and services

and the other set is called chained dollars, which is derived after various statistical manipulations

have been applied to the actual numbers. As this table shows, actual computer spending in actual

dollars went from $86.3 billion during the fourth quarter of 1998 to $114.2 billion in the second

quarter of this year. This represented an increase of $28 billion in actual dollars being spent

during the last six quarters.

http://www.financialsense.com/fsu/editorials/willie/2004/0130.html






Investment in Computers & Peripheral Equipment (billions of dollars)

Source: Department of Commerce: Survey of Current Business

However, after applying the hedonic deflator, that actual number is changed into $127 billion in

chained dollars for the same six quarters. This technique magnifies the actual contribution of

computer investment to GDP growth. This manipulated rise in GDP growth doesn't reflect actual

increases to GDP growth. Instead, it reflects the increase in computer power that businesses are

getting for their money. As the power of computers increases, so does the impact of the hedonic

deflator. Effectually, this creates a statistical mirage, which magnifies modest sums of money

spent in actual dollars into giant sums in chain-weighted dollars.

Official ‗annualized‘ GDP growth was claimed to be 8.2% for Q3 of 2003. A closer look at

treatment of information technology business activity in Q3 is highly revealing. Chain-weighted

figures show $93.1 billion in IT spending, of which only $11.5 billion occurred in real terms.

The remaining $81.6 billion, over 87% of the ledger item in the GDP calculation, incredibly was

attributed to adjustment for speed improvements, a treatment called ‗hedonic adjustment.‘ The

practice is highly deceptive, totally fallacious, and not based in any reality known to mankind.

1998 1999 2000

4th Qtr 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 1st Qtr 2nd Qtr

Actual Dollars 86.3 88.1 92.8 97.6 98.9 104.3 114.2

Chained Dollars 171.3 186.1 208.5 230.9 243.9 264.1 298.5

Source: Department of Commer ce: Survey of Current Business




That extra eighty billion in dollars flows nowhere, is available for business expansion nowhere,

can be devoted to worker payrolls or benefits nowhere, and appears nowhere on any financial

balance sheet. It is pure fiction, but serves a very valuable service in keeping the myth alive of

above normal growth.‖

Source: Willie, Jim, http://www.financialsense.com/fsu/editorials/willie/2004/0130.html),

January 2004

Another related area of statistical manipulation is computer software. Now economic growth

includes spending on software; it was formerly booked as a business expense but is now

regarded as an investment. Expenses are subtracted from revenues and thus reduce corporate

profits. Business expenses, until recently, were not included in GDP, especially as a GDP growth

item.

The following description is continuation from Jim Willie (January 2004,

http://www.financialsense.com/fsu/editorials/willie/2004/0130.html):

Investment in Software (billions of chained 1996 dollars)

1998 1999 2000

4thQtr

1st Qtr 2ndQtr

3rdQtr

4thQtr

1stQtr

2ndQtr

Softwa

re167.3 173.3 181.1 192.5 205.3 215.0 227.5













―Software spending has been running above $200 billion per year. The combination of inflating

the dollars spent on computers, and including software spending as a capital asset, has artificially

inflated GDP by a sum of over $500 billion. These statistical manipulations accounted for 32%

of the reported GDP growth.

Accounting gimmicks also overstate U.S. productivity figures. Productivity is simply the

increase in total output as measured by GDP, divided by the increase in total hours of labor used

to create that output. Recently, those numbers have been remarkable. Tinkering with the GDP

Deflator and adding the Hedonic Deflator have artificially enhanced the actual GDP numbers.

The larger the GDP number in relation to the total hours of labor, the higher the rate of

productivity.

The results of these measures have produced an awe-inspiring statistical mirage that has

camouflaged the inherent weaknesses and vulnerability of the U.S. economy. This unique way in

which the U.S. measures and accounts for its GDP and productivity has captured the attention of

international organizations such as the OECD. Other well-known writers from the Austrian

school like Dr. Kurt Richebächer, and financial writer James Grant, a columnist for the Financial

Times, have called attention to these statistical fallacies.

Writers in the mainstream press have attacked these truth-tellers. The mainstream press argues

that increases in DRAM, hard drive capacity, and such things as DVDs, although not costing




more today, add additional value to a computer that is not captured in its price. Nobody would

argue that today's computer is faster and more powerful than the computers built back in 1996.

However, computers have become a commodity that is subject to intense price competition. The

price of computers has fallen as production has ramped up and competition has decreased their

price as with any other commodity.‖

Source: Willie, Jim, http://www.financialsense.com/fsu/editorials/willie/2004/0130.html),

January 2004

Whether intentionally or not, hedonics alone have had large and persistent impacts on such

values as GDP & the GDP deflator, productivity, and the CPI. The ‖devil is in the details‖ and

most people neither understand or acknowledge they are looking at effectively spliced series.

In addition, there is a doucmented psychological tendency for humans not to sufficiently adjust

nominal values for the effects of inflation (see, e.g., Shafir et al. (1997) on ‘money illusion‘).297

Actually, as a general rule, people are systematically poor at adjusting almost any nominal value,

let alone a series. Their evaluations are biased toward a nominal evaluation, and normative

economics demands that they not be. Therefore, in fact nominal perceptions often drive real

actions by individuals in apparently predictable ways. This fact is not lost on, for example CBs,

who know this (as well as other governement bodies), and readily attempt to fashion policy

accordingly (see, e.g., Bernanke et al. (2004)). Of course, there are exceptions:

297 Financial academics have long been aware of the ‗money illusion‘ and related issues (see Lintner (1975) for areview and normative analysis of its impact on normative ‗models‘ in finance).








―It is widely believed that the US has experienced a productivity miracle that has left the rest of

the world behind. Reality may well be very different. The reason lies in the way that output is

measured either side of the Atlantic. In general, the US statisticians use what is known as

'hedonic' pricing and Europeans don't. The difference is startling. The Office for National

Statistics has estimated that over the past four years the apparent rise in British industrial output

would have been three times the previous estimate had the US system been in place.

Reality, of course, is not changed by the way it is measured, but perceptions certainly are,

and US and European statistics give very different impressions about productivity and inflation

as well as about output. …

Whether or not hedonic pricing is sound and sensible is the cause of heated arguments. What

cannot be doubted is that the use of different systems makes nonsense of the relative

measures of growth, productivity and inflation. …

Just because a computer can do more things than before, or do them faster, does not mean people

using them will wish to or be able to take advantage of this. Hedonic pricing of computers,

which is the big issue, has been likened to saying that a car costing £15,000 which can go at 150

miles an hour has the same value as one costing £5,000 with a maximum speed of 50 mph. But

of course there is a vast difference between the speed at which cars can go and the one at which

they do. … ―

Dated 16 October 2000, from Smithers & Co. LTD

Remember, as always, be suspicious of others motives (including the author). Conflicting

interests and seemingly suspicious motivations don‘t invalidate someone‘s point, but it should




make you more cautious about accepting it. Organizations (especially governments) and

individuals can and do manipulate data for self interest (and governments are in an ideal position

to manipulate macroeconomic data).

"As former Labor Secretary Bob Reich explained in his memoirs, the Clinton administration had

found in its public polling that if the government inflated economic reporting, enough people

would believe it to swing a close election. Accordingly, whatever integrity had survived in the

economic reporting system disappeared during the Clinton years. Unemployment was redefined

to eliminate five million discouraged workers and to lower the unemployment rate;

methodologies were changed to reduce poverty reporting, to reduce reported CPI inflation, to

inflate reported GDP growth, among others. The current Bush administration has expanded upon

the Clinton era initiatives, particularly in setting the stage for the adoption of a new and lower-

inflation CPI and in further redefining the GDP and the concept of seasonal adjustment.

If the 1980 GDP methodology were applied to today's data, the 2004 second quarter's annualized

inflation-adjusted GDP growth of 3.0% would be roughly three percent lower (effectively netting

to zero percent or below). In like manner, current annual CPI inflation is understated by about

2.7% against the pre-Clinton CPI methodology (would be about 5.7%), and the unemployment

rate is understated by about seven percent against its original design and what many people

would consider to be actual unemployment (would be about 12.5%)."




Gillespie Research's "A Primer on Government Economic Reports -- Things You've Probably

Suspected But Perhaps Were Afraid to Ask!" by Walter J. "John" Williams - Aug. 24, 2004 –

http://www.gillespieresearch.com/cgi-bin/s/article/id=264)

Therefore, for example, a large segment of the unemployed are not defined as unemployed. Two

specific examples are that the modifications have resulted in many, if not most, unemployed

people stop being counted as "unemployed" when their benefits run out, or when they retire early

because they are unemployed. One must question why these people are left out of the

calculation, when they are unemployed (at least by non-Orwellian definitions)? To me the

answer is obvious; clearly someone or some group wants to reduce the number of those that are

perceived to be unemployed. In the same vein, it is clear that some have wanted to play down the

inflation numbers, and increase the GDP and productivity numbers. The issue for me is a

consistent and unbiased data series (failing unbiased, then at least consistent). If one isn‘t

comparing a consistently derived series but one that, for example, represents one definition of

unemployment, then evolves into a more biased notion of unemployment (if it measures it at all),

it is difficult, if not impossible, to determine things like empirical causality. Simply put, ―garbage

in, garbage out‖.

Also, remember one of the basic axioms of expected utility is being able to identify and order

things; without consistent and unbiased numbers298, and even if all of us were strictly rational in

the strict economic sense (which we aren‘t), the identification of something as basic as inflation

298 Obviously, this means most are unaware of the bias and consistency issues (which, as of today, most treat thenumbers as unbiased and consistent, at least in most of academia and the mainstream media).




is not feasible and the subsequent ordering of our wants would thus be rendered impossible.

Think of the series of economic tasks that would no longer be able to be optimized, all because

the numbers we based our economic decisions on couldn‘t be relied upon as being unbiased or

even consistent. Therefore, keep in mind that the estimates of PV deviations are likely to be

conservative, because I am not even taking account of the impact of these inconsistencies and

biases.




Chapter 10: Overreaction and Underreaction (overshooting and

undershooting)

It is commonly acknowledged among practitioners that the financial markets seem to overshoot

and undershoot.299 Of course, given that academic finance doesn‘t have one accepted ‗model‘ of

what constitutes fundamental value this can only indirectly be inferred, if at all. Although, as in

the specific LOOP cases reviewed (e.g., ‗twin shares‘) it is relatively clear most relative prices

deviate from another adjusted value they shouldn‘t deviate from; but, again, the overall price

level remains elusive to the academic profession, at least as of today.

In addition, since a seminal academic article by De Bondt and Thaler (1985) on ‗overreaction‘

and two by Bernard and Thomas (1989 & 1990) on ‗underreaction‘ (more specifically, ‗earnings

drift‘), traditionally inclined finance academics have had the somewhat unenviable task of

disproving and trying to find contra evidence against what seems to be the obvious: that prices

are at least sometimes too low or too high, and sometimes that is caused by overshooting

(overreaction) and sometimes by undershooting (underreaction).

Furthermore, financial market overreaction and underreaction is a classic topic for this book

because it demonstrates the following principals:

(1) The markets are ‗inefficient‘ in the traditional finance textbook sense of the term.

299 In academic circles these terms are often applied to exchange rates, because there is a ‗model‘ using the terms.Otherwise, they are largely absent from academic discourse in economics or finance.




(2) Market participants almost assuredly are acting in ‗irrational‘ ways in order to

cause these two effects. That is, the psychology piece of behavioral finance seems

obvious in these cases, although the specifics may be difficult to discern, and

especially to prove.

(3) Although there appears to be a ‗free lunch‘ (i.e., as defined traditionally by

normative textbook finance), because textbook finance doesn‘t account for: (A)

realistic costs (e.g., transaction costs, taxes, brokerage costs, etc.), and (B)

realistic risk and/or characteristics, there may not be a ‗free lunch‘ after all. That

is, the limits to arbitrage part of behavioral finance seems to loom large in these

cases.

(4) It is also relatively clear that you can have more than one type of inefficiency at

the same time in the same market. That is, little or no ‗cancelation‘ is the norm.

Those are four common themes of this book and they all (at least for the author) come together

nicely in this chapter.

The chapter is organized as follows: Firstly, overreaction will be reviewed. Secondly,

underreaction will be reviewed. Lastly, a summary and discussion of the empirical facts

established regarding overreaction and underreaction (e.g., they occur in the same markets at the

same time – little or no ‗cancelation‘).




OVERREACTION – MOSTLY IN THE MEDIUM- TO LONG-RUN

In 1985 an academic article came out that created what I consider to be an odd combination of

somewhat immediate and emotional rejection as well as a general denial among many finance

academics. The article was by De Bondt and Thaler (1985, p. 793) and was justified as a means

of testing whether market participants, at least in the U.S. stock market, conformed to or violated

Bayes‘ law (also called Bayes‘ Theorem or Bayes‘ rule)300: ―Bayes‘ rule prescribes the correct

reaction to new information. It has now been well-established that Bayes‘ rule is not an apt

characterization of how individuals actually respond to new data (Kahneman et al. [14]). In

revising their beliefs, individuals tend to overweight recent information and underweight prior

(or base rate) data. ... violates the statistical principal that the extremeness of predictions must be

moderated by considerations of probability.‖301 De Bondt and Thaler‘s (1985) article on stock

300 Wikipedia ( November 11, 2009) defined the theorem as: ―Bayes gave a special case involving continuous prior and posterior probability distributions and discrete probability distributions of data, but in its simplest settinginvolving only discrete distributions, Bayes' theorem relates the conditional and marginal probabilities of events Aand B, where B has a non-vanishing probability: P(A│B) = ((B│A)P(A))/(P(B)) . Each term in Bayes' theorem has aconventional name: P(A) is the prior probability or marginal probability of A. It is "prior" in the sense that it doesnot take into account any information about B. P(A|B) is the conditional probability of A, given B. It is also calledthe posterior probability because it is derived from or depends upon the specified value of B. P(B|A) is theconditional probability of B given A. P(B) is the prior or marginal probability of B, and acts as a normalizingconstant, Bayes' theorem in this form gives a mathematical representation of how the conditional probability of event A given B is related to the converse conditional probability of B given A.‖

301 Important points to remember about Bayes‘ theorem:

1. Using what could be called ―probability logic‖, it is a logical mathematical identity applied to statistics. Therefore, it is normative, but in the realm of mathematics and statistics.

2. It deals with conditional probabilities. In fact, it may be the first written work on conditional probability.3. Explicitly, and most often implicitly, most normative finance models (which are most finance models, and

effectively all models before the 1990s) assumed it to hold (among many other explicit and implicitassumptions).

4. Humans are just horrible at conditional probabilities (i.e., unless trained otherwise).5. Therefore, descriptive reality is quit different than Bayes‘ theorem. In short, it is typically violated by

most people (individuals or groups) in most contexts most of the time, but it is assumed to hold for all

market participants all the time in most normative finance models.




market overreaction has withstood most, if not all, of the EMT proponent attacks mounted

against it.

Source: De Bondt and Thaler (1985, p. 800).

The empirical results are derived from what turned out to be a ―weak-form‖ test of market

efficiency (i.e., using past prices should not allow one to ‗beat the market‘). Their method was to




form two portfolios based on one to three year past returns.302

These portfolios of the worst and

best performing stocks were then tracked (and adjusted for risk) in effectively what amounts to a

sequence of ―event studies‖. The worst performing stocks are named the ‗loser portfolio‘, and the

best performing stocks the ‗winner portfolio‘. Over a 50 year period, their best 3-year formation

period result, or worst if you are an efficient market proponent, was an average +19.6% excess

return for the ‗loser portfolio‘ and an average -5.0% excess return for the ‗winner portfolio‘, for a

total excess return of 24.6% over a 36-month or 3-year period (i.e., 24.6% represents going

―long‖ ‗losers‘ and ―short‖ ‗winners‘). As can be seen from the last graph, the ‗loser portfolio‘ spikes upward at months 1, 13, and 25. Those months are January. Therefore, this type of

portfolio formation results in what at first pass would seem a very concentrated ‗January effect‘

(which effect in turn tends to be concentrated in smaller firms; but doesn‘t seem to be the

primary cause for this effect – see, e.g., De Bondt and Thaler (1987)).

Their results can be summarized as follows:

• Over the last half century, loser portfolios of 35 stocks outperformed the market by, on

average, 19.6%, 36 months after portfolio formation.

• Winner portfolios earned about 5.0% less than the market (so that the difference in

ACAR or cumulative average residual between the extreme portfolios equals 24.6%, with

a t -statistic of 2.20).

302 As background on the De Bondt and Thaler (1985) sample, monthly data for NYSE common stocks from theCRSP tapes for the period January 1926 through December 1982 were used as the dataset. An equally-weightedarithmetic average rate of return for all CRSP listed securities served as the market index. They excluded stockswithout at least 85 months continuous return data, because they needed to be able to calculate 36 months of cumulative excess returns. Given the dataset, there are 16 non-overlapping portfolio formation dates (Dec. 1932,Dec. 1935, … , Dec. 1977); and they used the worst or best 35 or 50 stocks selected for the worst and best deciles.Thus, the numbers discussed represent averages of the excess returns calculated over these periods.




• The ‗overreaction effect‘ is asymmetric (i.e., larger for losers than winners, at a ratio of

about four to one).

• Most of the excess returns are realized in January.

• For one year periods, no reversals for losers (actually slightly negative returns).

The key results are twofold (and in order of importance):

(1) That ‗loser‘ stock prices seem to overshoot and then positively correct back over a three

plus year period (especially each January).

(2) These ‗loser‘ excess returns are asymmetric relative to the ‗winners‘ excess returns,

which seem to overshoot in the other direction and then negatively correct back over a

three year plus period.

Again, what was most disconcerting for EMH/EMT proponents is that the excess returns are

generated by only using past prices as a signal of future returns. In addition, the general

behavioral bent of the study seemed to drive them crazy.

In general there were two sets of attacks on the ‗overreaction effect‘: (1) the most significant one

reduced down to essentially the claim that De Bondt and Thaler, among others, failed to properly

account for bid-ask spreads (i.e., transaction costs) (see Conrad and Kaul (1993)); and (2) of

lesser importance, was the point that the ‗overreaction effect‘ did not occur every period (e.g.,

during the Great Depression) (see Chen and Sauer (1997)). Both attacks were found wanting.

Regarding the second point first, yes indeed based on the original methodology there may be

some periods where the effect seems to recede in statistical significance (as could probably be

shown with virtually any real world effect), yet there still seems to be overwhelming evidence




that it exists. For example, see Antoniou et al. (2005) and Diacogainnis et al. (2005) on the effect

on the Athens Stock Exchange, see Mazouz and Li (2007) on the effect in the U.K. market, Fung

(1999) on Hong Kong large-caps, Wang et al. (2004) on Chinese A and B shares, Richie and

Madura (2004) on international ETFs, Poteshman (2001) and Stein (1989) on the options

markets, Larson and Madura on exchange rates, Rozeff and Zaman (1998) on insider

transactions, Seyhun (1989) on the October ―1987 crash‖, Vergin (2001) on NFL point spreads,

Dreman and Berry (1995), Dreman and Lufkin (2000), Howe (1986), Jegadeesh and Titman

(1995), and (Saleh (2007), etc. Also, there is evidence that directly contradicts their result (e.g.,

see Loughran and Ritter (1996)). Regarding the first point that specifically concerns accounting

for bid-ask spreads on U.S. stocks, especially small-cap stocks, it has been found to be wrong.

For example, Loughran and Ritter (1996, p. 1959) found: ―The difference in findings between

this study and Conrad and Kaul‘s is primarily due to their statistical methodology. They

confound cross-sectional patterns and aggregate time-series mean reversion, and introduce a

survivor bias.‖ In short, what they thought they were measuring they were not, and the effect

holds or even increases in intensity once this is taken account of.

In addition, to the initial bid-ask and time period issue complaints, one immediate criticism of

the original article was related to the ‗January effect‘. Specifically, proponents of the EMH/EMT

initially responded that De Bondt and Thaler‘s (1985) ‗overreaction‘ result was really just the

‗January effect anomaly‘, and hence nothing special, since the fact was that the bulk of excess

returns happened in January. It is empirically well established that the ‗January effect‘ is also

associated with a small firm effect. That is, much of the January effect is associated




overwhelmingly with small firms. Therefore, a priori it is possible, if not likely, that the

overreaction effect is entangled with whatever is/are causing the January and/or small-firm

effect. Recent standard normative practice in finance is to add a ‗risk factor‘ for size (e.g., small -

cap vs. large-cap stocks) and largely eliminate excess returns associated with small-cap stocks.

Again, even by normative standards, this has no real theoretical rational, but it achieves the

desired effect of largely eliminating excess returns associated with the supposed ‗anomaly‘ (this

is also done with value vs. growth stocks, and in some cases momentum). In response, De Bondt

and Thaler (1987, p. 557) did a follow-up piece showing this wasn‘t the case, and summarizedtheir results as follows: ―In this follow-up paper, additional evidence is reported that supports the

overreaction hypothesis and that is inconsistent with two alternative hypotheses based on firm

size and differences in risk, as measured by CAPM-betas. The seasonal pattern of returns is also

examined. Excess returns in January are related to both short-term and long-term past

performance, as well as to the previous year market return.‖ In response, Zarowin (1990, p. 113)

summarized his results as follows: ―This potential violation of the efficient markets hypothesis is

labeled the ‗overreaction‘ phenomenon. This paper shows that the tendency for losers to

outperform winners is not due to investor overreaction, but to the tendency for losers to be

smaller-sized firms than winners. When losers are compared to winners of equal size, there is

little evidence of any return discrepancy, and in periods when winners are smaller than losers,

winners outperform losers.‖303In short, the effect is still there it has merely been redefined as the

303 In fact, and oddly given his attempted arguments against overreaction, Zarowin (1990, pp. 121-124) findsevidence that during periods when the winners‘ portfolio is composed of smaller firms than the losers‘ portfolio, thewinner part of the effect is much more dramatic than De Bondt and Thaler‘s original article. That is, winners seemto outlose any losers‘ gain. This is interesting, but hardly contradicts overreaction. In fact, it would tend to supportit, and would seem to indicate (i.e., if true) that overreaction is a complicated phenomenon.




small-cap effect or anomaly.304

This type of response is typical of normative finance. The

standard procedure for discrediting empirical results that counter the most basic tenants of the

EMH/EMT is often to redefine descriptive reality as ‗anomalous‘. Again, as mentioned before,

redefining some contrary finding and/or adding a ‗risk factor‘ to account for it does not make it

any less real, especially when the reason for doing so is specious. Ignoring this last point, in fact,

in this case even the small-cap issue is largely wrong, if not misstated. Specifically it is now

accepted that there is long-term mean reversion in many or most financial series (especially

stocks, see, e.g., Fama and French (1988)), and as it turns out most, if not all, of this reversion

happens in January (see Jegadeesh (1991)). Therefore, we now accept that many markets are not

even ‗random walks‘, but that especially in the case of equities much or all of that predictable

reversion to the mean (call it overreaction unwinding) happens in January for the market in

general (i.e., whether they be small-, medium-, or large-capitalization stocks). Thus, not only

does the ‗overreaction effect‘ appear to be real as well as it appears to mostly happen in January,

but it seems to be part of a larger pattern that is difficult to reconcile with normative finance

theory, even the portion that is evolving incongruously with the strong evidence against it.

Before proceeding to overreaction, I find it useful to linger on the issue of violations of not just

Bayes‘ rule (which motivated the De Bondt and Thaler (1985) study), but additionally the

axioms of utility (i.e., the Savage and Von Neumann & Morgernstern axioms), and other related

304 Of course, the effect itself can never completely be ruled out (i.e., unless the ‗January effect‘ is eliminated as anormative finance ‗anomaly‘). In fact, Zarowin (1990, p. 121) stated: ―While the well known January phenomenonmay be responsible for this result, we cannot completely rule out investor overreaction as an explanation‖.Therefore, the article is purported to show that ‗overreaction‘ is not the cause, yet it cannot be shown (i.e., unless the‗January effect‘ itself is not real). Thus, the article claims to show something that even the author claims is largelyimpossible to show.




‗rules‘ or ‗axioms‘ in finance and economics. Clearly, with respect to finance and economics

these are hardly rules or axioms. That is, they are clearly not physical laws in the realm of

economics and finance. Furthermore, these so called axioms or rules have been shown repeatedly

not to hold in many, if not most, cases. The primary reason I mention such things and expend

some energy explaining them is because:

1. Almost all finance theories and models implicitly or explicitly rely on them (i.e., at least

most or all those listed in standard textbooks at this time).

2. This reliance isn‘t just superficial. That is, if these do not hold then, outside of a

philosophical argument (i.e., faith) one cannot state the theories or models apply.

Therefore, it is not an overstatement to suggest that if Bayes‘ theorem/rule and the basic axioms

of EUT do not hold (which empirically they do not seem to hold for most humans),305 then the

theories and models upon which they serve as a foundation do not hold. This would be analogous

to a house in which there is no foundation, plumbing, electrical wiring, insulation, etc. In short,

you can live there but it is not much of a house, and may only be called a house because you

alone call it one.

305 In addition, if prices are not determined at all times and all markets at the margin by traders for which they hold(which empirically they do not seem to be determined in that way) or ―cancelation‖ happens (which empirically itdoes not).




UNDERREACTION – THE EXAMPLE OF EARNINGS ANNOUNCEMENTS – MOSTLY IN

THE SHORT-RUN

Following some four years after De Bondt and Thaler‘s 1985 article, in 1989 an academic article

came out that also created what I consider to be an odd combination of somewhat immediate and

emotional rejection as well as a general denial among many finance academics. The effect came

to be known as ‗earnings drift‘. The article was by Bernard and Thomas (1989) and was justified

as a means of testing whether equity earnings were embedded appropriately (i.e., from an

efficient markets‘ perspective) into stock pricing. In addition, this is another way of testingwhether Bayes‘ rule is violated. Although from an EMH/EMT perspective, this was primarily a

test of ―semi-strong‖ market efficiency. That is, are earnings estimates (publically available

pieces of information) quickly and accurately embedded into equity prices? Answer: No. But

unlike the De Bondt and Thaler (1985) result, it appears that this is due to underreaction, not

overreaction. That is, with underreaction pricing in the equity market generally is slowly

catching up vs. overreaction where pricing generally went too far then drifted or reverted back.

More importantly, Bernard and Thomas‘ (1989) article on stock market underreaction/earnings

drift has withstood most, if not all, of the EMT proponent attacks mounted against it.




Source: Bernard and Thomas (1989, p. 10).




Their method for detecting deviation from earnings announcements was based on Foster (1977).

It revolves around producing something called Standardized Unexpected Earnings or ―SUE‖.

This involves producing a statistical forecast of earnings. ―The difference between actual

earnings and the forecast is scaled by the historical standard deviation of the forecast errors to

arrive at the SUE. For a given quarter, a firm‘s SUE is then compared to the distribution of all

sample firms‘ SUEs from the prior quarter to place the firm in a decile portfolio. Abnormal (size-

adjusted) returns for each portfolio are then cumulated beginning the day after the earnings

announcement to estimate the post-announcement drift.‖ (Bernard and Thomas (1989))

The last diagram (i.e., Figure 2) shows that post-announcement earnings abnormal returns

increase monotonically across the ten SUE decile portfolios. In other words, as we move from

those firms that are forecast to have the worst SUEs (decile 1) to those with the best SUEs

(decile 10) the lines stay in the same order. Normatively (i.e., according to the EMH/EMT) we

would expect that on and after the announcement (i.e., on and after time 0 or the announcement

date) the lines should oscillate around zero, but instead they do what they shouldn‘t do (i.e.,

based on normative finance theory). Over the sixty trading days subsequent to the earnings

announcements, firms with extreme good earnings news (i.e., SUE decile portfolio 10)

experience a mean abnormal return of nearly 2%, while firms with extreme bad news (i.e., SUE

decile portfolio 1) experience a negative abnormal return of approximately the same magnitude.

(Bernard and Thomas (1989)) Therefore, going from SUE 1 to SUE 10 generates about 4.2% of

abnormal returns over 60 days (i.e., or about 18% on an annual basis). The general thrust of the

effect is that the Cumulative Abnormal Returns (―CARs‖) for ―bad news‖ firms continued to




drift down, while for ―good news‖ firms they continued to drift up after the announcement of

earnings.306 In addition, larger abnormal returns can be gained by altering the research design in

certain ways (see the next diagram).

306 It is important to note that f orecast errors should not be autocorrelated (i.e., in an ―efficient market‖), but as youcan see they are. These types of studies are strong and direct evidence that ―stock prices reflect naïve earningsexpectations.‖




Source: Bernard and Thomas (1990, p. 323).

While generally firms seem to display ‗earnings drift‘, or underreaction to basic earningsinformation, small and medium sized firms show more of the drift/momentum effect.

307

Therefore, one could increase the apparent ‗free lunch‘ or excess returns by skewing toward

smaller firms. In addition, it appears the effect doesn‘t completely disappear until six to nine

months out (i.e., compared to their original study where they showed only sixty business days, or

about three calendar months).

As the last diagram shows, much of the effect happens within the three days after each quarter-

end announcement (notice the upward jumps at quarter ends). Additionally, the magnitude of the

effect is declining each quarter and then reverses in the fourth quarter. Furthermore, the first

quarter encompasses over one-half the effect, and, as mentioned, it is monotonically declining

across size of firm (i.e., small, medium, and large). Clearly, this market-efficiency ‗anomaly‘ is

rooted in a failure of information to flow completely into price.308

It is important to note that the Bernard and Thomas (1989) study was not the first academic

reference on this issue of ‗earnings drift‘. Claims of incomplete initial stock price reactions to

307

Therefore, both the magnitude and length of the drift/momentum effect is larger and longer, respectively, forsmaller firms. Therefore, instead of about 4.2% difference over 60 days between decile 10 and 1 SUE portfolios, ―acombined long position in SUE portfolio 10 and a short position in SUE portfolio 1 generates an abnormal return of approximately 10%, 9%, and 4.5% for small, medium, and large firms, respectively.‖ (Bernard and Thomas (1990))Also, ―the drift is about 50% larger when SUEs are measured relative to analysts‘ forecasts rather than the statisticalforecasts used in Figures 1 and 2 (Freeman and Tse (1989, Table 7)).‖ 308 Even Bernard and Thomas (1989, 1990) were somewhat perplexed by what they found and especially theearnings analysts themselves. They seem to almost be asking things like who would hire these people, and how such‗professionals‘ like these could exist in a competitive environment? My response would be that it is hardly acompetitive environment and possibly read the chapter on agents/actors in financial markets.




accounting earnings have existing at least since the late 1960s (probably Ball and Brown (1968)

were the first). What was unique was that their solid ―underreaction‖ documentation is a

relatively recent event. Some of the key studies in the area are Rendleman et al. (1982), Bernard

and Thomas (1989, 1990), Freeman and Tse (1989), Mendenhall (1991), and Wiggins (1991). As

a group, these studies im ply that: ―post-announcement drift arises because stock prices fail to

reflect fully what current earnings imply, on average, about earnings in subsequent quarters.‖

Hence, predictable return patterns, and simple earnings information is not fully and accurately

reflected in pricing.

In contrast to the seemingly overwhelming evidence of underreaction to earnings, there is some

evidence of ―overreaction‖ in the case where earnings and price changes were reversed (e.g.,

inferior earnings and price changes were followed by superior earnings and price changes). Thus

there appears to be evidence that when market participants are subjected to earnings (and other

accounting type information) where extreme changes in the information occur (e.g., reversals),

they may overreact; while normally they tend to underreact (see De Bondt and Thaler (1987),

and Ou and Penman (1989)). From a behavioral finance standpoint, is this reversal shift to

overreaction truly overreaction or underreaction (Chopra et al. (1992) support De Bondt and

Thaler (1987) who find it akin to overreaction)? Either way, it doesn‘t look good for the ―semi-

strong‖ form of market efficiency.309

309 Bernanke and Kuttner (2005, p. 1254) suggest that ―further exploration of the link between monetary policy and the excess return on equities is an intriguing topic‖.




Although a bit confusing at times, most of the underreaction literature strongly supports the

notion that information is not embedded in pricing as expected by normative theory; in addition,

it should be mentioned that, and consistent with both the overreaction and underreaction effects,

there is a fair amount of evidence on overreaction to certain reversals and/or specific accounting

information. Regarding underreaction specifically, most of the research has focused on earnings

themselves, but some have examined other phenomena. Earnings analysts underreact to recent

company earnings, and may represent about half of the underreaction effect (Abarbanell and

Bernard (1992)). Stock splits tend to show drift or underreaction (Ikenberry and Ramnath (2002)

find about 9% price drift after stock splits). Momentum is found in stock prices of about 12% per

year for about three to twelve months (Jegadeesh and Titman (1993)). After dividend decrease or

elimination announcements there is a tendency for negative drift up to about one year afterward

(see, e.g., Liu et al. (2008)); and both dividend omissions and initiations show strong drift

afterward for up to one year (Michaely et al. (1995)). Taken as a group, the evidence in favor of

underreaction is both broad and compelling.




OVERREACTION AND UNDERREACTION IN THE SAME MARKET AT THE SAME

TIME – AN EMH/EMT PROPO NENT‘S WORST NIGHTMARE

With ‗earnings drift‘ we have well documented underreaction over about six to nine months (two

to three quarters)310, while with overreaction we have well documented return reversal out to

three to five years (twelve to twenty quarters). Therefore, because of the general timing

differential between underreaction and overreaction we can have both occurring in the same

market without any significant ‗cancelation‘, arguably the EMH/EMT proponent‘s worst

nightmare.

311

In fact, all the original evidence concerned the U.S. equity market. Given, that at

least at the time of the studies largely confirming underreaction and overreaction, the U.S. equity

market was arguably the most liquid equity market, is it not reasonable to suppose that if U.S.

stock market was not weak- or strong-form efficient, the others were not likely to be either (i.e.,

just based on these two sets of indirect tests)?

The existence of overreaction and underreaction suggests that not only is it possible to earn

excess returns based on mean reversion (i.e., due to overreaction), but also to possibly combine

that with momentum (i.e., due to underreaction).312 There is nothing in normative theory that

would seem to counter combining the ‗free lunch‘ of overreaction/reversal with

310 In fact, the evidence across all studies clearly shows that for at least the first two or three quarters (t + 1, t + 2,and t + 3) after an earnings announcement there is autocorrelation, but it is declining over time; and theer seems tobe modest reversal in quarter t + 4 and maybe in t + 5. Thus, as we move beyond three quarters we begin to see veryslight evidence in favor of overreaction/reversal, even for earnings. Therefore, beyond three quarters strong‗earnings drift‘ tends to turn into mild earnings reversal. 311 There are now several descriptive theories incorporating overreaction and underreaction (e.g., see Barberis et al.(1998) and Daniel et al. (1998)).312 See Kihn (2006) regarding the practical possibility of combining momentum/underreaction withreversal/overreaction to enhace expected risk-adjusted returns.




underreaction/momentum. The larger question or questions seem to revolve more around cause

than effect.

Regarding causes, at this time, it is far from clear. For example, overconfidence has been

mentioned as a possible reason for underreaction. Scott et al. (1999, p. 56) state: ―the momentum

effect may actually have more to do with an overconfidence bias.‖ Therefore, they see it as a

cause, or at least partial cause, of underreaction. In addition, Odean (1998, p. 1916) states:

―When there are many overconfident traders, markets tend to underreact to the information of

rational traders. Markets also undereact to abstract, statistical, and highly relevant information

and overreact to salient, but less relevant information.‖ Therefore, generally it causes or at least

helps to cause underreaction to relevant information like earnings, but it may also cause

overreaction to other types of information like fundamentally meaningless news headlines.

Regarding the EMH/EMT standard explanation/excuse that the cause may be ‗time-varying risk

premia‘, or just misspecified risk itself, this is unlikely to be more than a very minor cause, at

least with respect to earnings drift/momentum. Bernard and Thomas (1989) checked and found

that about 8% to 13% of the drift/momentum effect could be due to misspecification of beta or

the CAPM. Regarding the possibility of macro type risk being neglected, the effect has been

found across large segments of time, and up or down market conditions, etc.313

, and Arbitrage

313 If there were macro events (e.g., a war, etc.), then this should be picked up in the effect being magnified in one ortwo key quarters, which wasn‘t the case. And if ―mean raw (total) returns on extreme bad news stocks were so lowas to raise doubts about whether declines in risk of any kind could plausibly explain their magnitude. Specifically,the raw returns were less than the Treasury bill rates during the week after the earnings announcement, and wereonly slightly greater than the Treasury bill rates during the first two months of the post-announcement period.‖ …Bernard and Thomas (1989) CAPM Theory would almost suggest that only under special circumstances (that do notseem to hold in this case) would this be possible (specifically, they would have to offer some peculiar hedging value




Picing Theory (―APT‖) type macro risk factors do not signif icantly alter the basic result. In fact,

the results seem to be so strong and stable across different specifications with respect to time and

method, that ‗risk misspecification‘ and/or ‗time-varying risk premia‘ are unlikely to be the

reasons for the effect, at least regarding earnings drift/momentum.

Regarding the overreaction/reversal effect, it also seems unlikely that standard EMH/EMT

retorts encompass the cause of the effect. Although, the original study by De Bondt and Thaler

(1985) formed ‗winner‘ and ‗loser‘ portfolios based only on past returns, other studies have usedmore fundamentally based accounting values and achieved even greater excess returns. For

example, Ou and Penman (1989) form portfolios on the basis of a ―Pr measure‖ (i.e., a

probability measure based on fundamental analysis estimated using historical data). Bernard

(1992, pp. 20-21) notes that their measure ―represents an estimate of the probability of an annual

earnings increase in the coming year, based on a function of financial statement variables

identified and estimated using only historical data. A key factor contributing to the success of Pr

as a predictor of future earnings changes is mean reversion in earnings scaled by equity. Firms

with recent earnings declines have high Prs and subsequently increasing earnings; the opposite

earnings patterns for low Pr firms. In this sense, the high (low) Pr firms correspond to De Bondt

and Thaler‘s losers (winners).‖ In effect, both De Bondt and Thaler (1985) and Ou and Penman

(1989) end up with directionally similar results, but derived based on different sets of publically

which would mean they could offer below the risk-free rate in equilibrium). This is very, very unlikely, if notimpossible. The only realistic possibility is if the results were specific to this thirteen year time period, which isunlikely and related to the macro events possibility.




available information. Regardless, normatively one should not be able to use either past returns

and/or past accounting information to generate excess returns years into the future.314

It is difficult not to conclude that both overreaction and underreaction occur. For earnings,

underreaction with respect to the specific context of the event (i.e., the specific questions of

equity analysts, large companies, and accounting earnings announcements and the games

analysts play with claiming to forecast them). When the information is more complex,

overreaction may tend to rule. For example, if, regardless of earnings, complex and noisy

information comes out about IBM which is mostly negative and seemingly very salient, the stock

may be unjustifiably hammered (i.e., not based fully on fundamentals), and in some cases all

stocks associated might be affected to lesser degrees. The analyst case is amusing in that it is

easy to identify who is who, whereas in most other cases it tends to be much messier. In

summary, they are different things, one is well specified and the mechanism is clearer, in the

other it is more extreme and a less controlled environment.

Finally, and as noted by Block (1999), earnings are arguably the most watched and analyzed

number currently and in the history of the financial markets, yet they are not ―efficiently‖

imbedded into pricing (i.e., in the Fama (1970, 1991) sense). Therefore, if earnings are not

―efficiently‖ embedded into pricing, why would we expect other much less known and much less

314 Note that Holthausen and Larcker (1992) show the Ou & Penman (1989) strategy performs poorly after 1983, thelast year of the Ou & Penman results. Although, when they change the model to reflect predictions of stock returns,the result comes back. In addition, Ou & Penman (1989) seem to capture a long-term risk shift vs. a transitory one(i.e., compared to De Bondt & Thaler‘s (1985) results, which seem to be corrected by a return to more fundamentalpricing). Furthermore, their results work for up to six years without a reduction in effect (see Stober (1992)), whilethe De Bondt & Thaler (1985) results generally decrease each January for three years.




commonly accepted information to be reflected into pricing in an ―efficient‖ manner? The likely

answer directly relates to information and pricing (i.e., in this case the most watched simple

information is not embedded into pricing in an ―efficient‖ manner). If this is true, what hope do

we have for less followed and/or more complex information? Given that earnings are arguably

the most researched, most followed (by both institutions and individuals), most speculated upon

(by both institutions and individuals), easiest information to obtain, etc. fundamental values in

finance, how couldn‘t they be ―fully reflected‖ in pricing?

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Chapter 11: Chapter 11315

―Effectively, once a business has been born it is either acquired or liquidated. Most businesses

end their lives being liquidated. Regarding firm mortality, it is more a question of when, not

whether, a firm will die.‖

Kihn (1996b, pp. 21-22)

Again, effectively practically all firms end their existence by either going bankrupt or being

acquired, and most bankruptcies end in some form of liquidation.316 Therefore, given this

common lifecycle of businesses where financial death of some form is almost inevitable, what

happens during and after bankruptcy and insolvency is critically important. If the agents in the

financial markets don‘t respond in a normatively efficient fashion during identifiable periods of

micro or macro financial distress, when would they be expected to respond in a normatively

correct fashion?

From a normative EMH/EMT perspective, odd things seem to happen before, during, and after

bankruptcy. Even though corporate bankruptcy, and even restructuring, can be considered a

corporate event (which are dealt with in another chapter, i.e., corporate events), I felt that the

315 As an aside, it should be noted that many traditional financial theories and models (e.g., Modigliani and Miller(1958) on optimal capital structure) implicitely assume zero costs and/or zero probability of bankruptcy. Of course,there are bankruptcies and costs associated with bankruptcy (both direct and indirect, and they can be substantial).Therefore, according to traditional normative finance, this chapter (and some others) shouldn‘t even exist. 316 Most businesses that die don‘t formally file formal bankruptcy proceedings, they just close down. The intent of the U.S. Bankruptcy Code (also known as the ―Code‖) is to either rehabilitate or liquidate. Even thoughrehabilitation through, e.g., Chapter 11 proceeding in the case of corporations, is preferred, the norm is liquidation(especially for individuals and smaller legal entities).




event of bankruptcy, or more generally insolvency, was of a nature as to be best dealt with on a

somewhat standalone basis. In addition, there is the issue of the type of state of the world under

which distress occurs. Namely, is there something unusual about recessionary or depressionary

environments, which in turn are directly related to elevated levels of bankruptcy and insolvency,

to cause us to question the standard stories of normative market efficiency? In fact, there is.

THE EVENT OF BANKRUPTCY OR RESTRUCTURING

In the U.S., there are three primary chapters of the Bankruptcy Code under which institutions and

individuals file:

1) Chapter 7: The ‗liquidation‘ chapter of the Bankruptcy Code that results in the sale of a

debtor's nonexempt property and the distribution of the resulting net proceeds to

creditors.

2) Chapter 11: Typically applying to incorporated legal entities, the ‗reorganization‘ chapter

of the bankruptcy code commonly results in a debtor proposing a plan of reorganization




(e.g., it is standard to specify, relative to pre-bankruptcy claims, reduced payments to

creditors over time).317

3) Chapter 13: This typically applies to individuals with excessive debts yet regular income.

Under this chapter of the Bankruptcy Code, the debtor is allowed to keep their property

and pay their debts typically over a three to five year period.

4) Chapters 9, 12, & 15: Chapter 9 applies to municipalities (e.g., counties, cities, towns,

etc.). Chapter 12 applies to a ―family farmer‖ or ―family fisherman‖. Chapter 15 is a

relatively new chapter (i.e., as of the new 2005 code) that applies to cross-border

bankruptcies.318

Therefore, and even though numerically Chapters 7 and 13 are the most common, because of the

focus on larger corporations with exchange listed common equity, for our purposes the focus will

be on Chapter 11. For better or worse, most of the empirical research on bankruptcy or

insolvency in finance has focused on Chapter 11. More specifically, most empirical work in

finance use the event of filing under Chapter 11 of the Bankruptcy Code as an event in order to

test one or more hypotheses concerning market efficiency.

The following graph is presented to give some notion of the volume of bankruptcy filings in the

U.S. over the recent past. Please note that spikes occuring around the year 2005 are the likely

result of mostly debtors responding strategically to the 2006 implementation of major changes

317 There are several forms of ―Chapter 11‖ for corporations and like legal entities. For example, a ―prepackagedChapter 11‖ is one form of Chapter 11 where the bankruptcy plan is submitted in such a way that the time the legalentity is under ―bankruptcy protection‖ is typically limited and management retains full control. Often this form of Chapter 11 is called a ―reorganization‖, which tends to result in some confusion as most non-liquidationbankruptcies are also referred to as reorganizations.318 In should be mentioned that there are also provisions/sections of the Bankruptcy Code, for example, coveringpeople in the military, brokers, etc. that I have chosen to ignore in order to better keep focused on that which hasbeen studied in some detail, namely Chapter 11 of the Bankruptcy Code.




the Bankruptcy Code (i.e., mostly people tried to decalre bankruptcy under what would have

been a less onerous bankruptcy code prior to the beginning of 2006).

Source: American Bankruptcy Institute (http://www.abiworld.org/).

Again, in particular, the ―non- businesses‖ spike occurs in 2005, even though economic

conditions are substantially worse after that spike in individual bankruptcy filings.319

In terms of the major chapters under which most have filed:

319 It is expected that personal and corporate filings will show significant increases in 2009 and beyond.

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

B u s i n e s s F i l i n g s

N o n - B u s i n e s s F i l i n g s

U.S. Bankruptcy Filings (1980 - 2008)

Non-Business Filings

Business Filings

http://www.abiworld.org/







Source: American Bankruptcy Institute (http://www.abiworld.org/).

Again, even though Chapter 11 filings are relatively numerically small, their economic impact is

well beyond their numbers. Regardless, what do we know about Chapter 11 bankruptcies with

respect to issues of normative efficiency?

Total Chapter 7 Chapter 11 Chapter 13

1980 287,564 213,983 460 73,121

1981 315,805 226,595 1,109 88,101

1982 310,942 212,657 2,187 96,098

1983 286,432 196,205 3,032 87,195

1984 284,507 195,826 2,472 86,209

1985 341,215 237,637 2,975 100,603

1986 449,188 324,073 3,372 121,743

1987 495,542 362,605 2,778 130,159

1988 549,599 399,128 2,138 148,333

1989 616,206 439,127 1,970 175,109

1990 718,093 506,931 2,498 208,664

1991 872,416 617,342 3,195 251,878

1992 900,831 643,512 3,197 254,122 1993 812,864 568,390 3,018 241,455

1994 780,417 537,533 2,265 240,619

1995 873,642 597,048 1,369 276,225

1996 1,125,202 779,719 1,173 342,991

1997 1,350,118 957,117 1,071 391,930

1998 1,398,182 1,007,922 862 389,398

1999 1,277,095 890,919 712 374,232

2000 1,220,062 838,885 687 378,400

2001 1,454,031 1,031,493 783 419,750

2002 1,558,871 1,102,397 986 453,477 2003 1,607,623 1,156,274 930 466,585

2004 1,562,343 1,115,048 959 456,636

2005 2,041,219 1,631,011 877 407,322

2006 599,971 349,012 519 238,430

2007 822,590 500,433 617 320,720

2008 1,074,225 714,380 888 358,947









BANKRUPTCY PREDICTION AND MARKET EFFICIENCY

As it turns out corporate bankruptcy is generally predictable (e.g., Altman (1968, p. 609) claims

a high degree of predictive accuracy).320

Not only is it seemingly predictable (i.e., most are

predictable), but those predictions can be used to generate excess returns or the not so elusive

appearance of a normative ‗free lunch‘ (e.g., see Katz et al. (1985)).

Furthermore, it has been found that for large corporate bankruptcy filings investor reaction tends

not to be centered on the actual bankruptcy filing date but when the more popular media lists the

bankruptcy (see Dawkins and Bamber (1998)). Apparently, at least according to Dawkins and

Bamber (1998, p. 1149), ―most of the market reaction does not occur on the bankruptcy petition

filing date when the information becomes publicly available. Rather, most of the reaction occurs

when news of the bankruptcy filing is more widely disseminated via the Broadtape.‖ In other

words, most investors don‘t react to the public filing; they wait for the more public media

announcement.321 Dawkins and Bamber (1998, p. 1162) suggest that the lack of basic

information efficiency ―is consistent with investors finding that it is not cost-effective to closely

monitor various jurisdictions for news of bankruptcy filings.‖ This excuse is absur d. Given the

size and value of at least their common stock at the time of filing, it is in fact economically

viable to search for such filings. The fact that most investors don‘t is an additional curiosity that

strongly contradicts the most basic notions of informational efficiency.

320 Note, bankruptcy prediction was not included explicitly in the ―the issue of predictability‖ list. Also, and thoughnot a focus for this chapter or the book more generally, individual bankruptcy is predictable. Essentially, once anindividual of firm reaches an insolvent state, its odds of formally declaring bankruptcy increase.321 Of course, sometimes those two dates coincide.




Finally, I should note that by varying, for example, the periodicity of the data and filters used it

seems to be often or always possible to obtain a result superficially supportive of normative

market efficiency (see, e.g., Morse and Shaw (1988)).322

Specifically, for example, by

introducing excess variance into the test you will tend to reject your null hypothesis, and

coporate bankruptcy is no exception to this general rule.

BEFORE, DURING AND AFTER BANKRUPTCY

Given the lack of concern for informational efficiency, how bad can ignoring the actual filing

date be? Clark and Weinstein (1983, p. 497) find that for their sample of common stocks, on

average and after controlling for risk, prices drop about 48% around the three day bankruptcy

announcement period (i.e., centered around the announcement date). That is, abnormal returns

are about -48% for three days. I would guess that most people would consider this is

322 Morse and Shaw (1988) use monthly data and throw out many firms due to their peculiar filtering technique orapproach. In addition, they find some evidence that the return generation process for the common equity of bankruptfirms has changed after the Bankruptcy Reform Act of 1978. Thus by focusing on monthly returns and using asample skewed by post 1978 firms (i.e., in addition to their filtering approach and event study methodology), theyare able to reject their null hypothesis of market inefficiency. In particular, they dropped firms that lacked certainaccounting data (see Morse and Shaw (1988, pp. 1197-1201)).




economically significant. Clark and Weinstein (1983, p. 504) conclude by stating: ―Based upon

these findings, we conclude that bankruptcy filings convey important unanticipated information

to the market.‖

Given that bankruptcy can be predicted fairly well, what about the period preceeding

bankruptcy? In other words, based on normative notions of market efficiency, we shouldn‘t see

any abnormal returns well in advance of a bankruptcy filing. Aharony et al. (1980, p. 1014) find

that: ―a significant negative cumulative differential portfolio return starting roughly four years

before bankruptcy. The unexpected deterioration in the bankrupt group was high, with investors

having to continuously adjust for declining solvency over about a four year period.

Investors were apparently surprised up to the time of the bankruptcy.‖ Therefore, investors

were surprised well before bankruptcy, yet most large bankruptcies are clearly obvious well in

advance (i.e., they were predictable). Even more problematic for normative market efficiency is

that investors seem to adjust slowly. They appear to never quit catch on to the impending

bankruptcy.

Thus far we know common stock investors are surprised before and at the bankruptcy filing, but

what about after the bankruptcy filing? For the day after the filing, Dawkins and Bamber (1998,

p. 1151) find an abnormal return of about -16%.323

Alright, so many investors are surprised by

the filing itself, but what about well after the filing date? It should be noted that it is rather

difficult to specifically define the period after a corporate entities‘ Chapter 11 filing. In fact,

323 For comparative purposes, Dawkins and Bamber (1998, p. 1158) find a significantly negative return of -12.24%on the filing date and -15.97% the day after the filing. Therefore, there is a larger reaction after the filing.




many, if not most, eventually turn into some form of liquidation. Therefore, many cases (even

some larger ones) go from being classified as Chapter 11 to Chapter 7.324 Thus, which are we

talking about, Chapter 11, Chapter 7, both, some combination, etc.? There is a relatively large

literature on the cost of bankruptcy (both 7s and 11s), but the costs associated with each are

highly contingent on things like the type of bankruptcy and general economic conditions.

Therefore, the sample of bankrupt firms most academics study tends to be a biased sample of

large firms that enter and emerge from bankruptcy relatively quickly, not those that either

emerge only to enter bankruptcy again or are converted to a liquidation (either before or after

emergence from Chapter 11 protection). Therefore, the sample itself tends to be skewed toward

positive results, especially after the bankruptcy filing.325

In addition, historically financial academics have tended to imply that the ex ante and ex post

costs of bankruptcy are significant (e.g., see Altman (1984, pp. 1079 & 1082), he estimates

indirect costs alone, pre and post bankruptcy, to be around 20% and 17%, respectively). Thus,

and depending on method and significant characteristics & factors, bankruptcy is costly after the

filing (i.e., both direct and indirect costs).326

324 See, for example, Kihn (1996b, pp. 38-43). Most of what begin as Chapter 11 filings don‘t end as clearcutChapter 11s. In fact, based on the actual experiences of most bankruptcy courts, the samples used by standardfinance studies are very skewed toward what would be considered ―successful‖ restructurings. In short, on one levelit is a wonder that negative abnormal returns are found.325

Another issue is that earnings analysts typically avoid firms after they have declared bankruptcy (i.e., they tend todrop coverage). In fact, few, if any, follow a firm once it has declared bankruptcy. Although, given their lack of forecasting prowess, earnings analysts do not have sterling reputations with respect to providing useful informationfor investors, their relative disappearance from providing bankrupt and distressed company information compoundsthe already limited to nonexistent normative market efficiency for distressed firms.326 Also, it is important to point out that it was, and probably is, common received wisdom for financial academics tobelieve that there are economies of scale with respect to bankruptcy costs (see, e.g., Deis et al. (1995)). The originalarticle pushing this view was Warner (1977, p. 345): ―This evidence suggests that there are substantial fixed costsassociated with the railroad bankruptcy process, and hence economies of scale with respect to bankruptcy costs.That is, the larger the firm the smaller the relative costs of bankruptcy.‖ In contrast, Kihn (1996b) and Bris et al.




In summary, with respect to large corporate bankruptcies, the following can be stated with some

confidence:

1) As a general rule, bankruptcy is costly to common equity holders and the firm in general

prior to bankruptcy.327

2) Although largely predictable, many, if not most, investors are surprised both before and

during most bankruptcy filings.

3) The event of the bankruptcy filing itself tends to be costly to common equity

shareholders.

4) As a general rule, bankruptcy is costly to the firm itself after the bankruptcy filing.

5) Overall, and as a general rule, bankruptcy is costly to investors yet they seem consistently

surprised by it and its effects.

6) Bankruptcy is overall not supportive of normative notions of market efficiency.

Bankruptcy as documented by finance academics seems to puzzle those with a normative bent.

What about states of the world like recessions and depressions when bankruptcy and financial

distress generally rise? That is, is there anything normatively odd about more macro distress that

would cast suspicion on normative market efficiency? Yes.

(2006) do not find the commonly accepted economies of scale associated with bankruptcy. In fact, depending onvarious characteristics and factors, diseconomies of scale may dominate. 327 Although not addressed here, it is also likely that, as a general rule, bondholders are also economically andsignificantly harmed by bankruptcy (i.e., in addition to common stock shareholders). See e.g., Kihn (1996b).




PERIODS OF ECONOMIC DISTRESS – WHEN MANY FIRMS HIT THE SKIDS

(RECESSIONARY AND DEPRESSIONARY PERIODS)

Normative finance has seemingly continually modified theory to incorporate various ‗anomalies‘

over time and/or try to explain them away. For example, the original model used to control for

risk was of the form related to the CAPM (e.g., as used by Jensen (1968) and applied to a

portfolio). For example, it can be represented as:328

-=+(-

)+ , where for time t :

= return on the portfolio being analyzed,

= return on the ‗risk-free‘ asset (typically some short-term Treasury or government rate is

used),

= return on the ―market portfolio‖ (typically the value weighted or equally weighted

portfolio of all, or most, stocks is used),

= the ‗beta‘ or sensitivity of the portfolio (or security) to ‗market‘ movements, and

= the random error term.

The alpha ( is intended to represent the difference between the realized mean return of the

time series and its risk-adjusted required return (i.e., as determined by the CAPM). Therefore, a

positive or negative and statistically significant alpha is considered to be evidence of normative

(i.e., based on roughly a CAPM view of finance) inefficiency. That is, from an EMH/EMT

perspective any statistically significant deviation from zero is considered to be evidence against

328 What now follows regarding ‗factor‘ representations of normative models used to adjust for risk is largelyrepetitive of a part of a section in a previous chapter. My rationale for doing this is that I view this material assufficiently important as to largely repeat part of it, and add some nuance with respect to this section of this chapter.




market efficiency (i.e., <> 0). In addition, the ‗beta‘ or betas should tend toward unity (i.e.,

by normative definition ≈ 1).

More recently, the following two forms are commonly applied in the empirical finance literature

(Fama and French (1993) and Carhart (1997), respectively):

-=+(-

)+ (-

)+ (-)+ , and

-=+(-

)+ (-

)+ (-)

+ (- )+ , where

= return on small-cap (small capitalization) stocks,

= return on large-cap stocks,

= return on ‗value‘ stocks,

= reurn on ‗growth‘ stocks,

= ‗beta‘ or ‗factor sensitivity‘ of the portfolio to movements in excess returns associated

with firm size (typically proxied by common stock capitalization),

= ‗beta‘ or ‗factor sensitivity‘ of the portfolio to movements in excess returns associated

with ‗value‘ (typically proxied by the dif f erence in returns associated with ‗value‘ stocks relative

to ‗growth‘ stocks; and often proxied by such things as ―book-to-market‖ ratio), and

= ‗beta‘ or ‗factor sensitivity‘ of the portfolio to momentum excess returns (typically

proxied by recent returns on the portfolio itself, and often expressed as a lagged variable, and

typically lagged no more than one year).329

329 Of course, like the CAPM example the preceded these two, these are but one representation. In actual practicewhat are often used are more purely statistical ―factor analysis‖ representations.




As you can see, over time as certain ‗anomalies‘ became empirically overwhelming, the models

changed to incorporate the ‗anomalous‘ returns.330

Therefore, as can be seen from the above

form of ‗models‘ used in literally thousands of empirical studies in finance, over time certain

‗risk factors‘ were added. For example, given that smaller firms tended to provide excess returns,

by including a size ‗factor‘ you largely or totally circumscribe those excess returns to that

‗factor‘ (even if they have little or nothing to do with a ‗size factor‘). In effect, you can eliminate

that nasty ‗anomaly‘ (or associated anomalies) by defining it away and thus co-opting it, or any

related ones. Thus, by adding these ‗risk factors‘, whether or not being true ‗risk factors‘, one

can at least eliminate statistically significant alphas for such things as size, value, and momentum

(and any related ones). As stated previously, this type of activity does not settle the debate as to

whether the ‗anomaly‘ is truly anomalous, it merely hides it under the statistical rug, as it were,

but EMH/EMT proponents tend to act as if the case is settled.331

With the issue of statistical misdirection duly noted, why bring these equations up? One reason is

to specify what is wrong with blithely assuming all is well just because the ‗3-factor‘ or ‗4-

factor‘ model implies it is. Not only would that be wrong according to normative theory, but

incorrect descriptively. The other reason is because normative theory would suggest that the ‗risk

330 Ex post identification of ‗risk factors‘ doesn‘t mean they are wrong, but it is suspicious. 331 As mentioned before, I would emphasize that there are more plausible explanations for such ‗anomalies‘ as sizeand value. For example, limits to arbitrage and psychology (i.e., behavioral finance) could offer some insight thatseems to be lacking. For example, it is very likely that at times many investors value growth and value stocksdifferently than at other times. What EMH/EMT proponents might call a ‗risk factor‘ for size or value might actually be something more akin to a ‗characteristic‘ of the stock that investors might or might not value differently atdifferent times and under different circumstances, largely due to such things as the ―pillars of behavioral finance‖(i.e., limits to arbitrage and psychology).




factors‘ should behave in certain ways during certain states of the world, yet in fact they tend not

to. Thus, both normative theory and descriptive reality are against such ‗modeling‘ of ‗risk‘.

The bottom line issue revolves around the following question(s): Are these so called ‗factors‘

really risk factors in the true normative sense of the term, or are they mispricings, or both? That

is, even more specifically, are , , , and really ‗risk factors‘ at all? To refresh

our collective memory, remember EMH/EMT proponents began the debate claiming that

was the only normative ‗risk‘ worth taking account of. It was expected that this ‗market risk‘ wasall that should be controlled for in order to test for normative market efficiency. Then after the

evidence piled up, other ‗risk factors‘ were added in the mid-1990s, but with little or no even

normative justification (i.e., , , and optionally ). This alone is suggestive that

normative theory stuggles to fit the pieces together ex post. The analogy could be akin to

theorizing that all the planets and the sun revolve around the Earth, only to discover a few more

planets. It isn‘t that one cannot reconcile these new discoveries; it‘s just that doing so within the

context of an earthcentric theory becomes more difficult. In my opinion it would probably be far

easier to scrap the original theory and apply something more nuanced than the original theory but

simpler and more stable than the current evolving one (e.g., behavioral finance would be my

suggestion); but alas we know that humans are predisposed to denial. In my opinion ―Occam‘s

Razor‖ is no longer the guiding heuristic in finance, rather the need not to falsify the EMH or

EMT.




As stated by Daniel et al. (2002, p. 156-157): ―There is a factor associated with book/market, but

there is no clear evidence as to whether this factor earns a risk premium.‖ In other words, sure

investors seem to respond to book-to-market values (recall book/mkt), but what does that mean?

Therefore, for example, think of (because book-to-market can be a proxy for value with

high book-to-market value firms possessing more ―value‖ than low book-to-market value ones)

as measuring or at least proxying the extent to which investors value value. The problem isn‘t

that investors don‘t value value, it is that value itself may be more of a ‗characteristic‘ than a

‗risk‘, or it may change from being more of a characteristic then at other times more of a risk. If

it does that, then it cannot be a purely ‗risk factor‘ (i.e., as per the more recent evolving

normative theory). For behavioral finance, whether it‘s more of a ‗characteristic‘ or ‗risk‘

doesn‘t really matter, but for normative finance it‘s seems to be life or death (i.e., at least with

this seeming iteration of the normative theory). In short, it seems normative finance ala

EMH/EMT has backed itself into a kind of corner over its more recent explanations of what is or

is not ‗risk‘ and how those risks should behave over time; whereas behavioral finance hasn‘t, and

is compatible with the ‗risk factor‘ explanation, yet finds that explanation descriptively lacking.

Also, what is, for example, a book-to-market ‗risk factor‘ anyway? That is, I might buy into the

notion that ‗market risk‘ (e.g., ) should be priced, but what about book-to-market or the

month of January, etc.? At some point the notion that something like overreaction, underreaction,

S.A.D., etc. should be viewed as some purely normative ‗risk factor‘ approaches the absurd. Of

course, most finance academics don‘t even believe this view of the normative theory they teach

(see Welch (2000)). Thus, on the one hand text books and lectures teach about ‗risk factors‘ but




most academics, at least as of 1999, don‘t even accept that normative version of finance but

rather feel that these are types of things are best described as ‗characteristics‘.332

Furthermore, and as mentioned previously, it has been normatively theorized that these types of

‗factors‘ represent hedges against shifts in the investment opportunity set, particularly financial

distress. But, for example, if this was true why do so many that can invest in their own

company‘s stock do so (i.e., when normative diversification theory would advise them not to;

and it is doubtful they are hedging future distress risk, quit the contrary)?

The point of contention in academic finance is that as it has become more and more difficult, if

not impossible, to reconcile the 1970 version of EMT with empirical reality, a new and evolving

view has emerged in parallel with all perceived threats to its view (and related agenda). Once

again, note the following:

Patterns of return predictability can have alternative explanations, and not all

explanations are equally plausible (specifically, strictly ‗rational‘ vs. behavioral).

The behavioral approach is consistent with risk based factor premia (i.e., people do price

various forms of risk, but there are other psychologically based things going on,

specifically, psychological biases influence pricing). Actually, it was the original

EMT/EMH and the CAPM that lead the majority of academics and practitioners to

believe that there was only one risk worth taking account of, whereas the behavioral

332 Interestingly, yet the majority felt the markets were ‗efficient‘ and arbitrage free (see Welch (2000, p. 523)).Fortunately, psychology can help explain how and why humans can keep contradictory notions at the same time.




approach has always maintained that explaining what motivates investors is more

nuanced.

The mispricing of factors is consistent with those factors identified as new additions to

the evolving EMT asset pricing model (i.e., the ‗3-factor‘ model of Fama and French

(1993) or the ‗4-factor‘ model of Carhart (1997)). Therefore, the ex post identification

and subsequent addition of new risk factors to the evolving EMT asset pricing model

doesn‘t mean those factors are anything more than the mispriced factors they were

identified as being in the first place.

In particular, the cross-section of securities returns is very difficult to rationalize based

upon ‗rational‘ risk measures (e.g., size, value (again, e.g., book-to-market being its

proxy), and momentum). Again, the Fama and French (1993) and Carhart (1997) asset

pricing models may help to ‗explain‘ away a great deal of the mispricing or ‗anomaly‘,

but that doesn‘t mean it isn‘t caused by mispricing and/or is in fact mispricing. The

seemingly important question has been largely ignored. That is, are those factors true risk

factors?

Ignoring for a moment that they were added after the fact (ex post identification, which is

a type of theoretical specification search, ironically a claim made against others) and they

have no real theoretical grounding in economics, what is a momentum, value, or size risk

anyways? If those factors were truly risk factors then the factor realizations should

strongly covary with investors‟ marginal utility across states. Specifically, rational

asset pricing models would suggest that low marginal utility states (e.g., economic

booms) should be associated with high relative returns for ‗value‘ stocks and high




marginal utility states (e.g., recessions/depressions) should be associated with low

relative returns for ‗value‘ stocks. In fact, they either don‘t seem to move much or move

in the opposite direction as that expected by the newly evolved theory. Therefore, there is

little or no evidence to support the ‗insurance‘ theory proposed by the evolving theory

(see Cochrane (1999) on an outline of what is predicted by this type of theory).

The bottom line is that, short of extreme preferences not accepted by or incorporated into

any ‗rational‘ model, it is very dif ficult to explain the very high Sharpe ratios achieved by

forming portfolios based on size, value, and/or momentum. This is true no matter how

high the correlations between the returns of portfolios based on these ‗factors‘ and

innovations in macroeconomic variables (see Daniel et al. (2002, p. 152-153)), that is,

those studies with signs in the ‗right‘ direction.

In addition to there being little evidence to suggest these returns are correlated with

macroeconomic variables that might proxy for marginal utility, there is very little

evidence of size, value, and momentum returns being correlated across countries.

Therefore, forming these types of portfolios internationally would result in even higher

risk-adjusted returns with even more problems for the evolving theory.

Therefore, stay tuned for the next adjustment(s) to the theory.

―Taken together, this evidence seems to imply that a frictionless, rational model which would

explain this evidence would have to have very unusual (and perhaps implausible) preferences to

accommodate very large variability in marginal utility across states.‖

Daniel et al. (2002, p. 153)




In short, it is not plausible to explain asset returns (especially stock returns) across economic

booms and busts with the current version of EMT. It isn‘t just that viewing such things as ,

, , and as ‗risk-factors‘ makes little or no logical sense, it‘s that even viewing

them as such doesn‘t make much descriptive sense across time as economies boom and bust.

―Value stocks‖, for example, at least according to the recent normative EMT should not behave

the way they appear to descriptively behave.333 The critical point is that if ‗value‘ stocks return

more than ‗growth‘,334 then in a ‗rational world‘ this extra return is due to extra risk, and this

extra risk will become more clearly visible in the extreme negative states of the world (for

example, recessions). Furthermore, most of these types of risk are a kind of ‗insurance‘ against

bad states that people are rationally worried about when pricing assets (again, think recessions).

Therefore, during recessions ‗value‘ stocks should return significantly less than ‗growth‘ stocks.

In fact, they tend to return significantly more than ‗growth‘ stocks. Actually, this type of result

tends to be generally true of most of the ‗anomalies‘.335 Thus, not only are the ‗risk factors‘ such

as size, value, and momentum suspicious, but their actual behavior during economic downturns

is normatively contradictory toward EMT.

333 One study in partial support of the evolved EMT is Liew and Vassalou (2000) on ten countries and using book-to-market to predict economic growth (‗most‘ countries support the view that returns of a portfolio based on book-

to-market and size are positively associated with GDP growth). In my view the key is recession, not growth; and, inaddition, this is only the sign that superficially appears to contradict the contradiction, but not magnitude. Also, theLiew and Vassalou (2000) study funds contradictory evidence in Japan. Therefore, on a market weighted basis, thereis no support. Therefore, in summary, there are the following issues: (1) only growth is focused on and notrecessionary or depressionary periods (when it should be the reverse), (2) only the sign is the issue not magnitude(i.e., when magnitude should be critical), and (3) on a weighted basis there is no supposedly supportive evidence.334 Which may be part of the ‗good stock/good company‘ effect. 335 Also, note, much of this comes down to consumption risk (see Daniel et al. (2002, p. 152)). Lakonishok et al.(1994) present evidence in strong support of the behavioral view (i.e., they look at recessions).




FINAL TWO COMMENTS FOR CHAPTER 11

First, I caution that the reader should not take away from the discussion on bankruptcy the notion

that bankruptcy is either unusual as an event or generally supportive of normative notions of

market efficiency. Descriptively the reality is that, at least with respect to mostly empirical

equity research, the bulk of research is either damning of normative EMT or could be interpreted

within a behavioral framework. Regarding EMT, the reverse could be said, namely, the research

is mostly and typically strongly unsupportive and it is almost impossible to interpret that research

from an EMT viewpoint.

Second, and even though it could be its own chapter, we haven‘t discussed research on debt

during financial distress. Suffice it to say empirical research in that area is small relative to

equity research, yet the complexity is increased. This is typical in academically inclined finance

research. Equity data tends to be far easier to come by, and the bond or debt results tend to be

some version of the equity results. In fact, and viewed from an embedded option perspective,

bonds can be much more complex. For example, and ignoring liquidity, conceptually the

following is a simplified contingent claims or option view of the general equations of the five

types of securities commonly encountered in financial research (see Kihn (1996a)):

(1) = , where is the value of Treasury bond i and is the value of ‗risk-

free‘ bondi;

(2) = , where is the value of high-grade corporate bond i and is the

value of interest rate call option i;




(3) =

, where is the value of low-grade corporate bond i and

is the value of default or put option i;

(4) =

, where is the value of convertible

corporate bond i and

is the value of equity call option i; and

(5) =

, where is the value of equity security i.

Clearly from this perspective alone (and even ignoring cross-products or other interaction terms),

convertible bonds are potentially the most complex of the five security types just listed.

Furthermore, other debt securities can be more complex than convertible bonds (for example,

common tax-exempt or ―municipal bonds‖ – see, for example, Kihn (1996c)). On this basis

alone, equities are relatively simple. To avoid relatively limited empirical research and needless

complexity, distressed debt was ignored in favor of the more popular and numerous equity

research. Therefore, I have chosen to keep the focus on that evidence which is more plentiful,

accepted by EMH/EMT proponents, and simpler. Finally, like most, if not all, of descriptive

finance, regardless of including debt in the analysis on bankruptcy, the evidence would still be

most plausibly explained by behavioral finance in comparison to standard normative EMT.




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Bankruptcy‖, Journal of Finance, Volume 23, Number 4, September 1968, 589-609.

Altman, E., ―A Further Empirical Investigation of the Bankruptcy Cost Question‖, Journal of

Finance, Volume 39, Number 4, September 1984, 1067-1089.

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Chapter 12: Illusions

Illusions are one area where psychology and finance can meet head on. The classic illusion that

has the potential to affect all financial markets is something known as the ―inflation illusion‖.

Given that normative finance and economics assumes people can distinguish been nominal and

real values, to the extent they tend not to can have significant valuation implications. That is,

minimally through its impact on discount rates.

Normative theory typically suggests that something, for example, like stock prices should be

inflation neutral. More specifically related to finance, to the extent that agents in the financial

markets are normatively supposed to discount nominal cash flows with nominal discount rates

and real cash flows with real discount rates, yet do not, then we have a large problem indeed

(i.e., from a normative perspective). In fact, for example, in the case of stocks (and probably real

estate), they seem to skew toward discounting real cash flows with nominal rates.

The easiest way to break down the inflation illusion is to group by the two forces and four main

asset classes it seems to affect: (1) stocks & real estate, and (2) bonds & currency exchange

rates.336 Although, I would classify bonds and exchange rates are more of an indirect effect likely

working through ‗biased expectations‘. With stocks and real estate, the evidence points to

investors generally discounting earnings (real cash flows, or claims on real cash flows) with

336 Clearly these two are related, and not so coincidently short interest rates drive both the ―expectations hypothesis‖and actual fita currency based exchange rates and their forward rates (in one case they are hypothesized to directlyaffect long rates and in the other they actually seem to impact them more or less directly, although they impact bothdirectly).




nominal rates. This tends to cause stocks and real estate to be undervalued during inflationary

times and overvalued during other times. With bonds, the EH suggests that a steeply upward

sloping term structure of interest rates implies that bond investors expect that nominal rates will

be rising in the future. To the extent the term structure represents an unbaised forecast of future

nominal rates, then, according to a normative theory like the EH, no predictable pattern should

be revealed, yet there appears to be a pattern that suggests that the term structure is a relatively

consistent biased forecast of nominal rates (especially at extremes). Specifically, when nominal

rates are high and the term structure is steeply sloped, rates tend to go down, not up; conversely,

when nominal rates are low and the term structure is flat to downward sloping, rates tend to go

up, not down. Traditional or textbook normative theory would suggest the opposite of what we

observe in the actual markets. Finally, exchange rates essentially give us the case of the bond

effect in two countries at the same time. The short term to medium term driver of an exchange

rate between any two countries tends to be primarily driven through the relative yields of the two

countries. Normative theory would suggest that investors should be able to adjust two sets of

nominal rates (i.e., two countries nominal term structures) for the impact of relative inflation and

expected unbiased appreciation or depreciation of one currency relative to another. In fact,

currency investors/traders seem to hold relatively predictable biased expectations that indicate

they have systematic trouble accounting in an unbiased way for what the real difference between

any two currencies is. For example, it seems that high real yields in one country relative to

another tend to drive the relevant exchange rate in a relatively predictable way. If currency

investors were unbiased, such a simple differential shouldn‘t be predictive, yet it seems to be.




They all seem to have in common cognitive errors when trying to adjust for inflation and

inflation expectations, either in one market or two. Essentially the common thread isn‘t the lack

of the ability to adjust nominal values, it‘s more the tendency to make nominal evaluations of

financial securities (and even real estate), when the optimal normative evaluation would be real.

‘MONEY ILLUSION‘ – A BRIEF EXPLANATION OF THE BIAS OR NOMINAL VS. REAL

EVALUATIONS

Shafir et al. (1997, p. 241) explain that ‗money illusion‘ is ―a tendency to think in terms of

nominal rather than real monetary terms. Money illusion has significant implications for

economic theory, yet it implies a lack of rationality that is alien to economists.‖ Essentially,

Shafir et al. (1997) explain that money illusion is a problem rooted in framing. We tend to frame

most things nominally, and that leads to our normative undoing. That is, our natural tendency is

to ―bias toward a nominal evaluation.‖




One example of money illusion and framing by Shafir et al. (1997, pp. 351-353) is Ann and

Barbara, with the same college, same publishing jobs, but one year apart. Based on their two

hypothetical individuals, they asked three questions: (1) who is happier, (2) doing better in

economic terms, and (3) more likely to leave her position? Assume that both have $30,000

starting salaries. Ann begins her job first with 0% inflation and a 2% raise at end of year ($600

increase from $30,000 to $30,600), then Barbara begins with 4% inflation and 5% raise at end of

her first year ($1,500 increase from $30,000 to $31,500). As they enter their second year, who is

better off economically? Most people said that Ann was better off. When asked who is happier,

most thought Barbara was happiest. When asked who will look for a job and likely to take it,

most said Ann. The last question answer seems odd given the other two. Regardless, here are the

results:

Economic terms (N = 150):

As they enter their second year on the job, who is doing better in economic terms?

Ann: 71% Barbara: 29%

Happiness (N = 69):

As they enter their second year on the job, who do you think was happier?


Job attractiveness (N = 139):

As they enter their second year on the job, each received a job offer from another firm. Who do

you think was more likely to leave her present position for another job?





When asked to evaluate in real terms most correctly answered in real not nominal terms (71%

answered correctly), yet when asked about happiness they tended to associate happiness with a

nominal evaluation (64%). In addition, when framed in terms of job attractiveness, most used a

nominal evaluation (65%) and thought that Ann would leave. Therefore, it seems that people can

distinguish between real vs. nominal, but context is critical. The key is that even though people

can adjust for inflation with training/learning it is not natural to think that way. People think in

nominal terms and adjusting for inflation takes training and/or thought.

INFLATION ILLUSION – STOCKS (ACCEPTING THE MODIGLIANI-COHN

HYPOTHESIS) & REAL ESTATE

In an article published in 1979, Modigliani and Cohn (1979) hypothesized (call it the MC

Hypothesis or the ―MCH‖) that investors might irrationally discount real cash flows

(specifically, company earnings) using nominal interest rates. This sort of psychological bias

would tend to lead to inflation-induced valuation errors. If true, during periods when inflation is

expected to be high, stocks would tend to be undervalued; which would be in contrast to periods




when inflation is expected to be low, during such periods stocks would tend to be overvalued.

Therefore, if true, and contrary to normative theory, we should expect the current major

overvaluation of the U.S. stock market to shift to undervaluation as we will eventually shift from

low expected inflation to high expected inflation.

More recently, some have linked what is commonly referred to as the ‖Fed model‖ to the MCH.

The ‗Fed model‘ is usually based on the following: the earnings yield (i.e., the ratio of equity

earnings to price) should be approximately equal to nominal rates. If nominal rates are higher,

then stocks are considered overvalued, if nominal rates are lower then stocks are considered

undervalued. Asness (2003, p. 22) correctly points out that: ―The very popular Fed model has the

appearance but not the reality of common sense. Its lure has captured many a Wall Street

strategist and media pundit. However, the common sense is largely misguided, most likely due to

a confusion of real and nominal (money illusion). ... Now, as opposed to its failure for

forecasting long-term stock returns, the Fed model seems to be a success at describing how

investors actually set current market P/Es. There is strong evidence that investors set stock

market E/Ps lower (P/Es higher) when nominal interest rates are lower (and vice versa).‖ In fact,

if you track, for example, for the U.S. stock market earnings yield relative to the generic 10-year

Treasury bond yield over time the reader will see Asness‘ point quite clearly.




Certainly since the U.S. broke with original Brettan Woods exchange rate agreement (that is,

after the world became an exchange rate regime based soley on fiat currencies), the relationship

between nominal Treasury rates and the earnings to price ratio (also known as the ―earnings

yield‖) on the S&P 500 (which represents the majority of the U.S. equity markets capitalization)

has been close. Therefore, as a general rule, as nominal rates have risen or decreased so has the

earnings to price ratio. Again, and with respect to this graph, the heuristic or rule of thumb for

the ―Fed model‖ is that if the red line in the above graph is above the blue line then the S&P 500

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

9.00%

10.00%

11.00%

12.00%

13.00%

14.00%

15.00%

16.00%

A p r - 5

3

O c t -

5 4

A p r - 5

6

O c t -

5 7

A p r - 5

9

O c t -

6 0

A p r - 6

2

O c t -

6 3

A p r - 6

5

O c t -

6 6

A p r - 6

8

O c t -

6 9

A p r - 7

1

O c t -

7 2

A p r - 7

4

O c t -

7 5

A p r - 7

7

O c t -

7 8

A p r - 8

0

O c t -

8 1

A p r - 8

3

O c t -

8 4

A p r - 8

6

O c t -

8 7

A p r - 8

9

O c t -

9 0

A p r - 9

2

O c t -

9 3

A p r - 9

5

O c t -

9 6

A p r - 9

8

O c t -

9 9

A p r - 0

1

O c t -

0 2

A p r - 0

4

O c t -

0 5

A p r - 0

7

O c t -

0 8

S&P 500 Earnings Yield vs. 10-year Treasury Yield(April-1953 through July-2009)

E/P

10yr(Trs)




is considered overvalued, and if the red line is below the blue line then the S&P 500 is

considered undervalued. At the time of this writing, the red line is well above the blue line. In

fact, the earnings yield of the S&P 500 is the lowest it has ever been (yet Treasury rates are very

low).

There have been a number of studies on the MCH or related to it, and overwhelming support for

it.337 For example, Fama and Schwert (1977, p. 115) was one of the first academic pieces to

identify a ―most anomalous result is that common stock returns were negatively related to the

expected component of the inflation rate‖ over their study period (1953 – 1971). In other words,

even if stock investors expected inflation to increase they tended to ignore this and possibly

penalize firm real cash flow with nominal discounting. Combined, Ritter and Warr (2002) and

Campbell and Vuolteenaho (2004) largely rule out alternative explanations as well as strongly

support the MCH.338 In addition, these types of studies directly and indirectly suggest profitable

trading strategies based on this valuation bias (e.g., see Ritter and Warr (2002, pp. 47-49)).339

337 Of course, as with almost any empirical evidence in finance and economics, if you specify a special time periodor include or exclude a critical period, significantly alter your model and/or assumptions, you can often fail to notivea result or even reverse results.338 This, of course, hasn‘t stopped EMH/EMT proponents from developing ‗rational‘ models that seek to contain this

irrational response within the normative paradigm (see, e.g., Bekaert and Engstrom (2008)). It never ceases to amazethis author how deeply ingrained the need to mathematically operationalize even relatively clear irrationality intosome kind of rational model (as always the terms rational and irrational are used in the EMH/EMT sense of theterms). To me this response is just crazy.339 Also, see, for example, Boucher (2006, p. 211) where he states: ―Our results are rather in line with behavioralfinance that has identified a number of cognitive errors to which investors are susceptible. However, the reasons forwhich inflation makes investors more risk averse remains to be explained.‖ In short, he also finds significant excessreturns that are apparently available to anyone willing to key on this illusion, yet correctly points out that themechanics of what is causing it aren‘t exactly specified. But we strongly suspect it is wrapped up in humans‘susceptibility to inflation illusion.




Regarding real estate, the same forces that apply to stocks seem to apply to real estate (see, e.g.,

Feinman (2005)). The price of a home or just a raw piece of land should be equal to the present

value of the future cash flows derived from it. For residential real estate, that is typically

represented by the discounted net actual or theoretically possible rental income stream expected

to be derived from the residence. Therefore, the rent/price ratio is analogous to the earnings/price

ratio or earnings yield. In fact, there is a tendency for rent to price ratios, much like earnings to

price ratios for stocks, to vary with nominal rates and the level of inflation itself. Of course, this

is not in line with standard normative theory.

BIASED INTEREST RATE EXPECTATIONS OR NOT – BONDS

―If the attractiveness of an economic hypothesis is measured by the number of papers which

statistically reject it, the expectations theory of the term structure is a knockout. Most tests

beginning with Macaulay (1938) find no evidence supporting the expectations hypothesis. Many

cannot even reject statistically the alternative hypothesis that the spread between long and short

rates contains no information about future interest-rate changes. To make matters worse, in




U.S. postwar data, future long rates tend to rise when short rates are above long rates. Since the

expectations hypothesis would predict that long rates tend to fall, the theory often does worse

than even the naive model that future interest rate changes are always zero.‖

Froot (1989, p. 283)

As it relates to bills, notes, and bonds (i.e., across the term structure), the EH is the notion that

forward prices are unbiased estimates of expected future spot prices. Its original specification (as

laid out by Lutz (1940, p. 37), who in turn referenced Hicks (1939) and Fisher (1896) before

him) implies that ―the long-term rate as a sort of average of the future short-term rates.‖

Minimally, the risk-free or government spot rate curve is shaped by risk-free or government

bond market expectations about future nominal interest rates. Therefore, an upward sloping

curve generally suggests that that particular bond market expects that rates will be increasing, vs.

a flat curve where the market generally expects rates to stay the same, vs. a downward sloping

curve where the market generally expects rates to be declining.340

So why have a theory when very little evidence seems to support it? Because, in the land of

normative theory, if you torture the data and/or theory enough something good will come of it, or

at least it seems to warrant publishing. Chance and Rich (2001) correctly point out that two

issues/arguments render the theory incorrect: (1) if bond investors are risk-averse (i.e., not risk-

340 We could also call this the Pure Expectations Hypothesis or Theory of interest rates (―PEH‖ or ―PET‖, alsoknown as the Unbiased Expectations Hypothesis – ―UEH‖), but I find it more useful to stay focused on the moregeneral point concerning biased expectations rather than making refined mathematical or tautological points.Therefore, let‘s just stick with the EH and the more general notion of the shape of a term structure or yield curvegenerally reflecting expectations about future rates and the arbitrage argument contained therein.




neutral as per the theory)341

, and (2) the theory should incorporate a storage and/or cost of carry

component.342 They correctly note that (Chance and Rich (2001, p. 84)): ―Arbitrage provides the

linkage between spot and forward prices so that there is no more information in forward prices

than in spot prices. Indeed, ..., there is precisely the same information in spot prices as in forward

prices. Therefore, why would anyone look to the forward market any more than the spot market

for a prediction of future spot prices?‖ To that I say, indeed. Thus, why concern ourselves with

biased forward rates when spot rates show approximately the same thing? Therefore, I‘ll take it

one small step further and state, that for our purposes it is sufficient to show that the yield curve

itself is a biased predictor.343

Therefore, ultimately the EH comes down to the basic question of whether government yield

curves display bias in such a way as to suggest that bond market participants err in a systematic

way, if at all. There are many fine details and the evidence can be mixed, largely due to various

statistical methods, datasets, etc., yet the vast majority empirically reject the EH. Here is a partial

list of those generally rejecting the EH: Frankel and Froot (1987), Fama and Bliss (1987),

Campbell and Shiller (1991), Bekaert et al. (1997), Bekaert and Hodrick (2001), Clarida et al.

(2006), Sarno et al. (2007), and Della Corte et al. (2008). There is one notable exception to

341 Campbell (1986, p. 183) notes ―that differences among expectations theories are second-order effects of bond

yield variability.‖342 For me, for example, I am most worried about things like equating ‗risk-free‘ rates with government securitiesthat can be defaulted upon. I never cease to be amazed at academic focus on what seem to me to be ‗academic‘issues, while the fundamental driver(s) is/are largely ignored.343 Actually, Longstaff (2000a) makes it simple. Longstaff (2000a, p. 989) ―shows that all traditional forms of theexpectations hypothesis can be consistent with the absence of arbitrage if markets are incomplete. A key implicationis that the validity of the expectations hypothesis is purely an empirical issue; the expectations hypothesis cannot beruled out on a priori theoretical grounds.‖ In other words, it isn‘t theoretical and/or mathematical issues that matter,it is the extent to which empirically/descriptively yields of different maturities are related, and if so, in what way(s).Thus, the other arguments are probably ―much ado about nothing‖.




rejection, Longstaff (2000b) finds that repo rates from overnight to three months do not reject the

EH344; although, based on the same updated dataset and using a different method, Della Corte et

al. (2008) reject the EH. All in all, the EH has been rejected across the term structure, in many

different countries, and with many different tests. One could say it has been one of the most

rejected hypotheses in finance.

Therefore, the question isn‘t so much as to whether it is rejected, but why? Whenever an EMT

proponent feels threatened he or she will often respond that rejection of a favored hypothesis

must be due to ‗time-varying-risk- premiums‘. With respect to the EH this seems an unlikely

explanation and is not supported empirically (e.g., see Frankel and Froot (1987)). My guess is

that it may have something to do with limits to arbitrage and/or psychology, and what evidence

there is seems to support that notion.

For example, according to the EMH there should be no excess returns available, not for any

maturity. Froot (1989) finds support for the notion that the expectational bias is at least in part

due to underreaction of future expected long rates to changes in the short rate. These prediction

errors at least suggest profit making opportunities. Again, and as stressed by Shafir et al. (1997),

it isn‘t that people don‘t understand real vs. nominal, but framing in nominal terms is more

natural. Furthermore, it is not that peo ple don‘t learn, it‘s that they may learn very slowly. The

fact that there is a strong tendency for the forecast error to be predictable doesn‘t necessarily

connect it directly to the failure of the EH, but it is suggestive.

344 Actually, Longstaff (2000b, p. 397) states they are ―almost unbiased‖. Regardless, the results are in contrast tothe many studies before it.




De Bondt and Bange (1992) made a study of the yield curve and expectations about inflation.

They found that the forecast error has a ―strong predictable component‖ that tends to be during

periods of increasing inflation and during periods of decreasing inflation. This is supported by

other studies showing forecastable inflation that is largely not accounted for (see Barsky and De

Long (1988)). That is, De Bondt and Bange found an ―underreaction phenomenon.‖ It appears

investors put too much weight on historical rates, possibly excessively anchoring on them. This

seems to be a primary driver of the cause of the EH failure. Thus, what seems to be causing the

EH failure is at least in part due to inflation forecasting bias, which in turn is likely driven by

inflation illusion. Specifically, here are some of the key findings:

Confirming past research, there isn‘t a one for one change in nominal rates relative to

changes in expected inflation as normative theory would suggest.

There is a tendency for inflation forecasts to be predictably too high during periods of

declining inflation, and too low during periods of increasing inflation.

The slope of the yield curve is predictive of the error/bias. Therefore, by historical

standards, when the slope is steep investors tend to make too high an inflation forecast vs.

when the slope is relatively flat the tendency is to make too low an inflation forecast.

When the twelve-month-ahead inflation forecast exceeded the six-month-ahead forecast,

investors subsequently earned positive abnormal returns by holding long-term bonds.

Therefore, excess returns are forecastable off inflation misjudgment.

―The surveys give too much weight to inflation in the distant past, relative to recent past

inflation.‖ De Bondt and Bange (1992, p. 485) It appears to be excessive anchoring.




―Expectations are insufficiently adaptive: if the economists paid more attention to recent

inflation, and interpreted the prevailing rate as less of a surprise, they would not make the

same error repeatedly.‖ De Bondt and Bange (1992, p. 485)

―The expectations theory and investor rationality imply that, when long-term rates are

above short-term rates, long rates ought to rise. Correcting for the term premia, the

resulting capital loss equates expected holding period returns across assets. Similarly,

when long rates are below short rates, long rates ought to fall. However, in practice, the

opposite tends to happen. As seen in Table 5 (Panel A), the more the term structure is

upward-sloping, the more long-duration instruments outperform bills.‖ De Bondt and

Bange (1992, p. 489)

―The third and perhaps most noteworthy implication is that past survey errors— which get

repeated and predict the spread —also predict ex post term premia.‖ De Bondt and Bange

(1992, p. 491)

―This result suggests potentially profitable bond trading strategies.‖ De Bondt and Bange

(1992, p. 492)

―From the above discussion, we conclude that movements in term premia are partly

driven by inflation forecast errors. Interestingly, however, the yield spread does not lose

all its predictive power if past inflation forecast errors are taken into account.‖ De Bondt

and Bange (1992, p. 493)

―Thus, contrary to intuition, if the yield spread is a risk proxy, it would appear that long-

term instruments are less risky when inflation uncertainty is high.‖ De Bondt and Bange

(1992, p. 493)




―As with bonds, the stock market risk premium is not explained by inflation uncertainty.‖

De Bondt and Bange (1992, p. 494)

―Apparently, past inflation forecast errors predict future forecast errors in surveys, predict

future movements in real rates, and predict term premia on U.S. Government Bonds.

Even though the inflation forecasts fail standard rationality tests, movements in the yield

spread strongly reflect their variation through time.‖ De Bondt and Bange (1992, p. 494)

Overall, the results paint a picture of underreaction and forecastability. For example, when the

term structure is sloped more steeply than average, inflation forecasts are too high. In addition,

past inflation forecasting errors are positively correlated with future excess returns. Thus,

overall, the actual government yield curve and/or term structure over time doesn‘t represent the

world of efficient markets in the textbook sense.




BIASED EXCHANGE RATE EXPECTATIONS OR NOT – EXCHANGE RATES & THE

FORWARD DISCOUNT BIAS (OR TWO WRONGS DON‘T MAKE IT RIGHT)

Many of the tests of the EH are also tests of unbiased currency expectations (e.g., Frankel and

Froot (1987)). There is a clear linkage between the bond markets and exchange rate market both

theoretically and in practice. For example, there is a strong link (i.e., post Brettan Woods

dissolution during the early 1970s) between the relative real short rates between two countries

and their respective exchange rate (i.e., the short interest rates of two currency areas at least in

part seem to determine the exchange rate between them).

Although not reviewed here, I encourage the reader to read up on the background material for

exchange rate determination, both normative and descriptive. For example, the interest parity

condition emanates from the observation that the domestic interest rate must equal the foreign

interest rate plus (minus) the expected appreciation (depreciation) in the foreign currency (i.e.,

based on basic arbitrage conditions holding). Interest parity in turn is contingent on the

Purchasing Power Parity theory (―PPP‖) that states that exchange rates between any two

countries will adjust to reflect changes in the price levels of the two countries, which is simply an

application of the LOOP. Even though some goods are not traded across borders, LOOP rests on

the assumption that all good are identical in both countries and that transportation costs and trade

barriers are not significant. Forward discounts for an exchange rate between two currencies are

normatively supposed to represent unbiased forecasts of future exchange rate changes.

Descriptively they seem not to be (e.g., Froot and Frankel (1989)).




The equivalent of tests of the EH for exchange rates are tests of the ‗forward discount bias‘.

Specifically, is the forward discount an ―unbiased‖ predictor of the future changes in the spot

exchange rate? Given PPP, relative inflation is the expected and likely driver of exchange rates

and furthermore the likely driver of any bias. Much like the tests of the EH, most empirical tests

of the unbiasedness hypothesis reject it. This has left the more refined question of whether this

bias is evidence of a risk premium or a violation of rational expectations (or both, or neither)?

One of the most popular tests of forward market unbiasedness is a regression of the future

change in the spot rate (i.e., the actual realized change) on the forward discount (i.e., today‘s

forecast, of sorts). It seems that there descriptively is a bias in the systematic component of

exchange rate changes in excess of the forward discount. Essentially, if you assume risk

neutrality, then the empirical evidence violates rational expectations. The essential questions

reduce down to: Which is it, expectational errors or the EMH/EMT standby excuse of a time-

varying risk premium being responsible for repeatedly biased forecasts of the forward discount,

let alone the issue of whether the risk premium is more variable than expected depreciation?

Froot and Frankel (1989, p. 151) provide a helful visual into some key issues associated with the

forward disocunt bias.




Source: Froot and Frankel (1989, p. 151).

The focus here is on the so called ―risk premium‖ being able to explain the forward rate error. It

doesn‘t. As you can see, as the error moves the risk premium doesn‘t seem to change much. This

should be especially disconcerting normatively because the forward rate errors are smoothed.

Therefore, relative to the errors, the risk premium is a relative constant. Some highlighted Froot

and Frankel (1989) results are:

1. Primary finding: ―the systematic portion of forward discount prediction errors does not

capture time-varying risk premium.‖




2. Reject the hypothesis that none of the bias is due to systematic expectational errors.

3. Cannot reject the hypothesis that all of the bias is due to systematic expectational errors,

or the hypothesis that none is due to a time-varying risk premium.

4. (2) + (3) imply that changes in the forward discount reflect changes in expected

depreciation (on a one-for-one basis).

5. Reject the hypothesis that the variance of the risk premium is greater than the variance of

expected depreciation (seems to be vise versa).

6. The risk premium does not vary with the forward discount (i.e., it doesn‘t vary much atall, and cannot reject the hypothesis that it is constant).

Since the risk- premium isn‘t the driver (i.e., causality doesn‘t run through it), and the risk-

premium plus depreciation equals the forward discount, then changes in depreciation translate

one-for-one into changes of the forward discount. Given that the forward discount is biased, this

implies that whatever drives the expected discount is driving the bias. Overall, the findings imply

that currency traders have a tendency to underreact. Much like the a domestic bond market, when

it comes to currency exchange rates we likely have inflation illusion induced underreaction

except that for exchange rates the bias is compounded by the fact we are dealing with two

markets not one (or one set of markets), with similar bias dynamics likely happening in each one.

Again, as with individual bond markets, excessive anchoring may be the culprit or possibly a

piece of the puzzle.




A FEW FINAL THOUGHTS ON INFLATION AND FINANCE ILLUSIONS

An alternative title for this chapter could have been ―probable underreaction to changes in the

rate of inflation‖. Stocks, real estate, bonds, and exchange rates all seem to show some normative

underreaction. The cause of the underreaction is uncertain, but then again what is certain? It is

plausible, if not likely, that biased expectations and inflation are driving forces.

Oddly, that is from a normative perspective, all can be traced to an inflation illusion of some

kind. This should not be descriptively surprising. Given that finance is fundamentally concerned

with discounted present values, and discount rates are influenced by the ‗risk-free‘ rate; then, it

should be of some normative concern if inflation is not quickly and correctly being incorporated

into the discount rate. For stocks and real estate, the fact that their values tend to move as biased

inflation expectations underreact. For bonds, to the extent the yield curve or term structure of

interest rates is relatively steeply sloped foreshadows a higher likelihood of nominal rates

moving down, not up as expected. For exchange rates, the implied discount of the expected

depreciating currency tends not to show up, or show up to the extent expected. In each case the

biased expectations can at least in part be traced to or influenced by actual and/or expected

inflation.

Why do agents in the financial markets tend toward an inflation illusion? I began the chapter

with a brief discussion on the ‗money illusion‘, and that is as good an explanation as any. In

short, it‘s not that we cannot think in real and nominal terms, it‘s just that it‘s not typically

framed that way, and we may have a strong tendency to anchor as well. Also, and especially




among non-economists, we do not like inflation and tend to associate it with a lower standard of

living, loss of morale, damage to national prestige, etc. (see Shiller (1996)). Therefore, there is a

visceral dislike of inflation among many that may, at least in part, explain investors‘ tendency to

discount most values nominally, even those real values that do not deserve it.

Finally, much like overreaction and underreaction, illusions is a classic topic for this book

because it demonstrates the following principals:

(1) The markets are ‗inefficient‘ in the traditional finance textbook sense of the term. (2) Market participants almost assuredly are acting in ‗irrational‘ ways in order to

cause these types of effects. That is, the psychology piece of behavioral finance

seems obvious in these cases, although hard to prove.

(3) Although there appears to be a ‗free lunches‘ (i.e., as defined traditionally by

normative textbook finance), because textbook finance doesn‘t account for: (A)

realistic costs (e.g., transaction costs, taxes, brokerage costs, etc.), and (B)

realistic risk and/or characteristics, there may not be a ‗free lunch‘ after all. That

is, the limits to arbitrage part of behavioral finance seems obvious in these cases.

(4) It is also relatively clear that you can have inefficiency, yet little or no, for

example, ‗cancelation‘ (e.g., exchange rates).

Again, those are four common themes of this book and they all come together in this chapter.




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of the Term Structure of Bond Yields‖, Journal of Financial and Quantitative Analysis, Volume

42, Number 1, March 2007, 81-100.

Shafir, E., Diamond, P., and A. Tversky, ―Money Illusion‖, Quarterly Journal of Economics,

Volume 112, Issue 2, In Memory of Amos Tversky (1937-1996), May 1997, 341-372.

Shiller, R., ―Why Do People Dislike Inflation?‖, NBER Working Paper Series, Working Paper 5539, Cambridge, Massachusetts, April 1996, 1-75.




Chapter 13: Descriptive theories in finance

Up to this point, we have discussed at least two descriptive finance theories and their associated

hypotheses, namely one-half of the MCH (the other part relates to leverage) and SAD. Besides

those two, there are others. Some of the notable ones are:

1. Lee et al. (1991) on CEFs. and

2. Kahneman and Tversky‘s (1979) prospect theory (―PT‖).

These are good examples of analyzing what actually happens in the financial markets, and then

based on that hopefully objective analysis345 backing out a theory and/or hypotheses from what is

the descriptive reality of one or more financial markets. These descriptive theories are in direct

contrast to most textbook theories in finance and economics (e.g., the CAPM and Modigliani-

Miller), that is at least until recently.

Thus, I will review four solid descriptive finance theories on:

1. ‗inflation illusion‘ and stock prices,

2. expected utility theory (i.e., a behavioral alternative to),

3. CEFs, and

4. SAD and stock prices.

I find it a useful exercise to review some theories and associated hypotheses that seem to provide

a superior alternative to the largely normative theories and unfalsifiable hypotheses that occupy

most textbooks on the subject of finance. Of course, this is not a complete list, but at the time of

345 Remember, the EMH and EMT could be considered a descriptive theory that was not based on an objectiveanalysis of the facts (e.g., counter evidence was ignored, or worse, purposely hidden).




this book‘s writing they represent some of the better theories and associated testable hypotheses.

In short, and unlike the EMH/EMT, they are falsifiable and have not been rejected at the time of

this writing.

A TEMPLATE FOR DESCRIPTIVE FINANCIAL MARKETS HYPOTHESES

Although no template exists for descriptive financial market theories it seems rather likely that

there is a critical path, of sorts, that one would go through to develop such theories and related

hypotheses. That template might take the form:

1st Identify the seeming unusual pricing behavior to be explained; and identify the market or

markets it seems to impact, and any timing issues associated with the phenomena.

2nd Propose a theory that parsimoniously addresses as many of the phenomena as possible (ala

Occam‘s razor), hopefully all. Obviously, try not to make any unrealistic assumptions.

3rd Create one or more testable hypothesis; and test (i.e., apply standard western scientific

methods).




There is nothing special about such a path toward developing a theory and/or hypotheses, but it

seems at least somewhat incongruously unusual for ‗modern finance‘ and economics, at least as

of today.

MODIGLIANI-COHN THEORY & RELATED HYPOTHESES

In the chapter mostly concerned with inflation illusion, the Modigliani-Cohn (1979) Hypothesis

(―MCH‖) was introduced as an explanation for the seemingly odd way equities are valued as

inflation has waxed and wanned, in the U.S. in particular. Normative theory demands that real

returns should be unaffected by inflation, yet they appear to be affected by inflation. Again, it

was recognized that equity valuation seemed to be affected by ‗money illusion‘ when it should

not be affected by it. That is, it was this possibility of ‗money illusion‘ affecting pricing and its

impact on the equity markets that was of interest to the authors who wrote the article. One

combined quote from them is telling:

―The reader may ask: ‗Is it credible that investors have systematically undervalued equity values

for at least a decade, and are still undervaluing them by as much as 50 per cent, solely as the




result of the mistakes suggested by your analysis?‘ ... we readily admit that our conclusion is

indeed hard to swallow – and especially hard for those of us who have been preaching the gospel

of efficient markets. ... when the hypothesis first crossed the mind of the senior author some four

years ago, it was lightly dismissed as too preposterous to be entertained seriously. But over the

ensuing three years that hypothesis continued to provide the only seemingly useful clue to

market performance, and we finally succumbed to the temptation of undertaking the systematic

tests reported in this article.‖

―Confronted with overwhelming statistical evidence consistent with our error hypothesis, and nodirect evidence inconsistent with it, our original skepticism turned into a degree of confidence

approaching belief – and certainly high enough to justify placing our findings before the public.‖

Modigliani and Cohn (1979, p. 35, p. 36)

In other words, even they didn‘t believe their own basic conclusion as to causation, largely

because it went against the efficient market doctrine. Also, remember this was back in the mid-

1970s when they were kicking this theory around. Therefore, given the timing, and probably

unbeknownst to them, they may have developed one of the first truely descriptive behavioral

finance theories.

Regarding the theory itself, MC hypothesize that inflation causes investors to make two major

errors in pricing common stocks (Modigliani and Cohn (1979, p. 24)):




1. ―First, in inflationary periods, investors capitalize equity earnings at a rate that parallels

the nominal interest rate, rather than the economically correct real rate – the nominal rate

less the inflation premium.‖

2. ―Second, investors fail to allow for the gain to shareholders accruing from depreciation in

the real value of nominal corporate liabilities.‖

In other words, investors are hypothesized to: (1) discount real equity earnings by nominal rates,

and (2) largely ignore the impact of leverage. We have thus far focused on the first error.

Regarding the possibility of error related to improperly (i.e., from a normative perspective), for

example, accounting for inflation‘s impact on debt, Ritter and Warr (2002) do indeed find strong

support for the second hypothesis. They find that levered stocks are more undervalued than less

levered stocks during inflationary times and more overvalued during less inflationary times.

Therefore, both hypotheses receive strong support and continue to reflect what is observed in the

actual financial markets. Unlike the EMH or even the CAPM, this is a theory that has seemed to

grow stronger with testing over time.




Source of data: Federal Reserve Bank of St, Louis (FRED) – September 27, 2009 download, and Pinnacle Data for

the S&P 500 return series.

The above graph plots the rolling three-year covariance of two discount scenario series (real and

nominal) against S&P 500 quarterly returns (3-month returns). The values being discounted are

derived from a series that comes from the government called ―CPATAX‖ or ―Corporate Profits

After Tax with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment

(CCAdj)‖. In other words, these values are supposed to represent a version of all corporate cash

flows after taxes and adjusting for most non-cash depreciation. What I have done is to discount

CPATAX by my approximate version of a real and nominal rate with a duration derived from the

dividend yield of the S&P 500 index itself. The nominal rate is the ‗generic‘ 10-year Treasury

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C P A T A X n o m i n a

l a n d r e a l c o v a r i a n c e

w i t h t h e S & P 5 0 0

Do Stock Investors Tend to use Nominal or Real Discount Rates for Real Cash Flows?March-1957 through June-2008

CPATAX/Real

CPATAX/Nominal

10yrTrs




yield, while the real rate is the generic 10-year Treasury yield less the realized one year SGS CPI

inflation rate. Therefore, to arrive at a real rate of +5% would require that, for example, a

nominal 10-year rate of 10% is netted against actual measured CPI inflation of +5% for that date.

The nominal 10-year Treasury yields are shown by the blue line and are plotted against the right

side vertical axis. The other two lines represent the rolling three-year covariance of the CPATAX

discounted by the real rate (shown by the red line) and the CPATAX discounted by the nominal

rate (shown by the light green line). The important part to visualize is that any significant

movement away from zero by either the green or blue lines shows significant implied valuation

of the U.S. stock market (i.e., in this case proxied by the S&P 500) as either driven by real (i.e.,

the red line) or nominal discounting (i.e., the green line). What the reader will note is that only

the green line really shows any of these types of periods, and predominantly during very high or

very low nominal interest rate periods. In fact, the actual correlation (and yes, correlation, or in

this case covariance, does not mean causation, but here it is certainly suggestive) between the

S&P 500 return and the CPATAX real and CPATAX nominal is about -4% and +38%,

respectively. In other words, given the methodology, real discounting is statistically insignificant

but nominal discounting is highly significant. Therefore, at least in this example, even though

investors are normatively supposed to use a real discount rate, they instead seem to wrongly use

a nominal one (again, wrongly from a normative perspective).

Also, in addition, and although not directly intended to address real estate valuation or bonds,

this theory seems to apply directly or indirectly to those asset classes as well. Again, as

mentioned, Feinman (2005, p. 35) notes that at least one could argue that the first hypothesis




could be applied analogously to real estate where rental income can be imputed. In addition,

Miller and Shulman (1999, p. 45) state: ―in the presence of ‗money illusion‘ the correlation

between stock and bond returns will be abnormally high during periods of high inflation.‖

Therefore, another possible asset class affected via a potentially similar inflation illusion

mechanism, bond prices are likely more directly linked to stock prices as inflation increases.346

Overall, any theory like the MCH that is largely explained by ‗money illusion‘ will likely go

beyond normative mistakes in just equity valuation.

CLOSED-END FUNDS (―CEFS‖), AND IPO SIMILARITIES

Up until around the early 1990s, CEFs had puzzled normative finance academics. Specifically,

and not all being equally as important, there are roughly four factual parts of the ―closed-end

puzzle‖ and their related questions:

1. At IPO they are sold with a large commission (about 7%) at a premium (of about 5%),

then they under-perform over the short-run (by about 5% over the next 20 days), after

346 Although, one could argue that biased interest rate expectations already covers this likelihood.




which they then under-perform more (by a total of about 10% over the 120 days

following the offering – all toll, a grand total under-performance of about 25%) (see

Weiss (1989) on stock funds). Why do people buy them, and who are these people?

2. CEFs tend to trade at substantial discounts to their NAVs (e.g., around 10% or more

historically for many stock funds).347 Why aren‘t prices equal to their NAVs?

3. Discounts fluctuate greatly (even among groupings of funds), but they tend to co-vary

with other CEFs, and tend to shrink in January, but not for the stocks they own (see

Brauer and Chang (1990)). Why do they vary so much, and why don‘t they vary somuch?

4. When ―opened‖ the discount goes away. Why aren‘t they opened more often; and why do

some argue NAVs are mispriced, or aren‘t even the correct pricing formula?

Overall, CEFs seem to be a normative mess. There may be four general issues related to them,

but more than four questions associated with those general issues.

As a reminder, I selected a relatively recent ―hot‖ CEF in the energy area as a backdrop to basic

CEFs issues:

347 Pontiff (1995, p. 341) states: ―fund premia are negatively correlated with future returns. Funds with 20%discounts have expected twelve-month returns that are 6% greater than nondiscounted funds. ... Economicallymotivated explanations do not account for this effect.‖ In short, basic mean-reversion with little or no economicbasis drives significant excess returns.





As the reader can see, this energy stock CEF began at a premium of about 7%, then quickly went

toward discount, then several times crossed over to premium territory, then more recently to end

this graph at a discount of about 30% (i.e., an approximate average daily discount of 30% for the

month of September 2009). Again, normatively it is never supposed to deviate from the zero line.

In 1991 an article was published by Lee et al. (1991) that suggested there might be a reason for

the four ‗puzzles‘ surrounding CEFs. Their theory was called ‗investor sentiment‘, and they

posited that the underlying cause of all of these puzzles was individual investor sentiment. One

could call it a form of ‗noise trader‘ or irrational trader risk. In general, when individual investors

were excited about an asset class they tended to overdo it; one could even say they overreacted.

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Discounts/Premiums for the Kayne Anderson Energy Development Company Fund




For example, say if individual investors are excited about technology stocks, then the theory

would expect to see more technology share related CEFs be issued at premiums, and as that

positive sentiment eventually ebbed, then overall premiums would turn to discounts, etc.

Additionally, as more or fewer individual investors increased their holdings relative to

institutional investors you would expect discounts to shrink or expand accordingly. Thus, NAVs

are the correct pricing model, but investor ‗sentiment‘ will drive prices above and below NAV as

sentiment waxes and wanes, or until the CEF is opened and values converge to the NAV.

Consider this theory to emphasize irrational traders (often called ‗noise‘ traders), but in this casethe irrational traders are primarily individual investors.

Lee et al. (1991) actually checked to see if their theory fit the facts. Regarding CEF IPOs, they

found that CEF IPOs tend to happen when CEFs are generally selling at a premium348 (i.e., a

strong negative relationship between discounts and CEF IPO activity), especially based on

historical standards. Also, and as Weiss (1989) found, about 3/4 ths of equity CEF IPOs are

purchased by individuals, with the remainder held by institutions at the offering. Regarding their

primary thesis (i.e., small investor sentiment drives the premiums and discounts), indeed they

found evidence consistent with the investor sentiment theory. Specifically, discounts on closed-

end stock funds narrow when small stocks do well, which is in contrast to the largest stocks. As

Lee et al. (1991, p. 75) summarize the basic conclusion of the study: ―The evidence supports

these predictions. In particular, we find that both closed-end funds and small stocks tend to be

348 Specific types of CEFs clearly have periods when IPOs are high and correspondingly their premiums are athistoric highs.




held by individual investors, and that the discounts on closed-end funds narrow when small

stocks do well.‖

Lee et al. (1991) results can be summarized as:

With respect to the discount, individual investor sentiment matters. That is, discounts are

high when investors are pessimistic and low (or even negative) when they are optimistic.

Sentiment risk is connected with holding CEFs. This same sentiment is widespread

enough to affect small stocks primarily held by individual investors as well.

By extension, discounts can be considered a proxy for the small stock premium

associated with small stocks and/or stocks held primarily by individual investors.

Findings imply relatively risk-free arbitrage opportunities (e.g., also see Brauer (1988)).

Overall, there is strong support for the theory that has probably grown since it was first proposed.

How did the theory do, or did it explain the four general issues? Regarding the first

Three issues, the answers are yes, yes, and yes. Lee et al. (1991) didn‘t directly address issue #4

on opening CEFs, but see Brauer (1984) or Brickley and Schallheim (1985) for any doubts on

that issue (i.e., CEF market prices converge to NAV when opened, actually they begin to

converge well before formal opening). In fact, the Lee et al. (1991) investor sentiment theory did

such a good job, that it seems that their overall results are accepted as true. Therefore, at the

time, it would seem that it is largely accepted as explaining most of what were viewed as

puzzles.




Additionally, from research on CEFs flow several pieces of prescriptive advice:

1. Don‘t buy CEF IPOs.

2. Wait until they are selling at a discount, then make sure they are selling at a historically

low discount to NAV (or else just avoid them, i.e., especially if they are trading at a

premium).

3. When buying or selling, try to find the lowest brokerage commissions (although, this is

generally true).

4. Avoid purchases around January (especially for small stock funds).

5. Avoid trendy asset classes (i.e., unless you are shorting them).

6. If you can, try to buy at a deep discount and open the fund up.

Normative finance theory doesn‘t have much to say about CEFs, but descriptive evidence and

theory can.

Finally, and I would be remiss without acknowledging the similarity between IPOs and CEFs.

CEFs and equity IPOs are directly related. CEFs have an equity IPO which mirrors traditional

IPOs (i.e., initial underpricing and longer term overpricing). This is the key and they are very

much alike with respect to two of the puzzles (i.e., apparent initial overreaction and longer-run

underperfromance/underreaction), but in the case of standard equity IPOs there isn‘t an explicit

premium or discount to look at. This is too bad and due to the lack of an agreed upon pricing

formula for regular IPOs descriptive theoretical work has been relatively slow and messy.

Therefore, with IPOs we must assume a pricing model applies when we don‘t have one that




applies (i.e., that academics can agree on). But after that assumption we generally come up with

two implicit issues/puzzles (and one based on the first two) vs. four explicit puzzles for CEFs.

Essentially there are two or three phenomena associated with IPOs:

1. short-term initial under-pricing and/or outperformance/overreaction (and in many cases a

‗hot-issue‘ market), followed by

2. long-term underperformance or implied overpricing with longer term underreaction349,

and possibly

3. the time varying, and possibly other characteristics, nature of #1 and #2 (i.e., the degree

of initial underpricing and eventual underperformance can vary greatly from time to time

and group to group of IPOs). Therefore, choice of sample period and sample group can

greatly impact how much, how long, and how strong the effects for #1 & #2 occur or

are.350

Therefore, the initial price is generally set too low, then subsequently goes too high, and finally

over what typically takes several years drifts back down in relative price.

In addition, it should be mentioned that there are three principal actors or agents involved (which

are the same as that for CEF IPOs): (1) the issuing firm, (2) the underwriter or underwriters, and

(3) investors. Also, remember, CEFs begin life as equity IPOs, hence a direct link between the

two. Traditionally, for example, Loughran and Ritter (1995) find that in the long-run U.S. equity

349 This is unusual in that underreaction tends to be a more short-term proposition. Although maybe not so unusualin that all misspricings, whether large or small, have to start somewhere.350 Of course, this could also be noted for CEF IPOs and most other types as well. Actually, it‘s very true of manyphenomoena in finance.




IPOs underperform their comparison group on non-IPOs by about 6% per year for five years

after issuing (using a market-cap control, but no other controls).351 In contrast, Brav and

Gompers (1997) indicate that long-run underperformance is more complicated than generally

indicated by most IPO studies. Specifically, by also matching on market-to-book (i.e., as well as

market-cap) they find that underperformance only occurs in small firms not backed by venture

capitalists.352

Regardless of exact long-term underperformance mechanics, in effect short-term

investors tend to initially overreact, and then underreact on a more macro basis. Shiller (1988)

suggests that market for IPOs is subject to fads (there is strong evidence for hot markets in

IPOs)353, and enhanced by underwriters creating the impression of doing the investor a favor. In

essence, all the actors see and seize a window of opportunity that is cyclical in nature, as well as

possessing a tendency to build over time. Overall, it would seem that the three principal agents

each seem to have shifting motivations (e.g., the regret of missing the ‗hot‘ market). Investors

will bet on trends, overweight the recent past, and are overly optimistic. Issuing firms see a

window of opportunity and appear, at times, almost desperate to find an underwriter and time the

market. Underwriters also see a window of opportunity, but are in a position to play investors off

against issuing firms.

351 Based on U.S. data covering 1970 through 1990, about 5% for new IPOs and about 7% for SEOs, according toLoughran and Ritter (1995). Note that although a bit different in their initial overpricing characteristics, seasonedequity offerings are similar in their long-run underperformance characteristics compared to new IPOs.352 In effect, they pick up on the fact that big underwriters tend to pushing marginal companies at opportune times inthe IPO market cycle. That is, underwriters tend to key on high market-to-book firms that have shown short-runsuccess, but predictably lack long-term performance.353 For example, Ritter (1991, p. 3) finds ―substantial variation in the underperformance year -to-year and acrossindustries.‖ Indeed, certain industries do seem to have ‗hot‘ IPO markets at certain times, then fad away into virtualobscurity again.




Generally, the IPO evidence and theory (normative and descriptive) can be summarized as

follows (see Ritter and Welch (2002)):

Most IPO empirical results are not stationary (e.g., the degree to which the IPO is initially

underpriced), that is, they vary greatly over time. For example, in the U.S. equity markets

during 1980s the average first-day return was about 7%, then roughly doubled to about

15% during 1990 through 1998, then went to about 65% during the final bubble years of

1999 through 2000 (see Loughran and Ritter (2004))! Also, at different times and places,

there seem to be windows of opportunity for some types of firms but not for others (see,

e.g., Ritter (1991)).

While initial underpricing of the IPO is a persistent empirical feature of IPOs generally,

the cause is not so clear. The more normative rationale has been asymmetric information.

I tend to agree with the Ritter and Welch (2002, p. 1816) that: ―it is not so much a matter

of which model is right, but more a matter of the relative importance of different models.

Furthermore, one reason can be of more importance for some firms and/or at some

times.‖ Thus, at different times and for different markets the specific cause(s) may be

very different (i.e., as one would expect in behavioral finance).

While long-run underperformance of IPOs is a persistent feature of IPOs generally, a

primary complicating feature is that most IPOs tend to occur among sets of firms that

tend to have poor long-run performance. Thus, using ‗control‘ non-IPO firms to match

against IPO firms tends to make the effect diminish or go away statistically, yet says little

or nothing about why those types of firms generally tend to underperform after the IPOs

occur for that group of firms possessing similar characteristics (e.g., Internet company




IPOs that were issued during 1999 had horrible relative performance over 2000 through

2003).354

From a behavioral perspective, one of the more interesting issues is the cause for initial

underpricing. For example, Loughran and Ritter (2004, p. 5) find that: ―We attribute much of the

higher underpricing during the bubble period to a changing issuer objective function. We argue

that in the later periods there was less focus on maximizing IPO proceeds due to an increased

emphasis on research coverage. Furthermore, allocations of hot IPOs to the personal brokerage

accounts of issuing firm executives created an incentive to seek rather than avoid underwriters

with a reputation for severe underpricing.‖ Thus, at least in the U.S. more recently, a primary

reason for initial underpricing of about one order of magnitude higher than previously (i.e., about

65% vs. 7%) is corruption and/or fraud at least in part seemingly tied to the changing of

incentives and/or incentive structure.

354 This seems to be a fairly common approach of EMH/EMT promoters. It is as if they forget that by pointing outthat one ‗anomaly‘ is subsumed by another larger ‗anomaly‘ that the first somehow will go away. What seems to beforgotten in apparent rush to discredit one ‗anomaly‘ is that they just showed that the issue is much more widelydistributed that first realized. Thus, not only hasn‘t the ‗anomaly‘ gone way, but it has grown. This issue withgeneral long-run underperformance of IPOs is just such an example; overreaction being mostly a Januaryphenomena is probably another.




Source: Loughran and Ritter (2004, p. 14).

The preceding graph shows issuance as bars (i.e., ―number of IPOs‖) and average first-day

returns as a line of connected dots. As the above graph shows, there was a special spike in first-

day returns to IPOs issued during 1999 that is rather unique. My guess is that the why of this

spike is certainly not increased competency and more likely linked to increased corruption and

fraud.355

355 Loughran and Ritter (2002) find that the amount of money ―left on the table‖ during 1990-1998 is, for example,more than the amount of underwriting fees, or about three years of operating profits. This would seem not onlyirrational, but highly odd (i.e., at least from a normative perspective), especially if one cannot assume that a rootcause is at least some form of corruption or fraud. That is, it is hard to imagine the issuer and especially theunderwriter, since it is their primary job to raise funds, could be that incompetent. Loughran and Ritter (2002)theorize that wrapped up in the initial underpricing is at least in part due to unanticipated wealth increases,especially for ‗hot IPOs‘. For them, it is a kind of temporary insanity that takes over. I find that a credible link, butnot necessarily the driving cause.




PROSPECT THEORY

In 1979 an article was published by Kahneman and Tversky (1979) (or ―KT‖) that may be the

first formal descriptive based theory in economics. The theory was called prospect theory (―PT‖)

and was submitted as a more realistic theory in place of standard expected utility theory

(―EUT‖). EUT was designed to directly address decision making under uncertainty as first

formulated by Bernoulli in 1738 and respecified formally some 200 or more years later by von

Neumann and Morgenstern. Tversky and Kahneman (1992) later followed PT with cumulative

PT or ―CPT‖. For this section I will focus on PT, as CPT is messier and I believe most of the

analytic benefit can be found in PT itself.356

Compared to the descriptive theories detailed thus far in this chapter (i.e., the MCH and the

‗investor sentiment‘ theory of CEFs), PT is more mathematical. Thus, unlike the others that

provide a general outline of what to expect, this is more detailed in its specification yet injects a

large part of the descriptive reality that is required of any realistic finance theory or hypothesis.

Economics historically has assumed away psychology as a relevant variable in decision making

in general, and decision making under uncertainty specifically. In effect, decision making was

largely ignored in favor of a normative ―black box‖. But we know, for example, that decision

makers (see Olsen (1998, p. 11):

1. Preferences‘ ―tend to be multifaceted, open to change, and often formed only

during the decision process.‖

356 This review is intended to be brief. That is, I am not going to do a full scale proof of PT or CPT. Therefore,detailed mathematical proofs can be had by reading Kahneman & Tversky (1979) and Tversky & Kahneman (1992).In addition, because it is not a normative theory and is primarily based on actual evidence, pay attention to theexplicit assumptions behind the theory and contrast them to EUT.




2. Appear to adapt their decisions to the environment the decision was made in (e.g.,

framing matters).

3. Tend to ―seek satisfactory, rather than optimal solutions.‖

Decision making reality it turns out to be very different than normative theory. In general,

simplicity and mathematical tractability have been chosen over empirical reality (e.g., rational

expectations). In PT several EUT assumptions are relaxed or modified to reflect known human

biases.

Driving the basic model of the evaluation of risky prospects/gambles are the assumptions behind

investor preferences. The majority of models assume that investors evaluate risky

prospects/gambles according to the EUT of Von Neumann and Morgenstern (1947). They show

that if preferences satisfy a series of axioms (completeness, transitivity, continuity, and

independence), then preferences can be represented by the expectation of a utility function.

Unfortunately, life is messier and people systematically violate EUT (e.g., Allais (1953)). There

have been a number of substitutes proposed, of which Prospect Theory (KT (1979), and Tversky

and Kahneman (1992)) are a promising alternative, for financial market applications in

particular.

As noted by KT, traditionally economic decision making under risk is usually viewed in strictly

normative probability terms, but it can also be viewed as a choice between prospects or gambles

(which is actually how most humans tend to view it). That is, the overall utility of a prospect,




denoted by U , can be viewed as the expected utility of its outcomes, but those outcomes may not

possess a purely strictly normative rationality form, unlike traditional EUT.

More formally, based on KT‘s PT, more traditional EUT can be placed in PT notation as follows:

A prospect ),;...;,(11 nn p x p x is a contract that yields outcome i

x with probability i p , where

1...21 n p p p .

To simplify, ),( p x denotes the prospect )1,0;,( p p x that yields x with probability p and 0 with

probability 1 - p (i.e., two outcomes).

The (riskless) prospect that yields x with certainty is denoted by ( x). The application of expected

utility theory to choices between prospects is based on the following three tenets:

(i) Expectation: )(...)(),;...;,(1111 nnnn xu p xu p p x p xU .

(ii) Asset Integration: ),;...;,(11 nn p x p x is acceptable at asset position w iff

)(),;...;,( 11 wu p xw p xwU nn .

(iii) Risk Aversion: u is concave (u‖<0).

Thus, with standard EUT all probabilities add to unity, expectations are linearly additive, etc.

(i.e., the EUT world with risk/uncertainty is normatively is so well behaved, that virtually

nothing behavioral is possible). KT (1979, p. 264) state: ‖a prospect is acceptable if the utility

resulting from integrating the prospect with one‘s assets exceeds the utility of those assets alone.

Thus, the domain of the utility function is final states (which includes one‘s asset position) rather

than gains or losses. ... most applications of the theory have been concerned with monetary

outcomes. … A person is risk averse if he prefers the certain prospect ( x) to any risky prospect

with expected value x. In EUT, risk aversion is equivalent to the concavity of the utility




function‖. Most of these properties of EUT will now be dropped for PT in favor of several more

realistic assumptions. This was done largely in response to behavioral experiments that showed

most people violate basic ‗axioms‘ underpinning traditional EUT.

For example, Allais (1953) performed the following experiment that showed a clear violation of

EUT:

Problem 1: Choose between

A: 2,500 with probability .33, B: 2,400 with certainty.2,400 with probability .66,0 with probability .01.

N = 72 [18] [82]*

Problem 2: Choose betweenC: 2,500 with probability .33, D: 2,400 with probability .34,

0 with probability .66, 0 with probability .66.

N = 72 [83]* [17]*

EUT implies u(2,400) > .33u(2,500) + .66u(2,400) or .34u(2,400) > .33u(2,500)

In this example, 82% of the subjects chose B in Problem 1, and 83% of the subjects chose C (i.e.,

effectively answer A from Problem 1) in Problem 2 (each is significant at the 1% level of

statistical significance). First preference of EUT implied that there should be no change, yet there

was an actual flip-flop. Problem 2 is obtained from Problem 1 by eliminating a .66 chance of

winning 2,400 from both prospects under consideration. The substitution axiom of utility theory

asserts that if B is preferred to A, then any (probability) mixture (B, p) must be preferred to the

mixture (A, p). The subjects did not obey this axiom. Eliminating a .66 chance of winning 2,400




from both prospects under consideration had a big effect (altered the character of the prospect

from a sure gain to a probable one).

PT, which is an alternative account of individual decision making under risk, attempts to avoid

such violations of EUT. KT note that many ‗anomalies‘ of preference result from the editing of

prospects (i.e., context and framing – see, e.g., Tversky and Thaler (1990) on preference

reversals, and Tversky and Kahneman (1986) on framing of decisions). KT delineate two phases

in the choice process: (1) an early editing phase, and (2) a subsequent evaluation phase. ‖Thefunction of the editing phase is to organize and reformulate the options so as to simplify

subsequent evaluation and choice.‖ Major editing operations are:

1. Coding: place into gains and losses (rather than as final states of wealth or welfare as

with EUT) relative to some reference point (typically a neutral point, like their current

asset position, but can be affected by the formulation of the offered prospects). The

critical point is that coding is done relative to some reference point.

2. Combination: prospects can sometimes be simplified by combining the probabilities

associated with identical outcomes.

3. Segregation: some prospects contain a riskless component that is segregated from the

risky component in the editing phase (e.g., (300, .8; 200, .2) becomes a sure gain of 200).

The preceding operations are applied to each prospect separately, while the following

operation is applied to a set of two or more prospects.




4. Cancellation: Discarding of components that are shared by the offered prospects (e.g.,

discarding the first step in a two-step sequential game because the stage was common to

both options).357

5. Simplification: Simplification of prospects by rounding probabilities or outcomes (e.g.,

the prospect (101, .49) becomes an even chance to win 100). Another important form of

simplification involves the discarding of extremely unlikely outcomes.

6. Detection of dominance: Scanning of offered prospects to detect dominated

alternatives, which are rejected without further evaluation.

Note, some editing operations either permit or prevent the application of others (e.g., (500, .20;

101, .49) will appear to dominate (500, .15; 99, .51) if the second constituents of both prospects

are simplified to (100, .50)). Therefore, the final edited prospects could depend on the sequence

of the editing operations, which will likely depend on the structure of the offered set and the

format of the display (as in the real world). PT assumes no room for further editing or no

ambiguity for the edited prospects. These six steps are intended to reflect a version of what

people actually do in reality applied to decision making under risk.

Following the editing phase, assume the decision maker will evaluate each of the edited

prospects, and choose the prospect of highest value. The overall value of an edited prospect is

denoted V , and is expressed in terms of two scales, and v. associates with each probability p

a decision weight )( p , which reflects the impact of p on the overall value of the prospect.

357 This is a version of what is called the ‗isolation effect‘. It is a form of framing that ―leads to inconsistent preferences when the same choice is presented in different forms.‖ People can decompose distinctive and commoncomponents in more than one way, and different decompositions sometimes lead to different preferences (e.g., atwo-stage game).




However, is not a probability measure (e.g., )1()( p p is typically less than unity). The

second scale, v, assigns to each outcome x a number v( x), which reflects the subjective value of

that outcome. Remember, outcomes are defined relative to a reference point, which serves as a

zero point of the value scale. Hence, v measures the value of deviations from that reference point

(i.e., gains and losses). This reference point is a critical distinction between PT and EUT, and

reflects how most people actually measure value.

KT consider only simple prospects of the form ),;,( q y p x which have at most two non-zero

outcomes. In such a prospect, one receives x with probability p, y with probability q, and nothing

with probability 1 – p – q, where p + q 1 . An offered prospect is strictly positive if its

outcomes are all positive (i.e., if x, y > 0 and p + q = 1); it is strictly negative if its outcomes are

all negative. A prospect is regular if it is neither strictly positive nor strictly negative.

The basic equation is as follows (it describes the manner in which and v are combined to

determine the overall value of regular prospects):

If ( x, p; y, q) is a regular prospect (i.e., either p + q < 1, or y x 0 , or y x 0 ), then

)()()()(),;,( yvq xv pq y p xV (1) for ‖regular prospects‖

where v(0) = 0, 0)0( , and 1)1( .

As in utility theory, V is defined on prospects, while v is defined on outcomes. The two scales

coincide for sure prospects, where V ( x, 1.0) = V ( x) = v( x). Equation (1) generalizes EUT by

relaxing the expectation principle.




The evaluation of strictly positive and strictly negative prospects follows a different rule. In the

editing phase such prospects are segregated into two components: (1) the riskless component

(i.e., the gain or loss which is certain to be obtained or paid); and (2) the risky component (i.e.,

the additional gain or loss which is at stake).

If p + q = 1 and either x > y > 0 or x < y < 0, then

)]()()[()(),;,( yv xv p yvq y p xV . (2) for ‖+ and - prospects‖

That is, the value of a strictly positive or negative prospect equals the value of the riskless

component plus the value-difference between the outcomes, multiplied by the weight associated

with the more extreme outcome (e.g., V (400, .25; 100, .75) = v(100) + (.25)[v(400) –

v(100)]).358

It is critical to note that PT introduces a subjective value and a reference point; this is not the

case in EUT. Key features of PT are:

It is not a normative theory (indeed, Tversky and Kahneman (1986) argue ―that

normative approaches are doomed to failure, because people routinely make choices that

are simply impossible to justify on normative grounds, in that they violate dominance or

invariance.‖ (Barberis and Thaler (2002, p. 16))

―Utility is defined over gains and losses rather than final wealth positions, an idea first

proposed by Markowitz (1952).‖ (Barberis and Thaler (2002, p. 16)) Therefore, wealth is

defined as something relative, not absolute (most people define their lives, and such

358 Note, the RHS of (2) = )()](1[)()( yv p xv p . Hence, equation (2) reduces to equation (1) if

1)1()( p p .




things as light, loudness, and temperature based on earlier levels, rather than in absolute

terms).

Shape of the value function v is unique (it is concave in the domain of gains, and convex

in the domain of losses). This implies that people are risk-averse to gains and risk-

seeking over losses.

The v function has a kink at the origin (which indicates a greater sensitivity to gains than

to losses, also known as loss aversion).359

Nonlinear probability transformation (i.e., small probabilities are overweighted, so that

π( p) > p).

Higher sensitivity to differences in probabilities at higher probability levels (related to the

‗certainty effect‘, where people place higher weights on certain prospects).

As a bonus, it also explains preferences for insurance and lottery tickets (for example,

although v is concave in the region of gains (indicating risk aversion), for lotteries which

offer a very small chance of a large gain, the overweighting of small probabilities

dominates, leading to risk-seeking in the domain of gains).

Note, PT does not establish where the reference point360 is (but it is likely to be around a

person‘s current wealth level), nor does it establish where the weighting function reverses at

359 The ‗reflection effect‘ picks up a dramatic shift of risk aversion to risk. Especially true when moving frompositive prospects to negative prospects. Risk aversion in the domain of gains and risk seeking in the domain of losses. The aversion to uncertainty is also picked up here. Actually, ‖certainty increases the aversiveness of losses aswell as the desirability of gains.‖ Therefore, three effects:

(1) Risk aversion in the positive domain is replaced by risk seeking in the negative domain.(2) Preferences between the positive prospects are inconsistent with EUT.(3) The reflection effect eliminates aversion for uncertainty or variability as an explanation of the certainty

effect.




extremely high and low points (e.g., deep out-of-the-money lottery tickets/options).361

Thus, it

presents a general form of the shape of a person‘s weighting function. Again, people tend not to

follow strict rules, but may display a general pattern of behavior that may be well described by

such functional forms. The essential feature of equation (2) is that a decision weight is applied to

the value-difference v( x) – v( y), which represents the risky component of the prospect, but not to

v( y), which represents the riskless component. Also note that the conditions that need to be

satisfied for the right hand side (―RHS‖) of equation (2) to equal that described, which reduces to

equation (1) are generally not satisfied.

362

The two keys to this theory are: (1) PT assumes that values are attached to changes rather than to

final states, and (2) that decision weights do not coincide with stated probabilities (i.e., decision

weights are not probabilities, and therefore do not obey probability axioms). Both are departures

from EUT, which leads to inconsistencies, intransitivities, and violation of dominance (which

EUT does not allow, but actually happen). Such anomalies of preference are normally corrected

by the decision maker when he or she realizes that his or her preferences are inconsistent,

intransitive, or inadmissible; but in most cases the decision maker is never made aware (or made

360 This notion of the reference point is also related to ―get-evenitis‖ (which in turn is related to the ―disposition

effect‖), which is more applicable to the accounting of gains and losses relative to the original price paid for aspecific asset. Overall wealth has been emphasized, yet it is important to add this aspect about the reference point(which was originally noted and motivated by Markowitz (1952)).361 The PT of Tversky and Kahneman (1992 - CPT) is a generalized PT which can be applied to gambles with morethan two outcomes, and they incorporate a loss aversion coefficient (which is estimated to be about two to 2.25, i.e.,based on empirical research).362 As an aside, note that Markowitz (1952) was the first to propose that utility be defined on gains and losses ratherthan on final asset positions. Also, Markowitz noted the presence of risk seeking in preferences among positive andnegative prospects, and proposed a utility function which has convex and concave regions in both the positive andnegative domains (although, he retained the expectations principle).




aware only after the fact). Again, decision weights are not probabilities, and therefore do not

obey probability axioms; PT does not ignore this reality.

PT produces the following general form of ―value function‖363:

This contrasts and compares with the following utility functions:

363 Please note that what is missing from this diagram are reversals at the extreme regions of gains and losses.Therefore, the lines of the curve at the both ends reverse themselves.




From: Lopes, L., ―Between hope and fear: The psychology of risk‖, in Goldstein, W., and R. Hogarth, (editors)Research on Judgment and Decision Making: Currents, Connections, and Controversies, Cambridge Series onJudgment and Decision Making, Cambridge University Press, New York, N.Y., 1997, p. 683.

I do not want to give the reader the idea these (except for the upper left one) are standard utility

functions in economics or finance. Note, with the exception of the upper left utility function, the

others are not standard and not well received by theoretical microeconomists. The Bernoullian

function (upper left) is uniformly risk averse (i.e., negatively accelerated). The Friedman and

Savage (1948) function in the upper right. The Markowitz (1952) function in the lower left. The

KT (1979) function in the lower right (i.e., the utility version of PT‘s value function). All but the

Bernoullian have regions of both risk aversion (i.e., negative acceleration) and risk seeking

(positive acceleration). Note, risk seeking behavior is contrary to normative economics, but




observed under certain conditions in the real world. The upper two functions range from zero

assets to large positive assets. The lower two functions range about a customary asset level (e.g.,

the status quo). Both risk seeking behavior and the reference point kink are not traditional

normative finance or economics assumptions. They are shown here in contrast to the traditional

normative Bernoullian utility function that has neither of these features.

A few final comments on PT are in order. First, the math is substantially messier than standard

EUT for a reason. It better reflects reality and thus is accordingly mathematically messier.

Second, it is more realistic, but not perfect. The reader should now be more comfortable with the

thought that human behavior is tricky to forecast. Therefore, more realistic assumptions help PT,

but more could be made at the likely cost of more mathematical messiness and/or tractability.

Finally, it is an improvement, yet it is still wrong.




SAD AND STOCK EXCHANGE DISTANCE FROM THE EQUATOR

I will end this chapter with a psychology based descriptive theory applied directly to pricing in

equity markets, namely SAD and regional stock exchange pricing effects. This theory was

already discussed in the chapter on psychology (again, see Kamstra et al. (2002)). I mention it

again because I find it an excellent example of a descriptive behavioral finance theory. In effect,

the SAD theory has it all, and then some. There is at least the appearance of the two pillars of

behavioral finance, yet there are still some seemingly odd things going on. That is, there seems

to be both limits to arbitrage (e.g., large multinationals are typically traded outside of their home

country yet seem to be affected by their home distance from the equator, even when few, if any,

limits to arbitrage seem apparent) and psychology (i.e., a seeming direct link from clinical

psychology running through depression caused by the diminution of daylight hours during one

part of the year the further we move away from the equator). What else could a behavioral

finance person look for?

With SAD there is a very likely direct observable link between human behavior and the financial

markets. What is especially unusual is that a clear minority of people is clinically depressed, yet

it seems to affect the pricing equities in a predictable way. In addition, it would seem that it

affects large-cap and small-cap stocks roughly equally. This is unique and seemingly

arbitrageable, yet it still happens. It is likely, as mentioned before, that other foreign investors

seem to generally follow the local lead, yet it is still seems a bit odd.




Regardless, daylight hours/distance from the equator, clinical depression, and equity prices, who

would have believed such a theory? My guess is that before reading this book most would have

brushed such a theory off as absurd. Again, from Modigliani and Cohn (1979, p. 36):

―Confronted with overwhelming statistical evidence consistent with our error hypothesis, and no

direct evidence inconsistent with it, our original skepticism turned into a degree of confidence

approaching belief – and certainly high enough to justify placing our findings before the public.‖

I imagine that Kamstra et al. (2002) felt the same way; and I now hope the reader will begin to

entertain such thoughts concerning descriptive behavioral finance theories, to do otherwise is not

in their economic interest.

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Axiomes de l'Ecole Americaine‖, Econometrica, Volume 21, Number 4, October 1953, 503-

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Chapter 14: Volatility & volume (V & V) – or why so much trading?

―Noise makes trading in financial markets possible, and thus allows us to observe prices for

financial assets. Noise causes markets to be somewhat inefficient, but often prevents us from

taking advantage of inefficiencies. ... Most generally, noise makes it very difficult to test either

practical or academic theories about the way that financial or economic markets work. We are

forced to act largely in the dark.‖

Black (1986, p. 529)

Normative finance models have historically assumed homogenous expectations (e.g., the

CAPM). If all investors have the same expectations it is difficult, if not illogical, to generate any

trading in a normative model, and if there is no trading it is difficult, if not impossible, to

generate any volume. Yet, in the actual financial markets all kinds of trading/volume and

volatility goes on. Why? Specifically, if we all think the same and agree that, for example, the

price of IBM should be $10 a share, then there should be little or no trading and little no

volatility, yet there is. As suggested by Black (1986), would we essentially have no trading

without ‗noise‘? In short, why do we have all the volume and volatility that we seem to have?




VOLATILITY

In 1981 two articles were published directly examining the question of whether stock price

variability was justified by changes in cash flow (see Shiller (1981a) for dividends, and LeRoy

and Porter (1981) for earnings). As noted early in the book, actual finance is principally

concerned with present values or discounted cash flows. Shiller (1981a) essentially makes his

case based on the fact that the primary periodic cash flows of equities are their dividends.

Therefore, stock price variability should be approximately equal to the variability of their cash

flows (i.e., dividends). Shiller (1981a) calls this the ―simple efficient markets model‖. Based onsuch a model, Shiller (1981a, p. 422) graphs the model derived volatility against actual market

volatility for the S&P and Dow Jones indices.




From: Shiller, R., ―Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?‖,American Economic Review, Vol. 71, Issue 3, June 1981, p. 422.

The dotted lines represent the expected price series based on Shiller‘s discounted cash flow

model (i.e., discounted dividend cash flow model) for each U.S. stock index. Left to right, the

solid lines are the S&P Composite and Dow Jones indices, respectively. As the reader can clearly

see, in both cases the actual variation is many times the theoretical expectation. In fact, the actual

variation is somewhere between five to thirteen times too high!

It is difficult to imagine that looking at these graphs would make one a big supporter of the

notion that economic fundamentals are the primary drivers of the equity markets. Again, stock

price volatility has been five to thirteen times too high over the last century or so, as measured by

future real dividend uncertainty (depending on the level of confidence; and remember, this is

market volatility, therefore individual stocks or groupings of stocks can be much worse).

It seems that this result cannot be attributed to tax law changes, price index problems, or data

errors. Some arguments against Shiller‘s result are (1) that he should have used earnings, and/or

(2) factored in a time-varying real interest rate. Unfortunately for detractors, this doesn‘t really

change the results; in addition, it isn‘t clear what detractors mean by earnings and the real

interest rate. That is, it is hard to argue against something when you don‘t define what you are

arguing in support of. As usual in finance, one must expose oneself to some sort of pricing model

or it is difficult, by definition, if not impossible, to establish much of anything empirically. In

addition, actual nominal movements are less than the real rates would have had to move (i.e., it is

likely physically impossible to rationalize the counter argument by varying ‗real‘ rates alone).




Another counter argument has been whether there is a systematic underestimating the level of

uncertainty surrounding future dividends? Again, we cannot observe that, and it would have had

to be so big, that it is hard to imagine. Finally, for example, a well known attack against Shiller

(1981a) was by Marsh and Merton (1986) where they argued that by assuming nonstationarity

(vs. Shiller (1981a) assuming stationarity) the Shiller (1981a) result is no longer clear.364 As

always in normative finance and economics assumptions can trump evidence and basic logic.365

But on balance, in summary the general result has held and it has been and seems to be very

disconcerting to EMH/EMT promoters.

366

Using adjusted earnings (because earnings are typically not cash flows), a different present value

model, and different tests, LeRoy and Porter (1981, p. 573) came to a similar conclusion as

Shiller (1981a) and concluded by stating: ―we are not able to resolve this difference between our

results in which market efficiency is rejected with the standard results in which the opposite

conclusion is reached.‖367 By ―standard results‖ they mean Fama (1970). In other words, their

results are so far away from standard textbook market efficiency at the time that they really

didn‘t know what to write.

364 That is, by changing a basic assumption underlying the variance bounds tests, the results of those tests themselvesare no longer clearly interpretable. Obviously, for example, if you assume the moon is made of cheese any proof thatit is not made of cheese would not be convincing. Also, see, e.g., Mankiw et al. (1991) for another version of theMarsh and Merton (1986) result.365 Essentially, given that it is a present value formula, you can either attack the dividend part or the discount rates.

As Barberis and Thaler (2002, p. 30) point out: ―Some rational approaches try to introduce variation in the P/D ratiothrough the third term on the right in equation (14). Since this requires investors to expect explosive growth in P/Dratios forever, they are known as models of rational bubbles.‖ Alternatively, one can attack the rates part, but, note,actually interest rates only weaken the EMT case (e.g., typically, interest rates are lowest when the stock market ismost volatile, e.g., 1929). Also, see Shiller (2002, p. 9-10).366 Also, see, e.g., Shiller (1981b) for a review of the general statistical and/or econometric issues and his generalinference that it is more likely that ―markets are irrational and subject to fads‖ than, for example, ―ex ante realinterest rates show very large movements‖.367 Similar results for exchange rates as well (e.g., Huang (1981)). That is, basic market efficiency with respect tovolatility is violated not just for stocks.




These results and Shiller‘s (1981a) are a kind of basic efficiency tests that were not really

formally accounted for in the original EMH/EMT, yet they are conceptually devasting to

standard notions of market efficiency. Using Shiller‘s (1981a) lower bound of five times too

much volatility as can be justified by present value changes in cash flows, how could one accept

these results alone and still have faith in textbook market efficiency?

Finally, on the question of what or who might be causing excess volatility (i.e., from an efficient

market viewpoint), Sias (1996, p. 18) concludes with the following comment: ―Our empirical

results are consistent with the hypothesis that an increase in institutional investor interest induces

an increase in volatility.‖ In other words, institutional ownership seems to cause some volatility.

If this is true, then, like retail PMs ‗portfolio pumping‘, we have another area where supposed

‗smart money‘ is causing an inefficiency (in this case likely pushing prices around too much

from an efficiency standpoint as opposed to the prices being too high or too low per say),

although possibly not breaking rules or laws. Thus, you can get a kind of ‗cancelation‘ but

volatility is way too high and on that basis alone market efficiency is lacking.

Regarding behavioral rationales for the volatility puzzle, they can be grouped into two

overlapping areas: (1) beliefs, and (2) preferences. Some examples of each are:

Beliefs:

• ‗Law of small numbers‘ version of representativeness – People expect small samples to

reflect the parent population (e.g., extrapolating recent past into the long-term future).




• Overconfidence about perceived private information.

• ‗Money illusion‘ with respect to the P/D ratio (i.e., prices and cash flows will tend to

increase due to inflation, but people interpret this as a real, not a nominal, effect).

Preferences:

• Loss aversion is not constant, but depends on circumstances (e.g., prior losses or gains).

• The ‗house money effect‘ or willingness to take on risk after experiencing a sequence of

gains can change (therefore, the approximately 2.25 risk aversion coefficient found by

Tversky and Kahneman (1991) is not a constant).

Beliefs and preferences can impact both the cash flows and discount rates. In addition, it may at

least in part just reflect the nature of financial market tasks. For example, Olsen (1998) notes that

psychology has documented that complex problems combined with heterogeneous beliefs tend to

lead to a: ―unless arbitrage opportunities are complete, larger divergence of opinion will lead not

only to greater price volatility but also higher prices.‖368 Therefore, the nature of most financial

problems can cause volatility and higher prices (even possibly price bubbles). In summary, the

causes can be speculated upon, but are not well established. All that can be stated with

confidence is that it is very likely there is normatively too much volatility; but we do not know

why.

368 See Olsen (1998, pp. 16-17): ―Ex post, the performance of few ‗experts‘ on complex, ill-structured taskssurpasses naïve strategies, and when experts do outperform, the margin of superior performance is small andinconsistent. … Complex, ill-structured tasks or decisions give rise to great variability in decision outcomes becausethey tend to lie more toward the experience or intuitive end of the decision spectrum than the objective end andmake greater use of idiosyncratic information and procedures that are personal, concrete, holistic, affective(emotional), and based on such associative conventions as the use of analogies and stereotypes (Forgas 1991,Epstein 1994, Hammond 1996, and Busemeyer 1995). … unless arbitrage opportunities are complete, larger divergence of opinion will lead not only to greater price volatility but also higher prices.‖




VOLUME

―Trading volume on the world‘s markets seems high, perhaps higher than can be explained by

models of rational markets. ... we lack economic models that predict what trading volume in

these markets should be. In theoretical models trading volume ranges from zero (e.g., in rational

expectation models without noise) to infinite (e.g., when traders dynamically hedge in the

absence of trading costs). But without a model which predicts what trading volume should be in

real markets, it is difficult to test whether observed volume is too high.‖

Odean (1999, p. 1279)

Therefore, normative models can give you no trading or infinite trading. Of course, the most

‗rational‘ ones tend toward zero. Clearly, and contrary to one of the most relied upon

assumptions in traditional finance models, investors possess heterogeneous beliefs and

expectations, not homogenous ones, as often assumed in models such as the CAPM, or trading

wouldn‘t be so active and voluminous. In addition, often ‗noise traders‘ are introduced to force

trading into a normative model. Given that trading is the main activity of the financial markets,

then most activity in the financial markets has the larger part of its cause motivated by

psychology, not purely economics (remember, without trading these markets wouldn‘t exist).

Barber and Odean (1999, p. 51) point out that ‗rational‘ models ―provide little insight into why

people trade as much as they do. In some models, investors seldom trade or do not trade at all

(e.g., Grossman 1976). Other models simply stipulate a class of investors – noise or liquidity

traders – who are required to trade (e.g., Kyle 1985). Harris and Raviv (1993) and Varian (1989),

however, pointed out that heterogeneous beliefs are needed to generate significant trading. And




behavioral finance throws light on why and when investors form heterogeneous beliefs.‖

Descriptive models of trading that directly incorporate psychological biases such as

overconfidence tend to produce excessive volume (e.g., Odean (1998)). Regardless of theoretical

model assumptions, how much volume, if any, is enough? Rephrased, how much trading is

efficient? We don‘t know, but common sense is rather pressed to find actual levels are

normatively rational. As De Bondt and Thaler (1995, p. 392) noted concerning trading volume:

―the high trading volume observed in financial markets is perhaps the single most embarrassing

fact to the standard finance paradigm.‖

Without a normative answer, we should at least look at empirical reality. According to the

NYSE, their highest recorded annual turnover was 319% during 1901.369 Therefore, during 1901

institutions and individuals investing in U.S. common stocks on average held NYSE shares for

less than four months (i.e., less than 1/3rd of a year). More recently, average annualized turnover

on the NYSE through the end of September 2009 it was running at about 139% and about 138%

for 2008.370

Is it possible that annual turnover of even 100% makes economic sense?

Furthermore, could it be that the normatively optimal level of trading varies from one year to the

next by multiples (e.g., 319% one year, then less than 100% in another)? Specifically, it doesn‘t

seem likely that, for example, hedging requirements in 1901 were more than twice as great as in

2008, or more than 35 times as great as 1942 (see the next graph). Regardless of the seeming

absurdity of the absolute levels, is it even possible that the magnitude of changes from one year

to the next could be viewed as normatively rational?

369 See http://www.nyse.com/about/history/timeline_1900_1919_index.html.370 See http://www.nyxdata.com/nysedata/asp/factbook/viewer_edition.asp?mode=table&key=3084&category=3.




Source for data: http://www.nyxdata.com/nysedata/asp/factbook/ on November 3, 2009.

Besides the apparent irrational waxing and waning of trading levels, additionally there is the cost

issue of turnover, and not just transaction costs. For example, Odean finds that discount

brokerage investors don‘t just lose money trading more than they should, but lose money by

purchasing securities that perform more poorly than those they sell.371

Again, the quote is:

―The surprising finding is that not only do the securities that these investors buy not outperform

the securities they sell by enough to cover trading costs, but on average the securities they buy

underperform those they sell. This is the case even when trading is not apparently motivated by

liquidity demands, tax-loss selling, portfolio rebalancing, or a move to lower-risk securities.

371 Other countries also seem to exhibit this tendency or worse (e.g., see Barber et al. (2006) on Taiwan).

0%

25%

50%

75%

100%

125%

150%

175%

200%

225%

250%

275%

300%

325%

1 9 0 0

1 9 0 2

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2 0 0 0

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2 0 0 8

NYSE Annual Turnover - 1900 through 2008

http://www.nyxdata.com/nysedata/asp/factbook/







While investors‘ overconfidence in the precision of their information may contribute to this

finding, it is not sufficient to explain it. These investors must be systematically misinterpreting

information available to them. They do not simply misconstrue the precision of their

information, but its very meaning.‖

Odean (1999, p. 1280)

In other words, in general, people trade too much and generally do not have sound economic

motivation for so doing (i.e., trading is clearly economically excessive). In essence, much or

even most of financial market trading represents sheer normative irrationality. Overconfidence

alone is not even sufficient to explain the results.

Descriptively, overconfidence has been proposed as one primary cause of excessive trading or

volume in the financial markets.372 In addition, for example, self-attribution bias and the

disposition affect may have significant impacts on trading, especially under certain market

environments (e.g., see Statman et al. (2003)). During the front side of a financial bubble such

psychological biases inspired trading based on self-attribution bias can feed overconfidence.

Additionally, the disposition affect can affect trading both on the front and back sides of a bubble

(again, see Statman et al. (2003)). Therefore, shocks, especially large ones, to financial market

prices will provoke trading and empirically seem to be the case. Statman et al. (2003, p. 27):

―turnover levels are responsive to past market returns even when past security returns are

included in the model. In fact, past market returns are statistically and economically more

372 Also, Daniel et al. (1998) theorize that underreaction and overreaction are based on two biases: (1)overconfidence about the precision of their information, and (2) biased self-attribution.




important than past security returns in explaining individual stock turnover‖.373Therefore,

empirically, it seems that large up market moves inspire overconfident self-attribution biased

traders to trade more, while large down market moves tend to limit trading activity via the

disposition affect and the need to displace responsibility onto the mad market.

Finally with respect to the issue of overconfidence, I should note that there is some evidence that

standard psychological measures of overconfidence, namely calibration may not turn out to be

well correlated with excessive trading. Based on combining investor surveys with trading data,

Glaser and Weber (2003, p. 1) summarize their findings as: ―We find that investors who think

that they are above average in terms of investment skills or past performance trade more.

Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This

result is striking as theoretical models that incorporate overconfident investors mainly motivate

this assumption by the calibration literature and model overconfidence as underestimation of the

variance of signals.‖ Therefore, overconfident traders trade more, but with the critical caveat that

it seems to be more an issue of overly inflated self-perception with respect to trading skills,

illusion of control, etc. than miscalibration per say.

As background for what can go wrong with excessive trading (i.e., based on basic normative

standards), Barber and Odean (2000) have interesting findings. They find that households that

373 Glaser and Weber (2009) support this interpretation. In addition to a positive relationship between tradingvolume and past market and portfolio returns, Glaser and Weber (2009, p. 1) find that: ―After high portfolio returns,investors buy high risk stocks and reduce the number of stocks in their portfolio.‖




trade the most (i.e., the top quintile or top 20%) in their 78,000 sample374

turnover their stock

portfolios about 250% per year vs. an average of about 75% (over a sample period of

encompassing approximately 1991 through 1996 or around six years).375

More importantly,

those households that trade the most earn a net annual return of about 11.4% vs. those that trade

infrequently earn a net annual return of about 18.5%. Therefore, based on that comparison alone

the ‗traders‘ forego roughly 7% annually due to what appears to be unnecessary trading activity.

Even ignoring top to bottom trading quintile group comparisons, underperformance for the most

frequent traders varies between about 5.5% to 10.3% annually, depending on how one controls

for risk or what index returns are measured against (Barber and Odean (2000, pp. 793 & 797)).

Keep in mind, although gross returns were around measures of general index returns (i.e., before

transactions costs, taxes, etc.), the average household consistently underperformed the market on

a normative risk-adjusted basis.376 Therefore, even the average household broke the normative

economic trading rule that states that the marginal benefit of trading must be equal to or exceed

its marginal cost (e.g., see Grossman and Stiglitz (1980)), and the most aggressive traders were

seemingly on another planet altogether. Overall, it can be said that trading is a consistent source

of negative return for most investors most of the time. The question remains, why so much

trading when it clearly is harmful to your wealth?

374 Of those households, 66,465 have positions in common stocks (Barber and Odean (2000, p. 778)). Furthermore,―Roughly 60 percent of the market value in the accounts is held in common stocks. In these households, more than 3

million trades are made in all securities during the sample period, with common stocks accounting for slightly morethan 60 percent of all trades. On average during our sample period, the mean household holds 4.3 stocks worth$47,334, though each of these figures is positively skewed. The median household holds 2.61 stocks worth $16,210.In December 1996, these households held more than $4.5 billion in common stock.‖ 375 Given that the NYSE is routinely over 100% annual turnover and in the Barber and Odean (2000) sampleinvestors average 75% turnover, therefore, it is likely that institutional investors turn over their portfolios more thanhouseholds. If true, which is likely, this is not prima facie helpful for the view that institutions are more efficientthan individual investors with respect to trading efficiency.376 As confirmed by CEF research, individual investors, and households, tend to hold smaller-cap stocks with higherBetas than institutional investors (and high book-to-market).




In terms of overall cost to society from trading that appears to be generally a money losing

proposition, how much does it cost? Barber et al. (2006) got hold of all equity trades on the

Taiwan Stock Exchange (―TSE‖) and estimated that the costs: ―Using a complete trading history

of all investors in Taiwan, we document that the aggregate portfolio of individual investors

suffers an annual performance penalty of 3.8 percentage points. Individual investor losses are

equivalent to 2.2 percent of Taiwan‘s GDP or 2.8 percent of total personal income – nearly as

much as the total private expenditure on clothing and footwear in Taiwan.‖ Think about that for a moment, based on equity trades on the TSE only (i.e., excluding bond trading, outright

gambling, trading on other stock exchanges, etc.) the average Taiwanese spends at least as much

transacting money losing stock trades as he or she does on clothes and shoes. To date, this is the

only study that I know of that takes a stab at such an estimate. Also, note that the turnover on the

TSE during the study period was substantially greater than say on the NYSE (about two to three

times as great).

If trading is generally a money losing proposition for individual investors, what about

institutional trading? That is, does the negative relationship between returns and transactions for

individuals hold for institutions? For example, institutions tend to have lower transaction costs,

so they may not suffer as poorly. In the case of mutual funds, Carhart (1997, p. 69) finds: ―that

transaction costs describe most of the unexplained mutual fund performance.‖ In short, they are

important and can be a significant drag on performance. In addition, he found about 77%

turnover in his sample covering mutual fund monthly returns from 1962 through 1993




(inclusive), which is roughly comparable to individual investor turnover. In addition, regarding

costs of transacting Carhart (1997, p. 69) estimates it to be about 95 BPs per round-trip

transaction vs. Barber and Odean‘s (2000, p. 779) value of about 245 to 303 BPs for individual

discount brokerage clients (median and mean round-trip trading costs, respectively). Thus, it has

been roughly 1/3rd cheaper to transact for mutual funds vs. discount brokerage clients.

Nevertheless, it is costly and the transactions generally do not justify the trades made whether

individual or institutional investor.

As Barber and Odean (2000, p. 799) noted: ―It is unlikely that mutual fund managers buy and

sell stocks for the pure joys of trading despite the fact that this trading lowers the expected

returns of their shareholders.‖ This is related to one reason often given for individual investor

losses associated with excessive trading, namely a gambling rationale. It has been proposed that

individual investors may derive some satisfaction, as with gambling, from the mere ability to

gamble and thus set aside money they will knowingly have a strong tendency to be on the losing

side of most transactions. Barber and Odean (2000) looked into this for individuals and did not

find this to be a credible explanation. Regarding institutional investors like mutual funds, they

found the argument unconvincing based at least in part on empirical evidence like Carhart

(1997), Jensen (1969), and Malkeil (1995).377

Trading tends to hurt performance for both, but

because transaction costs tend to be higher for individuals, it just hurts individual returns more.

377 Although generally the average mutual fund underperforms basic market indices, there is one possiblycontradictory piece of evidence that could be interpreted to suggest trading enhances performance under certainspecific circumstances. For example, Grinblatt and Titman (1994, pp. 435-437) identify excess returns attributableto a subset of 279 equity mutual funds under study (December 1974 through December 1984); but they attributetheir unexpected significant statistic to the differential between their set of high turnover funds and low turnoverfunds. In fact, the cause seems to largely come from negative returns caused by their low turnover funds doing




REFERENCES

Barber, B., Lee, Y., Liu, Y., T. Odean, ―Just How Much Do Individuals Investors Lose by

Trading?‖, Working Paper, October 2006, 1-28.

Barber, B., and T. Odean, ―Trading is Hazardous to Your Wealth: The Common Stock

Investment Performance of Individual Investors‖, Journal of Finance, Volume LV, Number 2,

April 2000, 773-806.



Black, F., ―Noise‖, Journal fo Finance, Volume 41, Issue 3, July 1986, 529-543.

relatively poorly (0.8 percent per year vs. -1.3 percent per year). This somewhat truely anomolous result may be dueto: (1) their sample, and/or (2) the power of their tests.




Carhart, M., ―On Persistence in Mutual Fund Performance‖, Journal of Finance, Volume 52,

Issue 1, March 1997, 57-82.

Daniel, K., Hirshleifer, D., and A. Subrahmanyam, ―Investor Psychology and Security Market

Under- and Overreactions‖, Journal of Finance, Volume LIII, Number 6, December 1998, 1839-

1885.

De Bondt, W., and R. Thaler, ―Financial decision making in markets and firms: A behavioral

perspective‖, in R. Jarrow, V. Maksimovic, and W. Ziemba (Editors), Handbooks in Operations

Research and Management Science, Finance, Volume 9, Elsier, Amsterdam, 1995, 385−410.




417.

Glaser, M., and M. Weber, ―Overconfidence and Trading Volume‖, Working Paper, April 14,

2003, 1-55.

Glaser, M., and M. Weber, ―Which past returns affect trading volume?‖, Journal of Financial

Markets, Volume 12, Issue 1, February 2009, 1-31.




Grinblatt, M., and S. Titman, ―A Study of Monthly Mutual Fund Returns and Performance

Evaluation Techniques‖, Journal of Financial and Quantitative Analysis, Volume 29, Issue 3,

September 1994, 419-444.

Grossman, S., and J. Stiglitz, ―On the Impossibility of Informationally Efficient Markets‖,


Huang, R., ―The Monetary Approach to Exchange Rate in an Efficient Foreign Exchange

Market: Tests Based on Volatility‖, Journal of Finance, Volume 36, Issue 1, March 1981, 31-41.

Jensen, M., ―Risk, The Pricing of Capital Assets, and The Evaluation of Investment Portfolios‖,

Journal of Business, Volume 42, Isue 2, April 1969, 167-247.

LeRoy, S., and R. Porter, ―The Present-Value Relation: Tests Based on Implied Variance

Bounds‖, Econometrica, Volume 49, Issue 3, May 1981, 555-574.

Malkiel, B., ―Returns from Investing in Equity Mutual Funds 1971 to 1991‖, Journal of Finance,

Volume 50, Issue 2, June 1995, 549-572.




Mankiw, N., Romer, D., and M. Shapiro, ―Stock Market Forecastability and Volatility: A

Statistical Appraisal‖, Review of Economic Studies, Volume 58, Number 3, Special Issue: The

Economics of Financial Markets, May 1991, 455-477.

Marsh, T., and R. Merton, ―Dividend Variability and Variance Bounds Tests for the Rationality

of Stock Market Prices‖, American Economic Review, Volume 76, Issue 3, June 1986, 483-498.

Odean, T., ―Volume, Volatility, Price, and Profit When All Traders Are Above Average‖,Journal of Finance, Volume 53, Issue 6, December 1998, 1887-1934.

Odean, T., ―Do Investors Trade Too Much?‖, American Economic Review, Volume 89, Issue 5,

December 1999, 1279-1298.



Shiller, R., ―Do Stock Prices Move Too Much to be Justified by Subsequent Changes in

Dividends?‖, American Economic Review, Volume 71, Issue 3, June 1981a, 421-436.

Shiller, R., ―The Use of Volatility Measures in Assessing Market Efficiency‖, Journal of

Finance, Volume 36, Number 2, May 1981b, 291-304.






Sias, R., ―Volatility and the Institutional Investor‖, Financial Analysts Journal, Volume 52,


Statman, M., Thorley, S., and K. Vorkink, ―Investor Overconfidence and Trading Volume‖,

Working Paper, March 2003, 1-52.

Tversky, A., and D. Kahneman, ―Loss Aversion in Riskless Choice: A Reference Dependent

Model‖, Quarterly Journal of Economics, Volume 106, Issue 4, November 1991, 1039-1061.







Chapter 15: Corporate events

Under the original textbook definition of market efficiency, information, and especially public

information, is supposed to be embedded into pricing as it becomes available to ―the market‖. In

short, no ―excess returns‖ or ‗free lunch‘ should be available to investors trading on such

information.378 For example, and as mentioned in the chapter on overreaction and underreaction,

earnings announcements are arguably the most available and researched of all ‗publically‘

available financial information, yet they don‘t appear to be incorporated into pricing in a

normatively efficient manner. Specifically and as previously mentioned, Bernard and Thomas

(1989) reported extensive underreaction that resulted in risk-adjusted excess returns or a ‗free

lunch‘ of about 18% on an annual basis, which could be enhanced by focusing on small- vs.

large-cap stocks (Bernard and Thomas (1990)). How can this be? Furthermore, if this occurs for

earnings, what about pricing ―efficiency‖ for other less public and popular announcements?

Apparently, although there are questions as to statistical power, among other things, earnings

announcements not being ―efficiently‖ embedded into pricing are not alone.

In fact, according to financial market research, essentially every measurable corporate event has

at least some evidence of being ‗anomalous‘. Overall, as a general statement it seems that

extensive underreaction to most documented corporate events is the norm. The real substantive

debates concerning much of the descriptive corporate event literature typically revolve around

the statistical confidence of the ‗free lunch‘ and whether one can even be defined or tested.

378 Typically, any statistically systematic deviation of equity returns from zero is considered evidence of normativemarket inefficiency.




Of course, if an ‗anomaly‘ is the norm, it is not an anomaly, it is normal. Therefore, like so much

in finance, when actually examined, the balance of the evidence on corporate events is: (1) not

supportive of the EMH/EMT, and (2) even ignoring that evidence, it tends not to actually fit

normative theory very well, if at all. Also, like ‗earnings drift‘ much of the evidence is based on

‗event studies‘ that attempt to identify excess returns by: (1) identification of an announcement

date, and (2) assuming some asset pricing model is appropriate for controlling for risk. Although

announcement dates are less debatable (i.e., outside of such events as corporate bankruptcy),

there is considerable debate about the asset model used to control for risk. In effect, the old

standby problem of testing both normative market efficiency and an asset pricing model (i.e.,

essentially two hypotheses) applies to event studies looking to support or reject market efficiency

with respect to accepted corporate events.

In addition, of course, there is considerable counter evidence, but the most plausible explanation

is not EMH/EMT based. Therefore, while there is a long, and growing, list of empirical work in

the area of corporate events one cannot say, at this time, we have conclusively identified the

cause or causes of what appears to be the general rule of underreaction. That noted, what we can

normatively say, either generally or specifically, they all seem to be ‗anomalous‘ or have some

‗anomalous‘ component to them.

Thus, in this chapter I will not attempt to review the hundreds or thousands of more descriptive

studies in the area of corporate events, but rather try to present a brief outline of the area as it




relates to behavioral finance. First, a list of the common documented corporate events and then a

list of some evidence whether there appears to be normative ‗free lunch‘ for each will be

presented. Second, some discussion of the arguments of whether we can even say anything about

corporate events, especially as it relates to the ―power‖ of the statistical tests employed. Finally,

a few comments/summary as events studies of corporate events relate to behavioral finance.

A LIST OF CORPORATE EVENTS

Regarding the events themselves, here is my own attempt at a list (and ignoring corporate

bankruptcy):




Common Corporate Events

Event Description Evidence

Unambiguously

Supportive of EMH/EMT?

Event

Studies

Offerings:Initial Public Offerings (IPOs) No Yes

Seasoned equity offerings No Yes

Debt offerings No Yes

Cash flow related announcements:

Earnings No Yes

Dividends No Yes

Repurchases/stock buybacks No Yes

Stock and cash financed mergers & tender offers/M&A No Yes

Stock splits No Yes

Arguably the one major set of events that are missing are bankruptcies and restructurings

(covered in a previous chapter). As mentioned, those types of events are also generally not

supportive of normative market efficiency. Regarding examples of event studies and

approximate excess returns/‘free lunches‘ for the events listed:




Common Corporate Events – some excess return examples

Event Description Examples of approximate

excess returns found

Time

Period

Event Study

Offerings:Initial Public Offerings (IPOs) Timing and time matters:

e.g., during the Internet

bubble +65%

-23.4%

1st day

3 yr. avg.

Ritter & Welch

(2002, pp. 1822,

1817)

Seasoned equity offerings -44%

(note, same finding for IPOs)

5 yrs. Loughran & Ritter

(1995, p. 46)

Debt offerings (debt IPOs) Exchange matters: For

example OTC +4.66% vs.

NYSE/AMEX -1.78%

Bond rating matters: Low

grade +1.86% vs. high grade

-2.88%

Average equity move of -

6.35%

1st day

1st day

2 day avg.

Datta et al. (1996,

p. 391)

Data et al. (2000, p.

731)

Cash flow related announcements:Earnings Size matters: e.g., medium-

cap 10% vs. large-cap 4%

3 quarters Bernard & Thomas

(1990, p. 323)

Dividend reductions &

omissions

-6.85% to -11.04%

(depending on model)

1 year Liu et al. (2008, p.

996)

Repurchases/stock buybacks +12.1%

Value matters: e.g., ‘value’

stocks +45.3%

+3.5%

3 years

3 years

5 days

Ikenberry et al.

(1995, p. 181)

Ikenberry et al.

(1995, p. 206)

Stock and cash financed mergers& tender offers/M&A

+0.59% to 0.86% (about +7%

to +10% annualized)

1 month Baker & Savasoglu

(2002, p. 103)

Stock splits & reverse splits Splits: +7.05% & +11.87 splits

Reverse splits: -10.76% & -33.90%

1 year &

3 years

Desai & Jain (1997,

p. 409)

Source: Various.




Again, as previously noted, every corporate event listed has at least some strong evidence of

abnormal returns (i.e., according to normative conventions and models at the time the studies

were made). Therefore, what are often called ‗anomalies‘ are in fact not anomalous at all; rather,

they are the norm.

What is interesting is that in some cases, for example IPOs, you can get an initial relatively

short-run overshooting then a relatively long-run underreaction. But, again, as a general rule,

most corporate events seem to support underreaction. Thus, pricing isn‘t fully reflecting basicinformation and it seems to take in many cases up to one year, and in some cases beyond one

year, to do so.

In addition, for cash flow related variables (i.e., earnings and dividends) it has been stated, and

seems quit plausible that that which is driving earnings underreaction (also called ―Post Earnings

Announcement Drift‖ or ―PEAD‖) is also driving dividend announcements (e.g., see Liu et al.

(2008)). For students of behavioral finance, and even most who have studied accounting, this

should not be too surprising. It should not be shocking that the largely unknown mechanism

driving earnings to be reflected in stock prices over about three quarters works according to

about the same process for dividend surprises. Remember, finance is about discounted cash

flows or present values. In this case you have the basic periodic cash flow for equities (i.e.,

dividends) and what is often an accounting accruals adjusted proxy for cash flow available for

dividends (i.e., earnings after accounting and/or tax adjustments) behaving in similar ways. Of

course, they should behave in similar ways over large samples of firms because they should and




often do reflect similar cash flow dynamics. In short, dividends should move as earnings move,

because they are largely the same things. Thus, there really is no mystery here; but alas much

like attributing overeaction to the January effect, it doesn‘t explain what is so ‗efficient‘ about

basic cash flow changes (i.e., whether represented by dividends or earnings) taking up to about

one year to get incorporated into common stock prices. Therefore, explaining a normative

mystery with a normative mystery does not explain the normative mystery (i.e., either one of

them).

CONCERNING THE ―POWER‖ OF THE EVENT STUDY TESTS AND RELATED

ISSUES379

Even if the ‗model‘ is EMT inspired and rationalized and/or the test EMT sanctioned, a common

EMH/EMT supporter retort to events study results not supportive of ‗market efficiency‘ is that

379 See e.g. Campbell et al. (1997 pp. 149-180) for a thorough treatment of event studies and related issues.




the model is wrong or misspecified and therefore the test is misapplied.380

This is commonly

called the ―joint hypothesis problem.‖ In short, any test of market efficiency (e.g., event studies

tests) is a ―joint test‖ of efficiency and the asset pricing model used. The logic is that if the asset

model applied is not the true model, then we cannot really say anything about the efficiency (or

inefficiency) of the market (i.e., the efficiency test is not valid). Therefore, given that ―all models

are wrong‖, all empirical tests of market eff iciency (which includes all event studies of corporate

events) are misapplied and meaningless (i.e., according to this line of argument).

While the ―joint hypothesis problem‖ seems truly to be a problem, and potentially invalidates

most, if not all, normatively based descriptive research in finance, the arguably most important

issue is likely the ―power‖ of the tests themselves. That is, given a null hypothesis of the public

announcement of the event has no impact on security returns (typically common equity returns)

after the announcement, what is the probability of incorrectly rejecting such a hypothesis?

Apparently, the answer is that the probability is and has been typically quite high (see, e.g.,

Khotari and Warner (2006, pp. 14-20)). Therefore, it isn‘t just that the model is wrong (i.e., one

or more assumptions are descriptively incorrect, which they are), but that in addition one or more

of the assumptions of the statistical test is wrong. For example, most event studies tests assume

that the cross-sectional distribution of security returns is normally distributed. Strictly speaking,

it rarely is (for that matter, if they even can be). It‘s not just the distribution of returns that may

be problematic, other related issues include the following (see Khotari and Warner (2006) for a

more detailed review):

380 Of course, EMH/EMT supporters originally felt the models and tests were fine as long as the results weregenerally interpreted as supportive of market efficiency. Again, by my reckoning, the most important issue is thatthese studies focus on returns and not whether the price levels themselves are efficient.




Particularly problematic are long horizon event studies (long horizon is typically

regarded as one year or more). In general, the ―joint hypothesis‖ problem is more

problematic for longer horizon studies, and the power of the test(s) is/are lower. Thus,

short horizon studies are less reliant on the model employed to determine efficiency or

lack thereof.

In addition to the minimally implicit assumption of using the correct model (which is

unlikely, if not impossible), different models have different properties. Therefore, not

only is the model ―wrong‖, but it may not be very useful to prove or disprove efficiency.The precision and bias of each model will likely differ; therefore, minimally, it is difficult

to compare event studies.

In addition to the likely problematic assumption concerning the normality of returns

across the cross-section of returns, there is the assumption of returns being independently

distributed across time.381

This is unlikely as event-time clustering is, by design and

definition, not conducive to the independence requirement. Therefore, due to the nature

of the event study methodology, this assumption is wrong and the resulting inferences

incorrect. Unless a correction is made, the estimated standard deviation is biased

downward, and the resulting test statistic is therefore biased upward.

As with the previous issue, the nature of events themselves may preclude unbiased tests.

For example, and assuming some leakage of information before the event announcement

date (which is likely in many, if not most, cases) and/or managers are timing the market

(i.e., systemic misvaluation), the returns surrounding an event, and most importantly

381 Therefore, returns are normatively assumed to be independent through time and in the cross-section. Both aredubious assumptions.




before the event, are anything but representative of the normal distribution of returns for

that security. Therefore, unless the prior event date data is clean or not impacted by the

upcoming event (i.e., given the underlying statistical assumptions), then the test cannot be

unbiased.382 For example, price volatility tends to increase around a corporate event

announcement (even before the announcement, which is normatively not supposed to

happen). Clearly, many events are at least partially anticipated which largely defeats one

critical basic assumption made by event studies.383

The descriptive reality of nonrandom size and industry characteristics works against the

notion that increased noise decreases power.384 Furthermore, even ignoring more specific

sample characteristics, volatility varies through time. This alone would tend to introduce

diminished power for event study tests. Therefore, firm characteristics and even the

calendar can dramatically impact the power of event studies.

One time events (e.g., a single merger) are more difficult to adequately measure than

repeat periodic events (e.g., earnings announcements). For example, mergers

announcements tend to be smaller sample studies than earnings announcements.

For all the mentioned reasons alone, it can be generally hard to resolve whether

something is a mispricing and/or mismeasurement. We may conclude that there is

mispricing when none exists or conclude there is no mispricing when in fact there is.

382 Otherwise, it might be possible to compare to pre-event periods and adjust the bias accordingly, but then thatwould assume that the observable pre-event period(s) is/are unbiased. In a sense, in almost any statistical endeavorin economics one must rely on assumptions that are almost invariably wrong.383 Even more damming is the fact that, for example, managers clearly try to time stock and/or debt issuance, yetevent studies ignore this fact. As pointed out by Khotari and Warner (2006, p. 31) this will tend to result in findingsof market efficiency, particularly for the Jensen‘s alpha approach, when in fact it doesn‘t exist. 384 In addition, value characteristics can have significant effects (e.g., samples strongly biased toward low or highbook-to-market ratio firms).




―However, absent a sound economic rationale motivating the inclusion of the size, book-

to-market, and momentum factors, whether these factors represent equilibrium

compensation for risk or they are an indication of market inefficiency has not been

satisfactorily resolved in the literature (see, e.g., Brav and Gompers, 1997).‖ Khotari and

Warner (2006, p. 27) In other words, not just is the model a problem, but there is an issue

of whether the ‗risk factors‘ associated with models used in event studies are really that.

For example, what exactly is the justification for a size ‗risk factor‘? Again, are we just

measuring the size ‗anomaly‘ and calling it a ‗risk‘, even if it isn‘t, or is it somethingelse? Behavioral finance types would tend to say that size, value, momentum, etc.

‗factors‘ are not really purely ‗risk factors‘ but something more akin to ‗characteristics‘.

Therefore, effectively, the ‗model‘ is not controlling for various ‗risks‘ but actually

confounding risk with something beyond risk. Therefore, the ‗model‘ is not just ―wrong‖,

but misspecified.

In summary, almost regardless of the particular event study method, it is difficult, if not

impossible, to confidently state, especially concerning long horizon studies, that a study supports

or rejects market efficiency. Especially more recently for long horizon event studies, there are

suggested methods for correcting such things as skewness, non-normality, cross-correlation,

specification bias, lack of independence of returns, industry and other dimensions of

overrepresentativeness, etc., but none to my knowledge corrects all problems all the time.

Therefore, event studies, particularly beyond one year horizons typically infer a degree of

statistical accuracy that just doesn‘t exist. Actually nonrandom samples, such as those found in

event studies (actually most studies purportedly examining market efficiency) tend to reject the




null hypothesis of no abnormal performance more often than the studies infer from their stated

statistical values (see, e.g., Jegadeesh and Karceski (2004)).385

SO, WHAT CAN WE SAY ABOUT CORPORATE EVENT STUDIES?

Even if we cannot infer too much from long horizon studies, in the final analysis we can say the

following:

1. Corporate events can have significant impacts on stock prices.

2. All major researched corporate events show some signs of market inefficiency (i.e., as

normatively defined), especially at horizons under one year.

3. Given that all models and methodologies are wrong, especially at horizons of more than

one year, we should be somewhat circumscribed in our declarations.

In short, event studies are much like most of current finance, right, wrong, and messy.

385 Is it merely coincidental that most journals that publish such studies have a clear bias to reject such hypotheses?




REFERENCES

Baker, M., and S. Savasoglu, ―Limited arbitrage in mergers and acquisitions‖, Journal of

Financial Economics, Volume 64, Issue 1, April 2002, 91-115.

Bernard, V., and J. Thomas, ―Post-Earnings-Announcement Drift: Delayed Price Response of

Risk Premium?‖, Journal of Accounting Research, 1989 Supplement – Current Studies on the

Information Content of Accounting Earnings, Volume 27, Number 3, Autumn 1989, 1-36.

Bernard, V., and J. Thomas, ―Evidence that Stock Prices Do Not Fully Reflect the Implications

of Current Earnings for Future Earnings‖, Journal of Accounting and Economics, Volume 13,


Campbell, John, Lo, Andrew, and A. MacKinlay, The Econometrics of Financial Markets,

Princeton University Press, Princeton, New Jersey, 1997.

Datta, S., Iskandar-Datta, M., and A. Patel, ―The Pricing of Initial Public Offers of Corporate

Straight Debt‖, Journal of Finance, Volume 52, Number 1, March 1997, 379-396.

Desai, H., and P. Jain, ―Long-Run Common Stock Returns following Stock Splits and Reverse

Splits‖, Journal of Business, Volume 70, Issue 3, July 1997, 409-433.




Ikenberry, D., Lakonishok, J., and T. Vermaelen, ―Market underreaction to open market share

repurchases‖, Journal of Financial Economics, Volume 39, Issues 2-3, October 1995, 181-208.

Jegadeesh, N., and J. Karceski, ―Long-Run Performance Evaluation: Correlation and

Heteroskedasticity-Consistent Tests‖, Working Paper, April 2004, 1-40.

Khotari, S., and J. Warner, ―Econometrics of Event Studies‖, Center for Corporate Governance –

Working Paper, May 2006, 1-53.

Liu, Y., Szewczyk, S., and Z. Zantout, ―Underreaction to Dividend Reductions and Omissions?‖

Journal of Finance, Volume 63, Number 2, April 2008, 987-1020.

Loughran, T., and J. Ritter, ―The New Issue Puzzle‖, Journal of Finance, Volume 50, Number 1,

March 1995, 23-51.

Ritter, J., and I. Welch, ―A Review of IPO Activity, Pricing, and Allocations‖, Journal of

Finance, Volume 57, Issue 4, August 2002, 1795-1828.




Chapter 16: Can we learn our way to normative market efficiency?386

―Economists usually contend that in natural settings people either learn from personal experience

or are surrounded by institutions – such as advice of relatives or consultants – that provide advice

in unfamiliar situations. How well people learn from personal experience, and from the

experience of others, is therefore a central question in the debate about the behavioral

foundations of economics.

Our research suggests grounds for pessimism about both kinds of learning.‖

Camerer et al. (1989, p. 1246)

The above quote is from an experimental study showing that ‗experts‘ display the ‗curse of

knowledge‘. As opposed to providing a ―steady hand‖ to the ignorant investor, professional

investors and traders actually often display more bias than the ignorant. Haigh and List (2005, p.

523) found something similar when they checked for myopic loss aversion (―MLA‖ – the

combination of mental accounting and loss aversion) among a group of ―professional‖ CBOT

traders and compared them to a control group of students: ―Yet, much like certain anomalies in

the realm of riskless decision-making, these behavioral tendencies may be attenuated among

professionals. Using traders recruited from the CBOT, we do indeed find behavioral differences

386 This chapter could have been the most involved and far reaching of those in this book; it is not. I have decided tocut it short for various reasons. First, I am not an expert, but have done some reading and applied some commonsense. Second, I wanted to keep as focused on the financial markets and pricing therein as much as possible. Third,even though I think this is a critical topic, I want it to be as conceptually focused as possible, which is very difficultgiven that the subject needs to be focused on financial market pricing. Therefore, I choose to pick and choose what Ithought would be helpful for a reader trying to come to grips with behavioral finance and open to learning how tolearn the subject matter.




between professionals and students, but rather than discovering that the anomaly is muted, we

find that traders exhibit behavior consistent with MLA to a greater extent than students.‖ In other

words, student suck, but the traders are worse. Therefore, much like our search for the mythical

rational arbitrageur, when we begin to look for a correcting influence we often find agents who

are as bad or worse than the agents they are mythically supposed to help and/or correct (i.e., from

a normative perspective).

Again, can we learn our way to traditional market efficiency? Answer: Of course, but it is

unlikely. Remember there is the issue of the strict economic definition (which is based currently

on specific mathematical definitions) of rationality. As humans we just don‘t adhere to the

normative coherence and invariance economics demands. Therefore, the mathematical

specification of normative choice under uncertainty just cannot hold, that is, unless we learn to

be strictly economically rational (i.e., in the current normative sense). Is that possible? Answer:

Possibly for some, but probably not for all, minimally because of something called the ‗dual

burden‘ (which is related to overconfidence). If we at least had a strictly rational group of

economic agents, then we get back to the rational arbitrageur argument of this group correcting

mispricing in all markets all the time, but, of course, they would also need to have sufficient

capital to impact pricing sufficiently. If we at least had a group ready to correct mispricing, how

likely would it be that they would and/or could? Answer: It depends (minimally, it depends on

who is strictly rational and how much relative capital they possess at any given time).




―Economists presume that experimental subjects do not work for free and work harder, more

persistently, and more effectively, if they earn more money for better performance. …

In the kinds of tasks economists are most interested in, like trading in markets, bargaining in

games and choosing among gambles, the overwhelming finding is that increased incentives

do not change average behavior substantially (although the variance of responses often

decreases).‖ Camerer and Hogarth (1999, p. 1) Much like the ‗professional‘ vs. amateur

canard/normative myth, we have the normative myth that monetarily incentivized ‗professionals‘

will correct pricing, because they will make more money by so doing. As mentioned elsewhere

in this book, there certainly are some more rational arbitrageur types for which this is true, but it

presupposes that money will flow overwhelmingly to them, when in fact it often doesn‘t (e.g.,

during the front leg of a bubble).

Furthermore, many economists believe people will ‗learn their way out‘ of their irrational biases

(i.e., irrational from a normative economics perspective). Is there hope of this? Given current

incentives and associated financial market structure, it is unlikely. Although, some effects that

cause violations of economic axioms can be diminished by incentives and structure. Pointedly,

Camerer and Hogarth (1999, p.7) note that: ―no replicated study has made rationality violations

disappear by raising incentives‖.

Also, it is important to remember that even the so called professionals (e.g., WSSs, earnings

analysts, etc.) not only get it wrong, but they can be systematically wrong. This is somewhat akin

to a professional soccer player scoring for the opposing side more than they score for their own




team. If this sort of systematic bias (which you do not systematically see in professional sports)

toward pushing prices away from fundamental value occurs among ‗professionals‘ in the

financial markets, then why would we think the incentives are there to train the unwashed masses

(i.e., amateurs) to push prices toward their fundamental values? Well, you wouldn‘t.

In addition to diverging from the strict normative economic definition of rationality and the fact

that even many finance ‗professionals‘ are systematically biased, there is the issue of the

―exactingness‖ of the task(s) itself/themselves. Exactingness is the cost of failure to learn. For

example, what is the cost of not being able to learn to properly price a T-bill, or for a WSS to

forecast the stock market?

Furthermore, many, or even most, of us just may not be able to do what it would take to move

prices toward their fundamental values. Think of what tasks one would need to be able to

perform and the degree of precision required for the tasks required, and under what conditions it

would be required. Therefore, not only would a group of relatively well capitalized economic

agents have to exist, but they would have to perform cognitive tasks that are likely beyond the

majority of the population under conditions that don‘t seem to exist today. What are the odds of

this happening? If the current state of finance and the financial markets are any indication, it is

―not bloody likely.‖387

387 In the movie he was answering a rhetorical question about whether a soldier would die for ―King and country‖ if they knew what they were getting into.




And if that wasn‘t enough, there is the issue of the ‗dual burden‘. I‘m confident there are other

problems, but this alone should give one pause.

THE ‗DUAL BURDEN‘

―We argue that when people are incompetent in the strategies they adopt to achieve success and

satisfaction, they suffer a dual burden: Not only do they reach erroneous conclusions and make

unfortunate choices, but their incompetence robs them of the ability to realize it. … three points.

The first two are noncontroversial.

1. First, in many domains in life, success and satisfaction depend on knowledge, wisdom, or

savvy in knowing which rules to follow and which strategies to pursue. … .

2. Second, people differ widely in the knowledge and strategies they apply in these domains

(Dunning, Meyerowitz, & Holzberg, 1989; Dunning, Perie, & Story, 1991; Story &

Dunning, 1998), with varying levels of success. Some of the knowledge and theories that

people apply to their actions are sound and meet with favorable results. Others, … are

imperfect at best and wrong-headed, incompetent, or dysfunctional at worst.‖




3. The ‗dual burden‘ issue.

(Kruger and Dunning (1999, p. 1121))

The ‗dual burden‘ issue seems likely related to overconfidence. Essentially, whenever I am

ignorant of some area, for example financial market pricing, I suffer two burdens. First, because

I don‘t know financial market pricing models I cannot value financial market securities. Second,

because I don‘t know about financial market pricing models or even say basic finance (i.e., the

first burden), I find it impossible, or at least difficult, to find an ‗expert‘ to help me learn them, or to entrust to value securities for me. The second part is a practical circularity that can be broken

by learning, that is, to the extent I am capable of learning about financial market pricing, which I

may or may not be.388 The fact that most people, for example, invest in securities for which they

haven‘t the slightest idea whether they are priced ―correctly‖ or not, would clearly indicate they

must have some exaggerated level of confidence in themselves and/or others.

Kruger and Dunning (1999) analyzed three areas of ―metacognitive skills‖: (1) humor, (2) logical

reasoning, and (3) grammar. The results were similar in all three. They found:

1. ―Competence begets calibration‖ (hence, the ‗dual burden‘ without competency).

388 There is a clear difference between most tasks in the physical domain (e.g., shooting a basketball) and most in thecognitive domains (e.g., learning particle physics). Most tasks in the physical domain can be performed and/orevaluated by most people, yet in the cognitive domains this is typically far from true (e.g., financial market pricing).




2. ―The burden of expertise‖389(competent people tend to underestimate their abilities

almost as systematically as the least competent systematically overestimate their

abilities), but there are far fewer of them (roughly 1/10th

seem to be underconfident).

Based on the results of the first three tests, they also examined the extent to which learning was

possible (a fourth test). They found that learning may be possible under stringent feedback

and self-assessment, otherwise calibration could get worse (i.e., based on the fourth tests‘

results). The people in the lower quartile overestimated their competence by, on average, about

50% (i.e., they were in the 12

th

percentile and estimated themselves to be in the 62

nd

percentile).390 It is interesting that the competent have a burden of overestimating the ability of

their peers (and/or underestimating their own abilities).

When viewing the next three graphs, note that if people were well calibrated (i.e., had an

accurate and unbiased opinion of themselves), then people‘s ―perceived ability‖ (the dark line)

would approximately overlap the light line (i.e., the 45 degree line). If the black line is above the

light line, that is a measure of overconfidence; conversely, if the light line is above the black line,

that is a measure of underconfidence (which is relatively rare among humans, regardless of the

389 Obviously, this ―burden of expertise‖ differs from the one documented where ‗experts‘ showed more inefficient

bias than nonexperts.390 The tests were based on 65 Cornell University undergraduates for the humor test, 45 for the logical reasoningtest, and 84 for the grammar test. The humor test was based on 30 jokes from Woody Allen, Al Frankin, and a book of ―really silly‖ pet jokes by Jeff Rovin, which were sent out to individual professional comedians which rated themon a scale of 1 to 11 (not funny to very funny). The experts were in very strong agreement on the ratings. The logicalreasoning test was a set of 20 questions from the LSAT. The grammar test was based on the worth they assigned toAmerican Standard Written English (ASWE) from a 20 question test based on questions taken from a NationalTeacher Examination preparation guide. Also, a fourth test was given to 140 Cornell undergraduates from a singlehuman development course that was given extra credit for participating. Completed tests then went over theirresults, the asked to re-estimate their results.




subject matter, as you will see). The first graph is on humor, then logical reasoning, and finally

grammar.

HumorFigure 1. Perceived ability to recognize humor as a function of actual test performance (Study 1).From: Kruger, J., and D. Dunning, ―Unskilled and Unaware of It: How Difficulties in Recognizing One‘s OwnIncompetence Lead to Inflated Self-Assessments‖, Journal of Personality and Social Psychology, 1999, Vol. 77, No.6, p. 1124.




Logical reasoningFigure 2. Perceived logical reasoning ability and test performance as a function of actual test performance (Study 2).From: Kruger, J., and D. Dunning, ―Unskilled and Unaware of It: How Difficulties in Recognizing One‘s OwnIncompetence Lead to Inflated Self-Assessments‖, Journal of Personality and Social Psychology, 1999, Vol. 77, No.6, p. 1125.




GrammarFigure 3. Perceived grammar ability and test performance as a function of actual test performance (Study 3).From: Kruger, J., and D. Dunning, ―Unskilled and Unaware of It: How Difficulties in Recognizing One‘s OwnIncompetence Lead to Inflated Self-Assessments‖, Journal of Personality and Social Psychology, 1999, Vol. 77, No.6, p. 1126.

Regardless of test, again, the bottom quartile people overestimated their competence by, on

average, about 50%. There are several things to note:

1. There was systematic overconfidence at the bottom and underconfidence at the top, in all

three tests.

2. Overall, overconfidence is generally pronounced in all three (by a great deal).




3. With respect to logical reasoning, people in the middle show relatively good calibration,

whereas those at the bottom are worst (i.e., in terms of difference between actual and

perceived ability).

Even after repeated feedback (i.e., the fourth study), there was only slight improvement with

respect to logical reasoning and learning (i.e., the perceived and actual test scores begin to

steepen like the perceived ability line), not for humor or grammar.

But why? (See Kruger and Dunning (1999, p. 1131-1132))

1. ―People seldom receive negative feedback about their skills and abilities from others

in everyday life.‖

2. ―Some tasks and settings preclude people from receiving self-correcting information

that would reveal the suboptimal nature of their decisions.‖ This is espcially true, for

example, of WSSs.

3. ―The problem with failure is that it is subject to more attributional ambiguity than

success. For success to occur, many things must go right: The person must be skilled,

apply effort, and perhaps be a bit lucky. For failure to occur, the lack of any one of these

components is sufficient.‖

4. ―Incompetent individuals may be unable to take full advantage of one particular kind of

feedback: social comparison.‖ Again, the ‗dual burden‘ issue impedes progress or even

the ability to learn.

Think of the extreme counter-example, say professional athletes in an individual sport or in a

team sport where copious statistics on individual performance are kept. Do the best professional




players overestimate their ability in their sport by 50%? That is highly unlikely, not just because

feedback tends to be fast and blunt, but it is also typically physical. For example, if you miss a

penalty kick at the end of regulation play, it is not only physically obvious who missed, but your

team will tend to lose. Therefore, there is a clear difference between the physical domain and, for

example, most areas of finance (which are cognitive in nature).

Also, the authors mention other issues involved with feedback:

1. ―Self -serving trait definitions‖, such as ―selective recall of past behavior‖ and ―thetendency to ignore proficiency in others‖.

2. ―Incompetent are likely to be unaware of their lack of skill.‖

3. ―In other domains, however, competence is not wholly dependent on wisdom, but

depends on other factors, such as physical skill.‖ However, coaches may be skilled in the

strategies and tactics of the sport, but couldn‘t, for example, dunk a ball if their life

depended on it.

4. ―Finally, in order for the competent to overestimate themselves, they must satisfy a

minimal threshold of knowledge, theory, or experience that suggests that they can

generate correct answers. In some domains, there are clear and unavoidable reality

constraints that prohibit this notion. For example, most people have no trouble identifying

their own inability to translate Slovenian proverbs, reconstruct an 8-cylinder engine, ... .

In these domains, without even an intuition of how to respond, people do not

overestimate their ability. Instead, if people show any bias at all, it is to rate themselves

as worse than their peers (Kruger, 1999).‖




5. ―People are more miscalibrated when they face difficult tasks, ones for which they fail to

possess the requisite knowledge, than they are for easy tasks, ones for which they possess

that knowledge (Lichtenstein & Fischhoff, 1977). … the end result is a large degree of

overconfidence.‖

In essence, failure is fairly easy to achieve, but well deserved success in the more cognitive

realms requires not only skill and knowledge, but a level of self awareness that most of us are not

programmed with (and probably some luck).

It isn‘t that people don‘t want to succeed, but, for several reasons, they may just be unable to.

Specifically, people tend to have ―overly optimistic and miscalibrated views‖ of themselves.

Therefore, if it wasn‘t bad enough that many might not even be able to act like perfect economic

creatures, those that might want to and even be able to might decide they don‘t need to. Blame it

on the ‗dual burden‘.

The obvious problem of the ‗dual burden‘ can obviously be applied to finance. What if I am

unaware of what constitutes ―good performance‖, but I want above average risk-adjusted

returns? Then I would have at least two problems: (1) I cannot generate good performance (i.e.,

ignoring ―luck‖), and (2) I cannot be trusted with selecting a person or firm to generate ―good

performance‖. How can I hope to evaluate others if I am not even aware of the fact I don‘t know

how to evaluate what would constitute ―good performance‖ in the first place? Therefore, the dual

burden issue permeates organizations and the very existence of the inefficiencies themselves.

Then again, there is the issue of learning itself. The only way it seems most people can learn is




under tightly controlled conditions, which are almost the opposite of those encountered in the

‗real world‘ of finance (e.g., monetary tiebacks to errors that are systematic). Combining the

‗dual burden‘ with learning issues makes it rather difficult to be optimistic concerning learning

our way out.

Now pause for a moment and consider an individual and how he or she would evaluate an

investment, particularly after the investment has either gained or lost value (i.e., after controlling

for risk). Consider a simple world where there are only two possible outcomes, a gain or a loss.

In addition, consider that one can either be right or wrong in their assessment of the investment.

The 4 possible outcomes of the investment process

Loss Gain

W

r

on

g

Clearly bad (abandon all

hope, since likely due to

'dual burden') - Type IIerror analogy

Could be worst outcome

(likely result is a falsesense of confidence)

R

i

g

h

t

Not time to panic, if

recognized, this can be

good (need to work IP

towards lower right box)

Ideal, but remain paranoid

R

e

a

s

o

n

Outcome




Keep this 2 x 2 grid in mind. Obviously, two quadrants or boxes are akin to ‗Type I error‘ (also

called a ‗false positive‘)391 and ‗Type II error‘ (also called a ‗false negative‘)392. The ideal is to

gain for the right reason, but imagine you don‘t really understand why an investment gains or

losses, but erroneously think you do. The problem is that there can be cases where we gain but

for an erroneous reason. Now imagine that you indeed experienced a gain, but for the wrong

reason and do not know it because of overconfidence and/or the dual burden. This is potentially

dangerous and normal for most people in finance. Consider the Internet stock and Dot.com ―day

traders‖ during the mid- to late-1990s NASDAQ bubble. Didn‘t they think they knew why theywere making money? Most convinced themselves that they were savvy ―traders‖ with keen

insight. Few admitted to themselves, let alone others, that they were mostly tagging along with a

fundamentally unjustified ‗bubble‘. The proof of this was that most rode the bubble up and back

down after the spring of 2000 (i.e., around when the market peaked). If they really knew what

was going on (and if that was even possible) for the right reason(s), they would have sold

sometime in late 1999 or around the spring of 2000, and knew why they were doing it. In short,

most ―day traders‖ found themselves in the gain box for the wrong reason(s) and then after the

spring of 2000 found themselves in the loss box also for the wrong reason.393 That is normal, and

human psychology is a key component of the process.

Now take it a step further and imagine you are a finance ‗professional‘ advising other people and

you lose their money consistently but are handsomely rewarded for this (e.g., WSSs and earnings

391 Formally, it is rejecting the null hypothesis when the null hypothesis is true. This would be analogous to the gainfor the wrong reason.392 Formally, it is failure to reject the null hypothesis when the null hypothesis is false.393 See Linnainmaa (2003).




analysts). Furthermore, suppose you didn‘t really understand why your firm‘s clients lost or

made money, just that you were rewarded. What would you think? Most think they understand

what is happening and why because why else would they be paid so well? Aren‘t the finance

related labor markets efficient after all? As long as you are paid well you probably don‘t obsess

on the why. Clearly, given their lack of professional prowess, current and past WSSs and earning

analysts don‘t, and one can only surmise the incentives haven‘t been there to correct it.394

This brings us to the very basic issue of exactly how we make a decisions and learn from them,

or don‘t. As De Bondt (2002, p. 607) noted: ―Decision processes are often crucial to decision

outcomes.‖ Implicitly or explicitly economics and finance has not felt compelled to deal with the

actual decision making process that humans go through, including their capacity and/or ability to

learn from their mistakes in the markets. As it turns out, it is important.

394 My assumption is that given current turmoil in the finance industry things have a chance to change, but as of thewriting of this footnote, they haven‘t significantly changed.




HOW DO WE LEARN?

For what follows see, for example, Hogarth et al. (1991). First, a big caveat, the following

applies to repetitive tasks only. Therefore, non-repetitive tasks (e.g., the kind often found in

financial decision making) do not apply, but to the extent learning in general is useful in the

financial realm, I expect some basic lessons will still apply. This is largely why structured

training is focused on repetitive tasks (think military and large public & private bureaucracies),

because it works.

Learning from feedback (again, the following applies only to repetitive tasks):

1 There is a tradeoff between incentives and ―exactingness‖ (exactingness refers to

―severity‖ with which performance is evaluated, for example in a normal teaching

situation it is the grade).

2 Feedback is often ambiguous. It has an inferential component (e.g., a grade on a paper

informs the student of how to write a good paper) as well as an evaluative component

(e.g., the grade tells the student whether the effort was good, bad, or indifferent).

Therefore, confounding things is the extent to which the grade reflects student ability or

instructor grading policy.

3 Specifically, exactingness reflects the severity of penalties imposed for errors.

4 In addition, the outcome may or may not be perceived as consequential. That is, it may or

may not be motivating (e.g., rightly or wrongly, the grade may not be perceived to affect

job prospects).




5 Therefore, motivation is critical to learning, but due to cognitive limitations (e.g., the

―dual burden‖) and/or factual limitations (e.g., it actually may not matter) motivation may

be completely lacking.

6 For example, regardless of actual importance of a task or not but its perceived

importance, the extent to which the individual learning a task has an incentive to learn

the task can vary greatly.

7 Generally, there are two types of incentives: internal (i.e., intrinsic motivations – e.g.,

pride of mastery) and external (e.g., monetary or other explicit rewards based on

performance).

8 There is an interaction between exactingness and incentives that suggests the

following: there is a tradeoff . Therefore, Incentives can help up to point and

exactingness can matter up to a point (i.e., assuming the task is perceived to matter, etc.),

but at the margin too much of either tends to cancel the other out.

9 Intermediate exactingness was found best (i.e., not too lenient and not too exacting) with

no incentives to limited incentives. In lenient environments incentives can improve

performance, while in exacting environments they have deleterious effects.

10 In general, it is also helpful to reveal the evaluation function to the learners (e.g., explain

generally what will be on the test and what you are looking for as a teacher).

Now equate this to the financial markets and try to apply these concepts. In short, most financial

firms (actually most bureaucracies) are poor places to apply learning concepts. In addition, if we




overlay adverse selection and the economic incentives associated with it in the finance industry

we have some real issues to surmount to achieve efficiency, let alone learning.

But of course, in the final analysis it all depends on the individual and the learning task (e.g.,

under some circumstances incentives can be detrimental – see e.g., Greene and Lepper (1974)).

There appear to be tradeoffs between incentives and exactingness, but it all depends. It is clear

that exactingness can be overused. For example, think of the extreme, death or life as feedback

(genetic selection). If someone is pointing a loaded gun at you and asking you to perform brain

surgery, surely the motivation is there but the level of exactingness may have gone too far. Now

think finance, it should be profits or loss, but it isn‘t very often at the institutional level. That is,

most institutional investors are not risking their own capital. Does that influence motivation

and/or exactingness? Obviously, it does, and matters, for example, for hedge funds‘ structure vs.

that of mutual funds.

The key seems to be the nature of the task. For tasks that are more creative and/or complex,

incentives can ―divert needed attention from inference to evaluation, that is, from a concern

about how to do the task to how well one is doing. In tasks that are understood, however,

attention can be more profitably allocated to executing known strategies.‖ (Hogarth et al. (1991,

pp. 735-736) In financial portfolio management, for example, PMs and the annual bonus cycle

seem to mess up the IP. Many are so completely fixated on gaming their bonuses that they are

not trying to improve the IP.




Note that the penalty function in the aforementioned study was symmetric and the task was a

single task (Hogarth et al. (1991)). Additionally, because different evaluation functions induce

different rates of learning, it may not always be a good idea to reveal the evaluation function.

How often in realms like finance is the evaluation function well known? For say a hedge fund

PM it might be established. But contrast the general task of a teacher with that of a businessman

or businesswoman. In academics it‘s superficially easy, that is, the teachers job is to teach you

and make sure you learn (as a student your job is to learn); and in business it‘s also superficially

easy, that is, you are suppose to maximize profit, and maximize excess returns in finance (i.e.,

controlling for risk). Now contrast that with the military with academics or business. The

military is potentially the most exacting environment (e.g., front line soldiering). But even in

cases where the penalty and/or reward are well known it may not be so clear how to get there. In

some sense, that is the dificulty of the job itself. Alas, typically in finance the penalty function is

neither symmetric nor the task singular.

I feel it is important to again mention overconfidence as a legitimate issue for not only finance,

but almost any realm where brutal and constant feedback may be lacking. Barberis and Thaler

(2002, p. 12 footnote #10) suggest that it may be due to at least two other biases: self-attribution

bias (where people see their own talents as being responsible for something positive, and

blaming bad luck for negative outcomes), and hindsight bias (after an event has occurred to take

credit for predicting it; people tend to claim they predicted the past better than they did).395 It

395 Even after being confronted with the degree of bias, most people largely refuse to correct their behavior. Thisincludes cases where there is a direct link to compensation (e.g., Biais and Weber (2009)).




isn‘t just that we are subject to these biases, it‘s that without a strong and constant feedback loop

there isn‘t much hope of overcoming them.

PSYCHOLOGICAL ISSUES AND CANCELLATION

―The findings described in this subsection are generally consistent with limited attention and

memory capacity. They also illustrate that cognitive errors by individuals need not cancel out at

the level of market equilibrium, because people are prone to similar errors. The form of investor

error in each of these cases is specific, but such examples are extremely revealing. The fact that

blatant investor misperceptions demonstrably occur and cause price overreaction suggests that

less blatant errors frequently occur, but are simply harder to document beyond a reasonable

doubt.‖

Daniel et al. (2002, p. 169)

Many economists believe that economic agents making cognitive errors will cancel each other

out or people will learn. Up to this point the evidence would not support this (although people




can unlearn under certain conditions, just not those that generally exist in most financial market

contexts). Therefore, people can unlearn cognitive errors, but not in those bureaucracies that

generally exist at this time.

Although, undoubtedly there is some cancelation occurring. For example, ―portfolio pumping‖

and the ―January effect‖ will tend to cancel each other out. That is, portfolio pumping tends to

increase prices the last few days of the business calendar to largely be reversed during the first

few days of the new business year. Unfortunately for EMH/EMT proponents the overall effects

just don‘t seem to help much, and we still see a January effect and inefficient trading around year

end by many PMs.

CORRECTING COGNITIVE BIASES AND LEARNING – THE CASES OF

OVERCONFIDENCE AND HINDSIGHT BIAS

According to Fischhoff (1999) there are essentially two areas responsible for the inability to

correct cognitive bias (and three possible things that can go wrong):




1. The task itself (task) is unfair or misunderstood.

2. The individual judging the task (judge).

3. Combination of the two (judge and task, i.e., no one is to blame for not debiasing the

judgment task).

The empirical results point to #2, and some support for #3. Fischoff (1999) summarizes forty

studies on the financial markets and training/educating agents/actors in debiasing for

overconfidence and hindsight bias.

Modified from Fischhoff, B., ―Debiasing‖, in Kahneman, D., Slovic, P., and A. Tversky (editors), Judgment under uncertainty: Heuristics and biases, Cambridge University Press, New York, N.Y., 1999, p. 434.

Fischhoff's summary for debiasing overconfidence & hindsight biasStrategies Hindsight bias Overconfidence

1 Faulty tasks

Unfair tasks

Raise stakes 0 of 1 0 of 2

Clarify instructions/stimuli 0 of 1 0 of 5

Discourage second guessing 0 of 1 0 of 2

Use better response modes 0 of 1 1 of 10Ask fewer questions 0 of 4 0 of 1

Misunderstood tasks

Demonstrate alternative goal 0 of 5 0 of 1

Demonstrate semantic disagreement 0 of 4

Demonstrate impossibility of task 0 of 1

Demonstrate overlooked distinction 0 of 1

2 Faulty judges

Perfectible individualsWarn of problem 0 of 1

Describe problem 0 of 1 1 of 1

Provide personalized feedback 1 of 1

Train extensively 0 of 1 9 of 9Incorrigible individuals

Replace them

Recalibrate their responses 0 of 3

Plan on error

3 Mismatch between judges and tasks

Restructuring

Make knowledge explicit 0 of 1

Search for discrepant information 1 of 1 1 of 1Decompose problem 0 of 2

Consider alternative situationsOffer alternative formulations 0 of 2 0 of 1

Education

Rely on substantive experts 0 of 6 7 of 14Educate from childhood 0 of 2




Fischhoff (1999, p. 435, p. 436, respectively) on ‗debiasing‘, ‗hindsight bias‘, and

‗overconfidence‘:

―All in all, perhaps the best way to get subjects to work hard is by exercising the

experimentalists‘ standard techniques for increasing a task‘s intrinsic motivation and subjects‘

involvement in it.‖

―Confidence assessments have been extracted from a variety of people in a variety of ways,

almost always showing considerable insensitivity to the extent of their knowledge. Although the

door need not be closed on methodological manipulations, they have so far proven relatively

ineffective and their results difficult to generalize. What they have done is to show that

overconfidence is relatively resistant to many forms of tinkering (other than changes in difficulty

level).‖

In total forty studies were summarized across the three broad areas of responsibility for debiasing

failure. As stated, the k ey is to ―Train extensively‖ (nine out of nine studies support for debiasing

overconfidence), and maybe ―Search for discrepant information‖, but that technique only has one

study supporting it. Fischhoff (1999, p. 427) notes that discrepant is ―encouraging respondents to

search for discrepant evidence, rather than collecting details corroborating a preferred answer;‖

This is the opposite of ―corroborating a preferred answer‖. It reminds me of something much like

a Socratic method of deep critical thinking or the Western scientific method in general. Of

particular usefulness was that participants seemed to do better/learn when told to explicitly look

for fault with their answers (i.e., told to look for inconsistencies) (Fischhoff (1999, p. 438)).




In addition, brutal repeated calibration works (e.g., professional weather forecasters) (see

Fischhoff (1999, p. 439)). Finally, decreasing the difficulty of the question reduces

overconfidence (remember, Kruger and Dunning imply the reverse, i.e., the less you know about

something the more likely you are to underestimate your abilities, e.g., the Chinese language,

Slovenian proverbs, etc.).

Clearly there has been a great deal of work in the overconfidence area, but relatively little in the

‗hindsight bias‘ area. My personal favorite is ―Replace them‖, but it is untested (maybe becauseit is trivial, if not effective). Overall, the key seems to be ―Train extensively‖ (and maybe

―Search for discrepant information‖). In short, when in doubt try the Socratic method of deep

critical thinking or the Western scientific method in general.

As a reminder, EMT proponents have a much steeper hill to climb than debiasing investors of

overconfidence and hindsight bias in a simple experimental context (as was being done in most

of the studies Fischoff analyzed). Let‘s just look at overconfidence on a conceptual level:




The gist of the thing is that people tend to make decisions and then fool themselves with self-

deception and other forms of denial (unless there is a strong feedback mechanism). There are

many possible dynamic self-supporting processes.

Noise

Overconfidence for

agent X in the financial

markets

Biased self-attribution

(a dynamic supportingprocess)

Confirmatory bias


Hindsight bias


Basic rationalization


Cognitive dissonance


Fundamental economic

information

T h e c o

n s t a

n t s e a

r c h f o

r a r g u m

e n t s

t o s u p

p o r t t h

e d e

c i s i o n

t o b u y

/ h o l d / s e l l .

I n t e

r p r e t i n

g e

v i d

e n c e

i n f a v o r o f t h

e d e c i s i o

n .

I “ k n e w i t ! ”

( t h a t i t w o u l d h a p p e n )

I f I f a i l i t ‟ s

b a d l u c k ,

s u c c e

s s i s

a l l m

i n e .

T h e d e c i s i o n t o b u y / h o l d / s e l l i n c r e a s e s m

y b e l i e f i n t h a t d e c i s i o n .

Overconfidence and Self-Deception in the Financial Markets




In addition, as mentioned previously, confirmation bias itself is likely to play a role in this

dynamic. For example, Sabourian and Sibert (2009, p. 26) remark concerning ―people whose

rewards are determined by the perceived ability‖, seems to have hidden truth. Without an explicit

mechanism to counter perceptions, then those perceptions will tend to hold and be reinforced

through time (e.g., confirmation and related biases like hindsight bias).

The key is financial market feedback that reflects economic fundamentals only. Remember, in

the EMH/EMT it is not just that information needs to be incorporated into pricing, but

Noise

Fundamental economic information

Overconfidencefor agent X in the

financial markets

Biased self-attribution(a dynamic supporting

process)

Confirmatory bias(a dynamic supporting

process)

Hindsight bias(a dynamic supporting

process)

Basic rationalization(a dynamic supporting

process)

Cognitive dissonance(a dynamic supporting

process)

Overconfidence with Self-Deception in Check

Market feedback that

reflects fundamental

economic information

(a dynamic reality check

process)

P r o f i t s /

l o s s e s a

f f e c t e d

b y e c o n

o m i c

f u n d a m

e n t a l s.




noninformation should not be incorporated into pricing. We know that tremendous amounts of

noninformation get incorporated into pricing (e.g., volatility and saliency for country funds).396

Note that the dynamic supporting processes are cut and only fundamental economic information

drives the feedback. In the final analysis, all overconfidence disappears (or alternatively, is

constantly kept in check by real relevant economic information). But in reality the:

1) Exactingness,

2) incentives/motivation, and

3) feedback

are typically lacking. Think WSSs, earnings analysts, and investment bankers, let alone

individual investors (i.e., the investment ―professionals‖ vs. the ‗amateurs‘). That is essentially

the true bottom line, if the highest paid ‗professionals‘ display strong biases and don‘t learn from

them, and most significant financial market related biases are largely resistant to debiasing, what

chance do we have of learning our way out of inefficient markets we observe?

Finally, and as noted by others, what makes us think we will learn our way out when finance and

economics is still largely normatively based? After all, if the supposed true experts on the

financial markets, people that have devoted decades to learning about finance cannot learn

themselves, why should those with, for example, a dual burden with respect to pricing in the

financial markets be expected to figure it out? For example, it doesn‘t take a Ph.D. in finance to

realize there are taxes and they matter, yet most textbook models ignore them. Also, think of the

396 Furthermore, often critical information is avoided or totally ignored. For example mutual fund companies tend toneglect or completely ignore references to risk and expenses (see, e.g., Jones and Smythe (2003)). This is especiallytroubling since expenses predictably and directly impact returns and performance, and market risk tends to be muchmore stable than returns (which tend to receive the marketing effort).




practicing experts. When WSSs and earnings analysts are actually punished monetarily for

screwed up forecasts, then my own personal doubts will begin to recede. In my descriptive mind,

until the academic, and especially the practicing ‗experts‘, show signs of learning, count me as a

learning skeptic.

REFERENCES





Camerer, C., Loewenstein, G., and M. Weber, ―The Curse of Knowledge in Economic Settings:

An Experimental Analysis‖, Journal of Political Economy, Volume 97, Issue 5, October 1989,

1232-1254.




Camerer, C., and R. Hogarth, ―The Effects of Financial Incentives in Experiments: A Review

and Capital-Labor-Production Framework‖, California Institute of Technology, Social Science

Working Paper 1059, April 1999, 1-43.

De Bondt, W., ―Competing Theories of Financial Anomalies‖, Review of Financial Studies,

Special Issue: Conference on Market Frictions and Behavioral Finance, Volume 15, Number 2,

Special 2002, 607-613.

Fischhoff, B., ―Debiasing‖, pp. 422-444, in Kahneman, D., Slovic, P., and A. Tversky (editors),

Judgment under uncertainty: Heuristics and biases, Cambridge University Press, New York,

N.Y., 1999.

Greene, D., and M. Lepper, ―Effects of Extrinsic Rewards on Children‘s Subsequent Intrinsic

Interest‖, Child Development, Volume 45, Issue 4, December 1974, 1141-1145.

Haign, M., and J. List, ―Do Professional Traders Exhibit Myopic Loss Aversion? An

Experimental Analysis‖, Journal of Finance, Volume LX, Number 1, February 2005, 523-534.

Hogarth, R., Gibbs, B., McKenzie, C., and M. Marquis, ―Learning from Feedback: Exactingness

and Incentives‖, Journal of Experimental Psychology: Learning, Memory, and Cognition,

Volume 17, Issue 4, July 1991, 734-752.




Jones, M., and T. Smythe, ―The Information Content of Mutual Fund Advertising‖, Journal of

Consumer Affairs, Volume 37, Number 1, Summer 2003, 22-41.

Kruger, J., ―Lake Wobegon Be Gone! The ‗Below-Average Effect‘ and the Egocentric Nature of

Comparative Ability Judgments‖, Journal of Personality and Social Psychology, Volume 77,

Issue 2, August 1999, 221-232.

Kruger, J., and D. Dunning, ―Unskilled and Unaware of It: How Difficulties in Recognizing

One‘s Own Incompetence Lead to Inflated Self -Assessments‖, Journal of Personality and Social

Psychology, Volume 77, Number 6, December 1999, 1121-1134.

Linnainmaa, J., ―The Anatomy of Day Traders‖, Working Paper, March 2003, 1-31.

Sabourian, H., and A. Sibert, ―Banker Compensation and Confirmation Bias‖, Working Paper,

24-March-2009, 1-33.




Chapter 17: Conclusion

―Economics reflect human behavior. Human behavior is rarely completely informed and rational.

Economic models that presume completely informed and rational participants can only go so far

in describing what people do. … To the extent behavioral concepts are correct, it is foolish to

argue that an economic model that is inconsistent with those concepts is correct.‖

Ferguson (1989, p. 50)

What have we learned? Based on standard and even current finance and economics definitions, it

is clear that: (1) the markets are not efficient, (2) agents are not purely rational, (3) ‗free lunches‘

abound, yet may be hard to come by, and (4) the basic theoretical rational arbitrageur may not

exist. Behavioral finance contrasts with normative finance in that its descriptive theories and

related hypotheses are falsifiable. Specifically, its twin ‗pillars‘ of limits to arbitrage and

psychology seem to represent a superior method for approaching inquiry into the financial

markets. The key is the focus on the descriptive over the normative, and to directly apply western

scientific method. For example, we can assume that all investors are purely rational, yet that

doesn‘t make them purely rational, and it matters.

Furthermore, there is a great deal in economics that has been elevated to the level of quasi-

religious belief. Nowhere is this as true as in ‗modern finance‘, and especially what constitutes

‗market efficiency‘. This would be fine except for the claim that normative finance is a

‗scientific‘ field of inquiry, complete with mathematical rigor just like the ―hard sciences‖. This




is in contrast to most religions that tend be matters purely of belief and do not rest their existence

on falsifiable claims (e.g., ―A market in which prices always ‗fully reflect‘ available information

is called ‗efficient‘‖.). Also, this is in contrast to normative finance or ‗modern finance‘ where

basic hypotheses seem no longer upon to rest on falsification or even clear statements

reconcilable with reality.

Whether or not acknowledged, behavioral finance has driven the debate and much of the recent

descriptive and even normative modeling research in finance. De Bondt (2002, p. 608)

commented that: ―Modern finance has responded to the challenge in different ways. It either

reinterprets the new facts as nonanomalous (e.g., the abnormal profits compensate for time-

varying risk), it questions their pervasiveness and robustness [Fama (1998)] or it argues that

markets may yet be ‗minimally rational,‘ in the sense that markets fail to supply opportunities for

abnormal profits [Rubinstein (2001)].‖ None of these responses is likely to explain away the

descriptive reality of the financial markets.

For example, it is still theoretically or normatively possible to describe the universe from an

earth centric viewpoint (i.e., the other planets and the sun revolve around the Earth), but the

evidence is such that it looks likely that the Earth revolves around the sun. Similarly with finance

we can stick with the information centric view of finance where prices are always and in every

market at all times ‗efficient‘. The answer for me is which is more plausible, and why. My

answer is behavioral centric with its twin emphasis on limits to arbitrage and psychology.




The irony in all of this may be that, as De Bondt (2002, p. 607) noted, that the EMH was a

response to the perceived descriptive reality of the financial markets. He quoted Fama (1970)

that ‘there existed a large body of empirical results in search of a rigorous theory.‘ The EMH was

the result. As I mentioned, unfortunately there was counter evidence and other more plausible

reasons for market pricing than the information centric theory of market efficiency. Now some

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