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The Inefficient Market
Prentice Hall 1999
Visit our web-site at HaugenSystems.com
What Pays Off and Why
Part 1: What Pays Off
Abridged
The Evolution of Academic FinanceThe Evolution of Academic Finance
1930’s 40’s 50’s 60’s 70’s 80’s 90’s beyond
The Old FinanceThe Old Finance
Theme: Analysis of Financial Statements and the Nature of Financial Claims
Paradigms:Security Analysis Uses and Rights of Financial Claims
(Graham & Dodd) (Dewing)
Foundation: Accounting and Law
The Old Finance
Old Finance
Best investment strategy = – Stock-picking / value-investing approach, such
as Warren Buffett uses
1930’s 40’s 50’s 60’s 70’s 80’s 90’s beyond
The Old Finance
Modern Finance
Bob goes to college
Modern FinanceModern Finance
Theme: Valuation Based on Rational Economic Behavior
Paradigms: Optimization Irrelevance CAPM EMH
(Markowitz) (Modigliani & Miller) (Sharpe, Lintner & Mossen) (Fama)
Foundation: Financial Economics
The Evolution of Academic FinanceThe Evolution of Academic Finance
Modern Finance
Optimal investment strategy = – Invest in index funds, try to match market as
closely as possible at as low a cost as possible
1930’s 40’s 50’s 60’s 70’s 80’s 90’s beyond
The Old Finance
Modern Finance
The New Finance Bob goes to college
The New FinanceThe New Finance
Theme: Inefficient Markets
Paradigms: Inductive ad hoc Factor Models Behavioral Models
Expected Return Risk
(Haugen) (Chen, Roll & Ross) (Kahneman &
Tversky)
Foundation: Statistics, Econometrics, and Psychology
The Evolution of Academic FinanceThe Evolution of Academic Finance
New Finance
Market is inefficient, but hard to beat nonetheless
Optimal investment approach = – Use Markowitz optimization to create optimal
portfolios• APT Risk-factor model to model risk• Ad hoc inductive expected return factor model to model
expected returns
– Quantitative hedge fund, such as• Enhanced index fund• Long / short fund
Hedge Fund Risk/Return Profile
Hedge Funds en totalConvertible Arb
Short biased
Market Neutral
Event Driven
Distressed
Event Driven Multi
Risk Arb
Fixed Income Arb
Global Macro
Long/Short Equity
Managed Futures
Dow Jones
Lehman Aggregate
MSCI EAFE
Nasdaq
Russell 2000
S&P 500
T-Bill
-2.00%
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
18.00%
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00%
Standard Deviation
An
nu
alized
Retu
rn
Ten Years Ending 2/03
Rest of BookRest of Book
Part I: Describes one approach to developing a Part I: Describes one approach to developing a quantitative hedge fundquantitative hedge fund– Focus of this classFocus of this class
Part II: Discusses why that approach worksPart II: Discusses why that approach works– Chapters 9 – 12 won’t be covered in class, but can Chapters 9 – 12 won’t be covered in class, but can
read for own pleasureread for own pleasure
Probability Distribution For Returns to a PortfolioProbability Distribution For Returns to a Portfolio
Possible Rates of Returns
Probability
Expected Return
Variance of Return
Risk Factor ModelsRisk Factor Models
The variance of stock returns can be split into two components: Variance = systematic risk + diversifiable risk
Systematic risk is modeled using an APT-type risk-factor model
Measures extent to which stocks’ returns [jointly] move up and down over time
Estimated using time-series data
Diversifiable risk is reduced through optimal diversification
Expected Return Factor Expected Return Factor ModelsModels
Expected return factor models measure / predict the extent to which the stocks’ returns are different from each other within a given period of time.
Expected Return Factor Expected Return Factor ModelsModels
The factors in an expected return model represent the character of the companies.
They might include the history of their stock prices, its size, financial condition, cheapness or dearness of prices in the market, etc.– Unlike CAPM and APT, not only risk factors such as
market beta or APT betas are included
Factor payoffs are estimated by relating individual stock returns to individual stock characteristics over the cross-sectioncross-section of a stock population (here the largest 3000 U.S. stocks).
Five Factor FamiliesFive Factor Families
Risk – Market and APT betas, TIE, debt ratio, etc.,
values and trends thereof Liquidity
– Market cap., price, trading volume, etc. Price level
– E/P, B/P, Sales/P, CF/P, Div/P Profitability
– Profit margin, ROE, ROA, earnings surprise, etc. Price history (technical factors)
– Excess return over past 1, 2, 3, 6, 12, 24, & 60 months
The Most Important FactorsThe Most Important Factors
The monthly slopes (payoffs) are averages over the period 1979 through mid 1986.
“T” statistics on the averages are computed, and the stocks are ranked by the absolute values of the “Ts”.
Most Important FactorsMost Important Factors
1979/01 through1979/01 through1986/061986/06
1986/07 through 1993/121986/07 through 1993/12
FactorFactor MeanMean ConfidenceConfidence MeanMean ConfidenceConfidence
One-month excess returnOne-month excess return -0.97%-0.97% 99%99% -0.72%-0.72% 99%99%
returnreturnTwelve-month excessTwelve-month excess 0.52%0.52% 99%99% 0.52%0.52% 99%99%
Trading volume/marketTrading volume/marketcapcap
-0.35%-0.35% 99%99% -0.20%-0.20% 98%98%
Two-month excess returnTwo-month excess return -0.20%-0.20% 99%99% -0.11%-0.11% 99%99%
Earnings to priceEarnings to price 0.27%0.27% 99%99% 0.26%0.26% 99%99%
Return on equityReturn on equity 0.24%0.24% 99%99% 0.13%0.13% 97%97%
Book to priceBook to price 0.35%0.35% 99%99% 0.39%0.39% 99%99%
Trading volume trendTrading volume trend -0.10%-0.10% 99%99% -0.09%-0.09% 99%99%
Six-month excess returnSix-month excess return 0.24%0.24% 99%99% 0.19%0.19% 99%99%
Cash flow to priceCash flow to price 0.13%0.13% 99%99% 0.26%0.26% 99%99%
The Most Important FactorsThe Most Important Factors
Among the factors that are significant (i.e., that can be used to distinguish between which companies will have higher returns and which will have lower returns) are:
– A number of liquidity factors
– Various fundamental factors, indicating value with growth
– Technical factors, indicating short-term reversals and intermediate term momentum
• Suggest that technical factors provide marginal value when used in conjunction with fundamental analysis
– Notably, no CAPM or APT risk factors are included!
Projecting Expected Projecting Expected ReturnReturn
The components of expected return are obtained by multiplying the projected payoff to each factor (here the average of the past 12) by the stock’s current exposure to the factor.
Exposures are measured in standard deviations from the cross-sectional mean.
The individual components are then summed to obtain the aggregate expected return for the next period (here a month).
Factor Exposure Payoff ComponentBook\Price 1.5 S.D. x 20 B.P. = 30 B.P.
Short-Term Reversal 1.0 S.D. x -10 B.P. = -10 B.P.. . . .. . . .. . . .. . . .. . . .. . . .
Estimating Expected Stock Estimating Expected Stock ReturnsReturns
Trading Volume -2 S.D. x -20 B.P. = 40 B.P.Total Excess ReturnTotal Excess Return 80 B.P.80 B.P.
The Model’s Out-of-sample The Model’s Out-of-sample Predictive PowerPredictive Power
The 3000 stocks are ranked by expected return and formed into deciles (decile 10 highest).
The performance of the deciles is observed in the next month.
The expected returns are re-estimated, and the deciles are re-ranked.
The process continues through 1993.
Logarithm of Cumulative Decile PerformanceLogarithm of Cumulative Decile Performance
1
2
3
45
678
9
10
-1
-0.5
0
0.5
1
1.5
2
2.5
80Q1 81Q1 82Q1 83Q1 84Q1 85Q1 86Q1 87Q1 88Q1 89Q1 90Q1 91Q1 92Q1 93Q1 94Q1 95Q1 96Q1 97Q1 98Q1
Date
33 44 55 66 77 88 99 1010DecileDecile
-40%-40%
-30%-30%
-20%-20%
-10%-10%
0%0%
10%10%
20%20%
30%30%
00 11 22
Realized ReturnRealized Return
Realized Return for 1984 by DecileRealized Return for 1984 by Decile
(Y/X = 5.5%)(Y/X = 5.5%)
YY
XX
Extension of Study to Other Extension of Study to Other PeriodsPeriods
Nardin BakerNardin Baker The same family of factors is used on a
similar stock population.
Years before and after initial study period are examined to determine slopes and spreads between decile 1 and 10.
19971997
0%0%
10%10%
20%20%
30%30%
40%40%
50%50%
60%60%
70%70%
80%80%
90%90%
100%100%
19751975 19771977 19791979 19811981 19831983 19851985 19871987 19891989 19911991 19931993 19951995
YearsYears
19981998
differencedifference
slopeslope
Slope and SpreadSlope and Spread
Decile Risk CharacteristicsDecile Risk Characteristics
The characteristics reflect the character of the deciles over the period 1979-1993.
Fama-FrenchFama-FrenchThree- Factor ModelThree- Factor Model
Monthly decile returns are regressed on monthly differences in the returns to the following:– S&P 500 and T bills
– The 30% of stocks that are smallest and largest
– The 30% of stocks with highest book-to-price and the lowest.
Sensitivities (Betas) to Market ReturnsSensitivities (Betas) to Market Returns
1010
DecileDecile
11 22 33 44 55 66 77 88 99
0.950.95
11
1.051.05
1.11.1
1.151.15
1.21.2
1.251.25
Market BetaMarket Beta
Sensitivities (Betas) to Relative Performance of Small and Large StocksSensitivities (Betas) to Relative Performance of Small and Large Stocks
22 33 44 55 66 77 88 99 1010DecileDecile00
0.10.1
0.20.2
0.30.3
0.40.4
0.50.5
11
Size BetaSize Beta
Sensitivities (Betas) to RelativeSensitivities (Betas) to Relative Performance of Value and Growth StocksPerformance of Value and Growth Stocks
DecileDecile88 99 1010
11 22 33 44 55 66 77
-0.2-0.2
-0.1-0.1
00
0.10.1
0.20.2
0.30.3
Value/GrowthValue/Growth BetaBeta
Fundamental Fundamental CharacteristicsCharacteristics
Averaged over all stocks Averaged over all stocks in each decile and over all in each decile and over all
months (1979-83).months (1979-83).
Decile Risk CharacteristicsDecile Risk Characteristics
Debt to Equity
1.03 0.85
StockVolatility
1 2 3 4 5 6 7 8 9 10
Decile
0%0
1
2
3
4
5
6
7
8
Interest CoverageMarket Beta
Debt to Equity
Volatility
41.42%
33.22%
10%
20%
30%
40%
50%
Coverage
1.76
6.63
Beta 1.001.21
Size and Liquidity CharacteristicsSize and Liquidity Characteristics
$0$0
$10$10
$20$20
$30$30
$40$40
$50$50
$60$60
$70$70
11 22 33 44 55 66 77 88 99 1010
DecileDecile
Stock PriceStock Price
Trading VolumeTrading Volume
$400$400
$500$500
$600$600
$700$700
$800$800
$900$900
$1,000$1,000
$1,100$1,100
SizeSize
$14.93$14.93
$30.21$30.21
PricePrice
$470$470
$1011$1011
SizeSize
$42.42$42.42
$60.89$60.89
Trading VolumeTrading Volume
Technical HistoryTechnical History
11 22 33 44 55 66 77 88 99 1010
DecileDecile
-20%-20%
-10%-10%
0%0%
10%10%
20%20%
30%30%
Excess ReturnExcess Return
2 months2 months
-1.80%-1.80%
1.21%1.21%
12 months12 months
-15.74%-15.74%
30.01%30.01%
3 months3 months
-6.89%-6.89%
8.83%8.83%
6 months6 months
-12.14%-12.14%
16.60%16.60%
1 month1 month
0.09%0.09%
-0.14%-0.14%
Current ProfitabilityCurrent Profitability
Asset TurnoverAsset Turnover115%115%
Return on EquityReturn on Equity15.39%15.39%
Profit MarginProfit Margin 7.86%7.86%
Return on AssetsReturn on Assets 6.50%6.50%
90%90%
100%100%
110%110%
120%120%
Asset TurnoverAsset Turnover
22 33 44 55 66 77 88 99 1010
DecileDecile
80%80%-10%-10%
0%0%
10%10%
20%20%
11
Profit Margin Profit Margin Return on Assets Return on Assets Return on Equity Return on Equity Earnings GrowthEarnings Growth
Earnings GrowthEarnings Growth 0.95%0.95%
1 2 3 4 5 6 7 8 9 10
DecileDecile
5 Year 5 Year Trailing GrowthTrailing Growth
-1.5%
-1.0%
-0.5%
0.0%
Profitability TrendsProfitability Trends(Growth In)
Asset Turnover -0.13%
Profit Margin
-0.95% Return on Assets
-1.11% Return on Equity
-1.18%
Price Level Price Level
Sales-to-PriceSales-to-Price214%214%
207%207%
Cash Flow-to-Price
6%
17%
Earnings-to-Price
-1.55%
10%
Dividend-to-Price2.19%
3.69%
50%
100%
150%
200%
Sales-to-Price Book-to-Price
3 4 5 6 7 8 9 10
Decile
0%-10%
0%
10%
20%
1 2
Cash Flow-to-Price Earnings-to-Price Dividend-to-Price
Book-to-Price81%
80%
Simulation of Simulation of Investment PerformanceInvestment Performance
Efficient portfolios are constructed Efficient portfolios are constructed quarterly, assuming 2% round-trip quarterly, assuming 2% round-trip transactions costs within the Russell 1000 transactions costs within the Russell 1000 population.population.– Turnover controlled to 20% to 40% per annum.Turnover controlled to 20% to 40% per annum.– Maximum stock weight is 5%.Maximum stock weight is 5%.– No more that 3X S&P 500 cap weight in any stock.No more that 3X S&P 500 cap weight in any stock.– Industry weight to within 3% of S&P 500.Industry weight to within 3% of S&P 500.– Turnover controlled to within 20% to 40%.Turnover controlled to within 20% to 40%.
10%10%
12%12%
14%14%
18%18%
16%16%
20%20%
12%12%
An
nu
aliz
ed
tota
l re
turn
An
nu
aliz
ed
tota
l re
turn
17%17% 18%18%13%13% 14%14% 15%15% 16%16%
Annualized volatility of returnAnnualized volatility of return
1000 1000 IndexIndex
GGII
HH
LL
Optimized Portfolios in the Russell 1000 Optimized Portfolios in the Russell 1000 PopulationPopulation 1979-19931979-1993
PPossible Sources of ossible Sources of BiasBias
Survival bias:Survival bias:– Excluding firms that go inactive during test period.Excluding firms that go inactive during test period.
Look-ahead bias:Look-ahead bias:– Using data that was unavailable when you trade.Using data that was unavailable when you trade.
Bid-asked bounce:Bid-asked bounce:– If this month’s close is a bid, there is 1 chance in 4 that next If this month’s close is a bid, there is 1 chance in 4 that next
and last month’s close will be at an asked, showing and last month’s close will be at an asked, showing reversals.reversals.
Data snooping:Data snooping:– Using the results of prior studies as a guide and then Using the results of prior studies as a guide and then
testing with their data.testing with their data. Data mining:Data mining:
– Spinning the computer.Spinning the computer.
Using the Ad Hoc Expected Using the Ad Hoc Expected Return Factor Model Return Factor Model
InternationallyInternationally The most important factors across the 5 The most important factors across the 5
largest stock markets (1985-93).largest stock markets (1985-93). Simulating investment performance:Simulating investment performance:
– Within countries, constraints are those stated Within countries, constraints are those stated previously.previously.
– Positions in countries are in accord with Positions in countries are in accord with relative total market capitalization.relative total market capitalization.
Mean Payoffs and Confidence Probabilities for theMean Payoffs and Confidence Probabilities for theTwelve Most Important Factors of the World (1985-93)Twelve Most Important Factors of the World (1985-93)
One-month stock return
Book to price
Twelve-month stock return
Cash flow to price
Earnings to price
Sales to price
Three-month stock return
Debt to equity
Variance of total return
Residual variance
Five-year stock return
Return on equity
United StatesUnited States
Mean Confidence Level(DifferentFrom Zero)
-0.32% 99%
0.14% 99%
0.23% 99%
0.18% 99%
0.16% 99%
0.08% 99%
-0.01% 38%
-0.06% 96%
-0.06% 94%
-0.08% 99%
-0.01% 31%
0.11% 99%
GermanyGermany
Mean Confidence Level(DifferentFrom Zero)
-0.26% 99%
0.16% 99%
0.08% 99%
0.08% 99%
0.04% 83%
0.10% 99%
-0.14% 99%
-0.06% 96%
-0.04% 83%
-0.04% 80%
-0.02% 51%
0.01% 31%
FranceFrance
Mean Confidence Level(DifferentFrom Zero)
-0.33% 99%
0.18% 99%
0.12% 99%
0.15% 99%
0.13% 99%
0.05% 99%
-0.08% 99%
-0.09% 99%
-0.12% 99%
-0.09% 99%
-0.06% 94%
0.10% 99%
United United KingdomKingdom
Mean Confidence Level(DifferentFrom Zero)
-0.22% 99%
0.12% 99%
0.21% 99%
0.09% 99%
0.08% 99%
0.05% 91%
-0.08% 99%
-0.10% 99%
-0.01% 38%
-0.03% 77%
-0.06% 96%
0.04% 80%
JapanJapan
Mean Confidence Level(Different
From Zero)-0.39% 99%
0.12% 99%
0.04% 86%
0.05% 91%
0.05% 94%
0.13% 99%
-0.26% 99%
-0.01% 31%
-0.11% 99%
0.00% 8%
-0.07% 98%
0.05% 92%
Optimization in France, Germany, U. K., Japan and Optimization in France, Germany, U. K., Japan and across the five largest countries. 1985-1994across the five largest countries. 1985-1994
19.0%19.0%
17.0%17.0%
15.0%15.0%
13.0%13.0%
11.0%11.0%
9.0%9.0%
7.0%7.0%
5.0%5.0% 10% 12% 14% 16% 18% 20% 22% 24%
G
I
HFranceFrance
FranceFranceindexindex
U. K.U. K. H
I
G U. K.U. K.indexindex
GermanyGermany
GermanyGermanyindexindex
H
I
G
JapanJapan
H
I
GG
JapanJapanindexindex
five largest five largest countries countries
(including U.S.)(including U.S.)
H
I
G
index ofindex offive largestfive largestcountriescountries
Annualized Annualized total total
returnreturn
Annualized volatility of returnAnnualized volatility of return
Performance In Different CountriesPerformance In Different Countries 1985 - 1998 1985 - 1998 (September)(September)
0%0%
5%5%
10%10%
15%15%
20%20%
25%25%
30%30%
12%12% 14%14% 16%16% 18%18% 20%20% 22%22% 24%24% 26%26% 28%28% 30%30% 32%32%
VolatilityVolatility
ReturnReturn
AUS BEL CAN CHE DEU ESP FRAGBR HKG ITA JPN NLD SWE USA
Performance before fees, after transactions costs and includes reinvested dividendsPerformance before fees, after transactions costs and includes reinvested dividendsIndustrifinans Contact: Ole Jakob Wold +47.22.473300Industrifinans Contact: Ole Jakob Wold +47.22.473300 Measured in Norwegian Krone (NOK), Managed to stay neutral in country and sector weightsMeasured in Norwegian Krone (NOK), Managed to stay neutral in country and sector weightsPast performance is not a guarantee of future resultsPast performance is not a guarantee of future results Managed using modified (Haugen-Baker) JFE Expected Return Model by Baker at Grantham Mayo Van Otterloo, Inc.Managed using modified (Haugen-Baker) JFE Expected Return Model by Baker at Grantham Mayo Van Otterloo, Inc.
Industrifinans ForvaltningIndustrifinans ForvaltningGlobal FundGlobal Fund
170.65%170.65%
144.04%144.04%
-20%-20%
0%0%
20%20%
40%40%
60%60%
80%80%
100%100%
120%120%
140%140%
160%160%
180%180%
jan.95jan.95aprapr juljul octoct jan.96jan.96aprapr juljul octoct jan.97jan.97aprapr juljul octoct jan.98jan.98aprapr juljul octoct jan.99jan.99aprapr
Cumulative return since inception (31 October 1994Cumulative return since inception (31 October 1994))
Industrifinans WorldIndustrifinans World
Morgan Stanley World NOKMorgan Stanley World NOK
Industrifinans ForvaltningIndustrifinans Forvaltning
Probability that the expected return to the Global Fund Probability that the expected return to the Global Fund has been higher than the Morgan Stanley World Indexhas been higher than the Morgan Stanley World Index
92.2%92.2%
0%0%
10%10%
20%20%
30%30%
40%40%
50%50%
60%60%
70%70%
80%80%
90%90%
100%100%
Performance measured before fees, after transactions costs and includes reinvested dividendsPerformance measured before fees, after transactions costs and includes reinvested dividendsIndustrifinans Contact: Ole Jakob Wold +47.22.473300Industrifinans Contact: Ole Jakob Wold +47.22.473300 Measured in Norwegian Krone (NOK), Managed to stay neutral in country and sector weightsMeasured in Norwegian Krone (NOK), Managed to stay neutral in country and sector weightsPast performance is not a guarantee of future resultsPast performance is not a guarantee of future resultsManaged using modified (Haugen-Baker) JFE Expected Return Model by Baker at Grantham Mayo Van Otterloo, Inc.Managed using modified (Haugen-Baker) JFE Expected Return Model by Baker at Grantham Mayo Van Otterloo, Inc.
dec.94dec.94marmar junjun sepsep dec.95dec.95marmar junjun sepsep dec.96dec.96marmar junjun sepsep dec.97dec.97marmar junjun sepsep dec.98dec.98marmar
Probability of out-performing the Morgan Stanley World Index since inception (31 October 1994)Probability of out-performing the Morgan Stanley World Index since inception (31 October 1994)
130.31%130.31%
Analytic InvestorsAnalytic InvestorsEnhanced Equity Institutional CompositeEnhanced Equity Institutional Composite
102.73%102.73%
0%0%
20%20%
40%40%
60%60%
80%80%
100%100%
120%120%
140%140%
AI Contact: Dennis Bein 213.688.3015AI Contact: Dennis Bein 213.688.3015 Performance before fees, after transactions costs and includes reinvested dividendsPerformance before fees, after transactions costs and includes reinvested dividendsPast performance is not a guarantee of future resultsPast performance is not a guarantee of future results Managed using Haugen expected return model & Barra optimizer & risk modelManaged using Haugen expected return model & Barra optimizer & risk model
nov.96nov.96jan.97jan.97 marmar maymay juljul sepsep novnov jan.98jan.98 marmar maymay juljul sepsep novnov jan.99jan.99 marmar
Cumulative return since inception (30 Sep 1996)Cumulative return since inception (30 Sep 1996)
Institutional CompositeInstitutional Composite
S&P 500S&P 500
Analytic InvestorsAnalytic Investors
Probability that the expected return to the Enhanced Equity Probability that the expected return to the Enhanced Equity Institutional Composite has been higher than the S&P 500 IndexInstitutional Composite has been higher than the S&P 500 Index
93.3%93.3%
0%0%
10%10%
20%20%
30%30%
40%40%
50%50%
60%60%
70%70%
80%80%
90%90%
100%100%
AI Contact: Dennis Bein 213.688.3015AI Contact: Dennis Bein 213.688.3015 Performance before fees, after transactions costs and includes reinvested dividendsPerformance before fees, after transactions costs and includes reinvested dividendsPast performance is not a guarantee of future resultsPast performance is not a guarantee of future results Managed using Haugen expected return model & Barra optimizer & risk modelManaged using Haugen expected return model & Barra optimizer & risk model
nov.96nov.96 feb.97feb.97 maymay augaug novnov feb.98feb.98 maymay augaug novnov feb.99feb.99
Probability of out-performing the S&P 500 Index since inception (30 Sep 1996)Probability of out-performing the S&P 500 Index since inception (30 Sep 1996)
Performance of 413 Mutual Performance of 413 Mutual Funds 10/96 - 9/98Funds 10/96 - 9/98
““T” stat. on mean monthly out-performance T” stat. on mean monthly out-performance to S&P 500.to S&P 500.
Large funds with highest correlation with Large funds with highest correlation with S&P with a 36 month history.S&P with a 36 month history.
Three YearThree Year OutOut--(Under)(Under)-Performance T-Distribution-Performance T-Distribution
0%0%
5%5%
10%10%
15%15%
20%20%
25%25%
to -5.0 -5.0 to-4.5
-4.5 to-4.0
-4.0 to-3.5
-3.5 to-3.0
-3.0 to-2.5
-2.5 to-2.0
-2.0 to-1.5
-1.5 to-1.0
-1.0 to-0.5
-0.5 to0.0
0.0 to0.5
0.5 to1.0
1.0 to1.5
1.5 to2.0
2.0 to
T-statistics for mean T-statistics for mean outout--(under)(under) performance performance
Per
cen
t o
f sa
mp
leP
erce
nt
of
sam
ple
Can read Chapters Can read Chapters 9 through 12 at 9 through 12 at
your own leisure.your own leisure.
A Test of RelativeA Test of Relative Predictive Power Predictive Power
1980 -19971980 -1997
Model employing factors Model employing factors exploiting the market’s tendencies exploiting the market’s tendencies
to over- and under-reactto over- and under-react
vs.vs.
Models employing risk factors only Models employing risk factors only (“deductive” models of modern (“deductive” models of modern
finance).finance).
The The Ad HocAd Hoc Expected Expected Return Factor ModelReturn Factor Model
RiskRisk LiquidityLiquidity ProfitabilityProfitability Price levelPrice level Price historyPrice history Earnings revision and surpriseEarnings revision and surprise
Decile Returns for the Ad Hoc Factor Model Decile Returns for the Ad Hoc Factor Model (1980 through mid 1997)(1980 through mid 1997)
2 3 4 5 6 7 8 9 10Decile0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
1
AverageAverage Annualized Annualized
ReturnReturn
The Capital Asset The Capital Asset Pricing ModelPricing Model
Market beta measured over the trailing Market beta measured over the trailing 3 to 5-year 3 to 5-year periods).periods).
Stocks ranked by beta and formed into Stocks ranked by beta and formed into deciles monthly.deciles monthly.
Decile Returns for CAPM ModelDecile Returns for CAPM Model
33 44 55 66 77 88 99 1010 DecileDecile0%0%
5%5%
10%10%
15%15%
20%20%
25%25%
30%30%
35%35%
40%40%
45%45%
11 22
Average Average Annualized Annualized
ReturnReturn
The Arbitrage Pricing The Arbitrage Pricing TheoryTheory
Macroeconomic FactorsMacroeconomic Factors– Monthly T-bill returnsMonthly T-bill returns
– Long-term T-bond returns less short-termLong-term T-bond returns less short-term
– T-bond returns less low-gradeT-bond returns less low-grade
– Monthly inflationMonthly inflation
– Monthly change in industrial productionMonthly change in industrial production Beta EstimationBeta Estimation
– Betas re-estimated monthly by regressing stock returns Betas re-estimated monthly by regressing stock returns on economic factors over trailing 3-5 yearson economic factors over trailing 3-5 years
Payoff ProjectionPayoff Projection– Next month’s payoff is average of trailing 12 monthsNext month’s payoff is average of trailing 12 months
Average Returns for APT ModelAverage Returns for APT Model
Annualized Annualized
22 33 44 55 66 77 88 99 1010 DecileDecile0%0%
5%5%
10%10%
15%15%
20%20%
25%25%
30%30%
35%35%
40%40%
45%45%
11
Average Average
ReturnReturn
Overall ResultsOverall Results Ad Hoc Expected Return Factor Model
– Average Annualized Spread Between Deciles 1 & 10 46.04%46.04%– Years with Negative Spreads 0 years
Models Based on MODERN FINANCE– CAPM
• Average Annualized Spread Between Deciles 1 & 10 -6.94%-6.94%• Years with Negative Spreads 13 years
– APT• Average Annualized Spread Between Deciles 1 & 10 6.06%• Years with Negative Spreads 6 years
The Position of Portfolios in Abnormal Profit SpaceThe Position of Portfolios in Abnormal Profit Space
Effici
ent M
arke
t
Effici
ent M
arke
t
Line
Line
TrueTrue Abnormal Profit Abnormal Profit
Super StocksSuper Stocks
Stupid StocksStupid Stocks
PricedPriced Abnormal ProfitAbnormal Profit
The Position of Portfolios in Abnormal Profit SpaceThe Position of Portfolios in Abnormal Profit Space
Effici
ent M
arke
t
Effici
ent M
arke
t
Line
Line
TrueTrue Abnormal Profit Abnormal Profit
Investment Investment HeavenHeaven
Stupid StocksStupid Stocks
PricedPriced Abnormal Profit Abnormal Profit
The Position of Portfolios in Abnormal Profit SpaceThe Position of Portfolios in Abnormal Profit Space
Effici
ent M
arke
t
Effici
ent M
arke
t
Line
Line
TrueTrue Abnormal Profit Abnormal Profit
Investment Investment HeavenHeaven
InvestmentInvestmentHellHell
PricedPriced Abnormal Profit Abnormal Profit
The Position of Portfolios in Abnormal Profit SpaceThe Position of Portfolios in Abnormal Profit Space
Effici
ent M
arke
t
Effici
ent M
arke
t
Line
Line
TrueTrue Abnormal Profit Abnormal Profit
Investment Investment HeavenHeaven
InvestmentInvestmentHellHell
PricedPriced Abnormal ProfitAbnormal Profit
Can’t get to heaven by going around
the corner
You must go directly to heaven
How do you get to How do you get to Investment Heaven?Investment Heaven?
Three main steps:Three main steps:– Use risk factor models to estimate variances and Use risk factor models to estimate variances and
covariancescovariances– Use ad hoc expected return factor models to Use ad hoc expected return factor models to
determine desired stock characteristics and determine desired stock characteristics and estimate expected returnsestimate expected returns
• Cannot just screen sequentially (“going around the Cannot just screen sequentially (“going around the corner”) for stocks with the desired characteristicscorner”) for stocks with the desired characteristics
– Combine this information into optimal portfolios Combine this information into optimal portfolios through Markowitz optimizationthrough Markowitz optimization