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The Effects of Firm Maturity: IPO and Post-IPO Performance, Growth, Efficiency, Profitability and Returns;

& The Rational Part of Momentum

Jorge Alberto Murillo Garza

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy

under the Executive Committee of the Graduate School of Arts and Sciences

COLUMBIA UNIVERSITY

2008

UMI Number: 3305253

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ABSTRACT

The Effects of Firm Maturity: IPO and Post-IPO Performance,

Growth, Efficiency, Profitability and Returns;

& The Rational Part of Momentum

Jorge Alberto Murillo Garza

This thesis analyses the relation of firm maturity (age) and firm's performance at

their IPO and Post DPO returns and fundamentals. The first chapter analyses post-issuance

performance utilizing a sample of 9,400 IPOs spread from 1935 to 2002 and shows that

young firms (under 9 years old) are the most likely to underperform and be delisted; three

and five year cumulative abnormal returns range between -20% and -75% for this age

group. Between 8% and 18% of young firms delist before reaching their third IPO

anniversary, in contrast only 2% of old and mature firms delist. The increasing number of

young firms listed during the 80's and 90's, both on the Nasdaq and NYSE, accounts for

the strong underperformance during that period. Given the small supply of young-small

IPOs it is very plausible that investors seeking "the next Big Thing" overprice these

"lottery like" securities and underestimate their failure probabilities, resulting in overall

underperformance from this group.

The second chapter establishes an important link between industrial economics

and finance by exploring the effects of a firm's age on realized returns and firm

fundamentals. Over four decades, Mature firms generated an excess return of 20 to 30

basis points after controlling for industry, size and book-to-market characteristics. A

simple zero cost strategy that takes long positions in mature firms and short positions in

young and old firms yields annualized returns of 5.38% and 5.60% or between 7.33% and

3.71% in excess to size and value portfolios. We decompose age, listing cohort, and year

effects to analyze key firm fundamental characteristics related to growth, innovation,

efficiency, liquidity, default risk, and profitability. Maturity decreases firms' default

probability, earnings uncertainty, market illiquidity, and shortens the investment horizon.

While innovation and growth opportunities decrease with time, profitability, dividend

yield, and process efficiency increase with firm age. Firms in their mature stage enjoy

growth, profitability, lower risk and offer higher returns.

Finally, the third chapter presents arguments supporting rationality in the well

known momentum effect. We find that the returns of different momentum deciles closely

track a measure the rate of change in fundamental value calculated from analysts'

earnings estimates. We also find that while past changes in fundamental value predict

future stock returns, stock rates of return appear to predict subsequent changes in

fundamental value, up to a year in the future. The ability of past returns to predict both

future returns and future changes in fundamental value is consistent with heterogeneous

expectations models of capital market equilibrium, where the expectations of informed

investors create the apparent predictive ability of past returns. Since heterogeneous

expectations models are consistent with rational behavior, if there is a significant

irrational component to momentum, it is likely to deal with biases in the way analysts and

investors form estimates of earnings and fundamental value.

Table of Contents

Chapter 1 1

1.1 Introduction 1

1.2 Data 7

1.3 Methodology 13

1.4 Results 15

1.4. A Event-Time Returns 15

1.4.B Calendar-Time Returns 22

1.4.C Regression Results 26

1.5 Conclusions 27

1.6 References 53

Chapter 2 56

2.1 Introduction 56

2.2 Firm Age Models and the Relevance of Maturity 58

2.3 Data 62

2.3.A Measuring Firm Age 62

2.3.B Sample Selection 63

2.3.C Fundamental Characteristics 64

2.4 Firm Maturity and the Cross-Section of Returns 65

2.4.A The Firm Maturity Spread 65

2.4.B Time Series of Returns 70

2.5 What is Age? 72

2.5.A Firm Age and Growth 74

i

2.5.B Firm Age and Innovation Perspectives 77

2.5.C Firm Age and Process Efficiency 79

2.5.D Liquidity and Cash Flow Risk 81

2.5.E Default and Debt Structure 85

2.5.F Profitability and Cash Flow Distribution 88

2.6 Firm Age and Investment Opportunities 89

2.7 Conclusions 95

2.8 References 128

Appendix 2.A 131

Appendix 2.B 132

Chapter 3 133

3.1 Introduction 134

3.2 Representing Changes in Fundamental Value with Analyst's Estimates 141

3.3 Data and Variable Construction 145

3.4 An Initial Look at the Evidence 146

3.5 The Time Path of Momentum Deciles 148

1.5.A Returns 148

1.5.B Fundamental Value by Momentum Deciles 152

3.6 Ranking The Time Path of Momentum Deciles 155

3.7 Do Prices Predict Fundamentals or Do Analysts Chase Prices 158

3.8 The Prediction Horizon of Informed Investors 161

3.9 Conclusions 164

3.10 References 177

n

Tables and Graphs

Chapter 1

Table 1.1 Sample of Initial Public Offerings 30

Figure 1.1 Age Behavior Across Time 31

Table 1.2 IPO Characteristics by Firm Age 32

Table 1.3 IPO Characteristics by Industry 34

Table 1.4 EPO Characteristics by Size and Value 36

Table 1.5 First Day of Trading Returns and Survival 37

Table 1.6 Cumulative Abnormal Returns by Age Group 39

Table 1.7 Cumulative Excess Buy and Hold Returns by Age Group 44

Table 1.8 Wealth Relatives by Age Group 46

Figure 1.2 Three Year Cumulative Abnormal Returns by Age Group 47

Figure 1.3 Three Year Buy and Hold Abnormal Returns by Age Group 47

Figure 1.4 Three Year Wealth Relatives by Age Group 47

Table 1.9 Three Year Holding Period Return Distribution 48

Figure 1.5 Monthly Return Distribution by Age Group 48

Table 1.10 Distribution of IPOs per Year Cohorts and Listing Exchange Market.. .49

Table 1.11 Calendar Portfolio Returns for IPOs by Age Group 50

Table 1.12 Regression Coefficients for IPOs Three Year Returns 52

Chapter 2

Figure 2.1 Annualized Firm Age Portfolio's Returns 97

Figure 2.2 Annualized Firm Age Portfolio's Standard Deviation 97

Figure 2.3 Firms Book to Market Ratio by Firm Age 97

iii

Figure 2.4 Firms Sample as a Percentage of Total CRSP Firms 97

Table 2.1 Firm Sample and Summary Statistics 98

Table 2.2 Expected Monthly Returns by Age Group and Industry 99

Figure 2.5 Firm Age Decile Portfolios Returns 103

Figure 2.6 Firm Age Returns in Excess of Size and Book to Market 103

Figure 2.7 Firm Age Returns in Excess to Industry 103

Table 2.3 Time Series of Portfolio Returns and Fama French Factors 104

Figure 2.8 Mature Firms Alpha Estimate to Yearly Regressions 105

Table 2.4 Firm Age and Growth Rates 106

Table 2.5 Firm Age and Innovative Edge 109

Table 2.6 Firm Age and Process Efficiency I l l

Table 2.7 Liquidity and Cash Flow Risk 114

Table 2.8 Default and Debt Structure 118

Table 2.9 Firm Age and Profitability 121

Table 2.10 Industry Concentration and Firm Age 123

Table 2.11 Capital Invested Turnover and Firm Age 125

Table 2.12 "Value Creation" and Firm Age 126

Table 2.13 New Duration Estimates 127

Chapter 3

Table 3.1 Sample Size and Distribution 167

Table 3.2 Returns and Change in Value, Cross-Sectional Evidence 169

Figure 3.1 Hypothetical Returns for Momentum Deciles 171

Figure 3.2 Returns for Momentum Deciles 171

iv

Figure 3.3 Change in Value for Momentum Deciles 171

Table 3.3 Returns and Change in Value for Momentum Portfolios 172

Table 3.4 Returns and Change in Value for Past Value Portfolios 173

Figure 3.4 Change in Value for Past Value Deciles 174

Figure 3.5 Returns for Past Value Deciles 174

Table 3.5 How Fundamental Value Evolves When Momentum Works 175

v

Acknowledgements

The author is very grateful to Wei Jiang, Andrew Ang, Bruce Greenwald, James Scott,

and Tano Santos for all of their support, advice and mentorship. Also, the author

appreciates being a researcher at the Heilbrunn Center for Graham & Dodd Investing

working with Bruce Greenwald and James Scott. The author values comments from

Daniel Paravisini, Francisco Perez, and seminar participants at Columbia Business

School Ph.D. Seminars. Finally, the author thanks Gustavo Grullon and James Weston

from Rice University for initial discussions on this topic and sharing an initial compiled

age dataset.

vi

To my loving wife, Christina,

My always encouraging parents, Jose Antonio and Maria de los Angeles,

My dear parents-in-law, Bob and Jean,

My Brothers, Jose Antonio and Eric,

And all of my beloved family members.

vn

1

Chapter 1: Firm Maturity and IPO Underperformance

1.1 Introduction

The long run underperformance of Initial Public Offerings (IPOs) documented by

Ritter (1991) and Loughran and Ritter (1995) suggests that investors may systematically

be too optimistic and posses challenges to market efficient hypothesis. This paper

addresses two primary issues related to IPO underperfomance. First, the paper analyses

IPO performance in terms of firm maturity (age) at the time of the firm's IPO, if

investors are systematically overoptimistic firm maturity should not matter in the

observed performance. We find that IPO post-issuance long-run (three to five years)

underperformance comes primarily from young firms (less than 9 years old), while old

firms (more than 40 years) show no underperformance. Furthermore, young firms have a

higher probability of being delisted before their third year anniversary.

Second, the paper examines the pre and post 1970 IPO performance and sheds

light into the reason for the unobserved and observed underperformance pre- and post-

1970 as shown by Gompers and Lerner (2003). Prior to 1970, the percentage of young

firms listed was small. Post 1970, with the introduction of the Nasdaq, the percentage of

young firms listed grew dramatically to account for 50% of public offerings. An

expansion of firms with low short-term profitability and high growth expectations would

increase the number of failures and overall IPO underperformance. Hence, the

proliferation of young firms going public during the 70's, 80's and 90's (which more than

doubles every decade) caused lower survival rates and more underperformance than in

previous decades.

2

This paper shows that firm age is related to both size and book-to-market ratio,

and that age monotonically increases as size and book-to-market increase. If investors are

indeed overoptimistic, most likely they would be towards young firms which offer

potential high growth rates, positively skewed returns, and could be the "next big thing."

However, it is important to consider that young firms also will take longer to show

profitability, have high uncertainty on future cash flows, and high probability of being

delisted in their early years.1 This paper identifies the group of firms (young) which are

the main source of IPO underperformance. To my knowledge this is the first paper that

connects firm maturity to IPO performance using such a large dataset covering 9,400

IPOs from 1935 to 2002.

Proponents of efficient markets argue that once an IPO is publicly traded it is the

same as any other stock and, thus, aftermarket stock price should appropriately reflect the

share's intrinsic value. Consequently, risk-adjusted post-IPO stock price performance

should not be predictable. Miller (1977) proposes that investors have heterogeneous

expectations regarding the valuation of a firm, however given constraints on shorting

IPOs at the time of offering, only the most optimistic investors will buy the IPO; over

time, as the variance of opinions decreases, investors' valuations will converge towards

the mean valuation and its price falls. This is consistent with the drop in share price at the

end of the lockup period. An important question is whether IPO underperformance occurs

because of institutional constraints, such as short sale restrictions in the IPO market

which prevent the expectations of pessimistic investors from being reflected in the

offering price; or because of systematic over-optimism on behalf of investors.

The paper presented in chapter 2 decomposes firm age effects, listing cohort, and year effects for several fundamental characteristics associated with a firm.

3

Barberis and Huang (2007) propose that under cumulative prospect theory

positively skewed securities in small supply can be "overpriced" and therefore earn

negative average excess returns. It appears that investors could think of these stocks as

"lotteries," a heterogeneous holdings equilibrium can be supported as long as they are in

small supply and have sufficient positive skewness. We find young and small firm IPOs

are indeed in small supply relative to the market and that these firms tend to have higher

skewness than older and/or bigger firms. It is very plausible that investors are overpricing

young-small IPOs thinking that these might be the next "Ebay" (which quintupled its

market value three years post IPO) while underweighting their failure probability.

Rajan and Servaes (1997) show that IPOs have better long-run performance when

analysts ascribe low growth potential rather than high growth. They study earnings

growth forecasts and find that within six months of the IPO analysts predict that these

firms will grow approximately six percentage points faster than their industry peers.

Long-run growth predictions decline substantially over the following months, suggesting

that analysts eventually realize the predicted growth cannot be attained.

Because young firms have very short histories, investors may find it more

difficult to asses their growth potential and current value contributing to potential over-

optimism. Pastor and Veronessi (2002) argue that young firms have higher uncertainty of

future profitability and growth perspectives than older firms, and as firm age increases

this uncertainty decreases due to a learning process. However, it seems there may be

more than learning involved, young firms are innovation driven and hence face a high

probability of failure due to barriers of entry and volatility in the demand for their "new"

4

products. For the average investor, it is difficult to differentiate the next 'Yahoo' from

other young firms that might perish soon after their IPO.

Several other papers have proposed different explanations of why the observed

underperformance occurs. Teoh, Welch, and Wong (1998) propose that firms are eager to

'look good' when they conduct their IPO, and the market has difficulties in disentangling

carefully hidden warning signals. This would suggest that at least part of the poor long-

run performance is due to a market that is unduly optimistic and unable to properly

forecast tougher times ahead. This argument would point towards overconfidence by both

entrepreneurs (Bernardo and Welch, 2001), and investors (Daniel, Hirshleifer, and

Subrahmanyam, 1998). However, as mentioned earlier, investors might be more

optimistic towards younger firms (which could potentially give misleading information)

rather than well established mature firms which have a long track record.

In addition, Mikkelson, Partch, and Shah (1997) show that poor long-run return

performance is also accompanied by poor financial accounting performance post-IPO

relative to pre-IPO performance and industry conditions. Similarly Jain & Kini (1994)

show a significant decline in operating performance subsequent to the IPO, which is

inconsistent with the fact that EPOs are initially priced at high price-earnings (P/E)

multiples, implying high future growth expectations on part of the investors. IPO firms

start out with high market to book and P/E ratios relative to their industry counterparts

and experience a decline in these measurements post-IPO. In general, results suggest that

investors appear to value firms going public based on the expectations that earnings

growth will continue. However, in actuality, the pre-IPO profit margins, on which

expectations are formed, may not be sustained. This paper proposes that these

5

expectations may be higher and more uncertain for younger firms than for mature firms.

Additionally, some of the poor performance could be attributed to optimistic accounting

early on in the life of the firm.

Brav and Gompers (1997) suggest that venture backed IPOs outperform non-

venture backed IPOs when portfolios are equally weighted and that value weighting

significantly reduces performance differences and substantially reduces under-

performance for non-venture IPOs. They also show that IPO underperformance is a

characteristic of small and low book-to-market firms regardless of whether they are IPO

firms or not. In addition, Brav and Gompers, propose that VCs may be able to entice

more and higher quality analysts to follow their firms, thus, lowering potential of

asymmetric information between the firm and investors, implying that markets may not

discount the shares of non-venture backed companies enough. Consistent with Brav and

Gompers, this paper shows that both size and B-M characteristics are related to EPO

underperformance; however, we show that age is key characteristic that explains IPO

underperformance above size and book-to-market. Since young-small IPOs represent a

very small percentage of the overall IPO market value (small supply) it is not surprising

that by value weighting performance differences would be mitigated. It would be

interesting to analyze whether having a VC helps differentiate young IPO winners from

losers by lowering the information asymmetry between investors and firms, and whether

VCs might let a firm mature longer before taking it public.

On the other hand, Gompers and Lerner (2003) convincingly show that in an

earlier sample from 1935 to 1972, IPOs do not underperform benchmarks on the

aggregate, arguing that IPO underperformance observed in the last three decades may be

6

just a small sample effect. Thus, there may be no IPO underperformance ex-ante, but

during the post-1970 period we may have drawn a small sample where too many IPOs

perform very poorly ex-post. In contrast we show that post 1970 underperformance is

attributed to failure of young firms. Schultz (2002) presents arguments that point towards

market timing, where we would observe that more IPOs follow successful IPOs and

hence the last large group of IPOs would under-perform and be a relatively large fraction

of the sample. However, Ang, Gu, and Hochberg (2005) develop a model to test whether

IPO underperformance may result from observing too few star performers ex-post rather

than expected ex-ante. After allowing for returns to be drawn from mixtures of

outstanding, benchmark, or poor performing states, they find that small sample biases are

extremely unlikely to account for the magnitude of the post-1970 IPO underperformance

observed in data. The evidence in this paper suggests that the post-1970

underperformance is due to the large quantity of young firms listed in the Nasdaq. Young

firms listed on Nasdaq account for 43% of the sample of IPOs or 4,070 firms. When

Nasdaq was introduced, the volume of EPO's (particularly young firm IPOs) increased

dramatically, allowing for weaker firms to list on a more lenient exchange which may

have made it harder to differentiate good firms from bad ones, increasing the "lottery"

behavior of these group.

The rest of this paper is organized in the following way: Section 1.2 presents the

data and its characteristics. Section 1.3 describes the methodologies used to measure IPO

performance. Section 1.4 presents results. Section 1.5 concludes the chapter.

7

1.2 Data

The main focus of this chapter is to introduce firm maturity as a key characteristic

to explain the firm's post IPO performance. The sample is comprised of 9,400 initial

public offerings from 1935 to 2002. To be included in the sample, an IPO firm must have

an offer price greater than one dollar and must be subsequently listed on the Center for

Research in Securities Prices (CRSP) tapes within six months of the offering date. In line

with previous literature, all unit offerings, REITs, ADRs, limited partnerships, and public

offerings of close-end funds are excluded from the sample. IPOs are identified using two

methods: (1) IPOs that occurred between 1970 and 2002 are identified using the

Securities Data Corporation (SDC) Global New Issues database. (2) IPOs that occurred

before 1970 are identified following the data sources and methodology described in

Gompers and Lerner (2003).

Firm age, the key piece of information, is computed as the difference between the

year of IPO and the earliest of either its founding or incorporation date. The founding

dates and/or incorporation dates are obtained from the compilation of two datasets. (1)

The extended Jovanovich and Rousseau (2001) dataset. This dataset was extended by

Fink, Fink, Grullon and Weston (2005); it contains the date of first incorporation and/or

founding date for a sample of publicly traded firms between 1925 and 2005. (2) The

Field-Ritter dataset of company founding dates, used in Field and Karpoff (2002) and

Loughran and Ritter (2004). This dataset contains the founding dates for 8,309 firms that

went public in the U.S. during 1975-2005. All where the founding dates were different

the data was verified by hand; we did 280 additions and about 350 corrections.

8

The resulting dataset was then extended by hand using various sources. Kelley's

"Business Founding Date Directory" was used to identify extremely old firms that were

founded before 1915. The data sets mentioned in the previous paragraph contained

several firms that were classified as having between zero and three years of age at the

time of listing. Some of these firms (especially those with very high asset values or sale

levels) seemed unlikely to be "new" firms. Therefore, these dates were verified by hand

and in most cases reclassified. The "International Directory of Company Histories" was

used to obtain these additional founding dates, and also to assign the correct founding

dates for spin-offs, rollups, and reverse LBOs, which in most instances had received the

date of issuance as its founding date. Several dates were also verified through electronic

databases and the firm's own webpage. It was difficult to determine the founding date

for some of the reclassified firms. In those cases a judgement about the most accurate

date was based on the whole history of the firm, its subsidiaries and/or divisions that went

public.

In order to explore the effect of firm age on IPOs aftermarket performance in

detail, I have designated twelve age groups based on the age of the firm at the time of the

IPO: 1) zero years; 2) one year; 3) two to three years; 4) four to five years; 5) six to eight

years; 6) nine to twelve years; 7) thirteen to eighteen years; 8) nineteen to twenty seven

years; 9) twenty eight to forty years; 10) forty one to fifty five years; 11) fifty six to

seventy five years; and, 12) firms greater than 75 years. Given that age is computed as the

difference between two years, a firm can be classified as a one year firm even though it

might be some months older or younger than this age; for example if the firm is

2 Business and Company Resource Center, Business Insights, D&B Million Dollar Database, Hoover's

Company Capsules & Profiles, and Thomson ONE Banker.

9

incorporated in January of 1990 and listed on December of 1991 it will be classified as a

one year old firm, however, effectively it would be almost two years old. These

differences in age should average out to the corresponding age group given the size of the

sample. Particular attention should be placed on the zero age group, many of these firms

(especially those listed on the NYSE) might not be as young as presumed. Even though

these firms were revised by hand it was not possible to find more information that would

change their current classification, but it is very likely that these firms are either spin

offs, roll-ups or consolidating firms. This group was included in the sample for

completeness, but all conclusions will be derived from the other eleven groups.

Table 1.1 presents the IPO sample distribution over time and includes number of

IPOs per year, median and average firm age. The sample of IPOs is mostly concentrated

in the mid 80 's and throughout the 90's. It can also be observed both in Table 1.1 and

Figure 1.1 that firm age decreases dramatically after 1970, both on the mean and the

median. Previous to 1970, average age fluctuated between 40 and 50 years old. Post-

1970, firm age dropped significantly below 30 years, reaching a 10 year low in 1999.

Moreover, table 1.1 presents the average, median, 25th percentile, and 75th

percentile rankings of market value and the ratio of book to market equity value at the

close of the first day of trading for each year. The table shows that market value varies

across time, in general the average market value is high during the period of 1945 - 1972,

then drops considerably during the 70's and 80 's, and not surprisingly rises during the

late 90's. It is important to note the behavior of the book to market ratio both on the

median and the average, which previous to 1956 is usually above 1.0, declines to 0.51 in

10

1968, then bounces back to ratios above 1.0 in the late 70's and decreases consistently

during the 80's and 90's to hit a minimum of 0.5 in 2000.

Market value is computed using the closing price of the first day of trading

multiplied by the number of shares outstanding given by the CRSP tapes. In order to be

able to compare between years, market value is normalized to 2002 dollars, adjusting by

the Consumer Price Index. Since at the date of the IPO book value has not yet been

reported for any firms, the book to market ratio is set to the average of the three digit SIC

industry book to market ratios. Book value is computed using Compustat North America

data tapes, and is defined as the sum of shareholders equity (data60), balance sheet

deferred taxes (data74), and investment tax credit (data51). In addition to the book value

computed with Compustat, the dataset is enhanced by including the hand-collected book

equity values from the Moody's Industrial, Public Utility, Transportation, and Bank and

Finance Manuals provided by Davis, Fama and French (2000). The book to market ratio

is computed using concurrent book value and market values.

Table 1.2, panels A and B, present the sample distribution, average, median, 25th

percentile, and 75th percentile rankings of market value and the ratio of book to market

equity value at the close of the first day of trading for each of the twelve age categories.

Table 1.2, panels C and D present statistics on the sample one year after the first day of

trading. The book to market ratio of each firm is updated as information from Compustat

becomes available. Hence, the descriptive statistics presented in panel D correspond

mostly to the book to market ratio of the firm and not the three digit SIC industry average

of book to market ratios. It can be noted that 60% of sample IPOs can be classified

between 2 and 18 years old and that market value increases as firm age increases,

11

implying that older firms are also bigger firms. At the same time, book to market ratios

increase as firm age increases. The average book to market in panel D revolves around

0.5 for firms below 18 years and climbs to 1.01 for firms above 75 years old, implying

that young firms behave as growth firms and old firms as value ones.

Table 1.3, panels A, B, and C, present the sample distribution, average, median,

25th percentile, and 75th percentile and rankings on the mean of firm age, the median for

market value, and the ratio of book to market equity value at the close of the first day of

trading for each of the twelve industry group categories defined in Fama and French

(2002). Table 1.3, panel D, presents statistics on the book to market ratio one year after

the first day of trading.

In panel A, it can be observed that 73% of the IPO sample is comprised of firms

in the following sectors: Business Equipment, Shops, Finance, Health, and Other.

Business equipment, firms which make up almost 24% of the sample, are ranked as the

second youngest industrial sector with an average age of 10.89 years; health firms rank

first and telecommunication rank third with average ages of 9.61 and 11.48 years

respectively. Not surprisingly, these industry sectors are considered to be high growth

and high risk ones, with potential high outperformers as wells as several firms that will

perish. On the other hand, utility firms which only make for 1.36% of the sample are

ranked as the oldest sector with an average age of 36.36 years; utility firms are followed

by finance, non-durables, and manufacturing industry sectors with average ages of 34.84,

32.04, and 31.19 years respectively.

In panel B, it can be observed that median rankings of market value realized after

the first day of trading for finance, durables, shops, and health sectors receive the lowest

12

ranking or market value; the sectors of energy, chemicals, telecommunications and

utilities receive the highest rankings. In panel C, it can be noted that median rankings of

book to market value ratios realized after the first day of trading for health,

telecommunications, and business equipment industries can all be considered growth

sectors; meanwhile the industry sectors of manufacturing, utilities, and finance are

classified as value sectors. This classification is maintained one year after the first day of

trading as it is observed in panel D.

Table 1.4 presents the sample distribution and firm age median and average based

on the Fama and French (1992) quintile classification on size and book to market ratio.

Panel A classifies each IPO based on the information available on the first day of trading;

most of the sample is classified on the second, third and fourth book to market quintiles.

Panel B classifies IPOs based on information available one year after the first day of

trading. This is a more representative descriptor of firms characteristics, the majority of

firms are classified in the upper left triangle (small growth firms). Furthermore, it should

be noted that in both panels A and B it is observed that age increases monotonically as

size increases well as book to market ratio increases, which classifies old firms on

average as big value firms and young firms as small growth firms. When Brav and

Gompers noted that underperformance is a characteristic of small, low B-M firms, they

did not consider that a key characteristic of these firms is that they are extremely young

and hence have higher uncertainty and probability of failure. We will address this point in

detail in Section 1.4.

13

1.3 Methodology

Following standard practice, the long-run performance over t months, particularly

3 and 5 years (36 and 60 months) of initial public offerings is measured by constructing

benchmark-adjusted returns for stock /' relative to benchmark m in month t as the

difference between raw returns on the IPO and benchmark returns for the corresponding

period, rt t (m) = Rit -Rmt where R{, is the raw return of firm i in event month t and Rmt

is the benchmark return in event month t. Returns are computed from the first listing on

the CRSP daily return tapes. Event months are defined as successive 21 trading-day

periods relative to the IPO date. The first day of trading (month 0) is set apart and is not

included in the analysis. Thus, returns for the first month comprise of returns listed on

days 2 to 22, the second month listed on days 23 to 43, and so on. Because several of the

EPOs are delisted before reaching their 60th month of trading, it is important to adjust for

the delisted return. Furthermore, it will be shown that the probability of being delisted,

which is higher for young firms, plays a key role in the observed underperfomance.

Delisted EPOs are assigned the delisting return provided by CRSP event tapes. This

paper presents results using six different value weighted benchmarks:4 1) matching Fama

and French quintile portfolios for size and book-to-market; 2) matching industry

portfolios using Fama and French twelve industry classification; 3) all stocks traded on

the NYSE, Amex, and Nasdaq; 4) stocks traded on the Nasdaq; 5) NYSE small stocks;

and 6) S&P500.

In addition to the tests presented in this paper using CRSP event tapes delisting return, tests were also run using a delisting return of-0.3 for all non-Nasdaq firms and -0.55 for Nasdaq firms as suggested by Shumway and Warther (1999). These results were very similar and consistent with the presented results.

Results for equal weighted benchmark portfolios were also computed. These results were stronger than the ones presented of value weighted portfolios; in that sense, these results are more conservative.

14

Following Ritter (1991), cumulative average adjusted m benchmark excess returns

(CAR) to event month horizon s for each age group are defined as:

5 nage,t

CARages (m) = j ] ARagel (w) where ARagel (m) = •£- £ rit (m) and naget is the number of

stocks in the IPO age group portfolio in event month t. Thus, ARaget (m) is the average

benchmark-adjusted return across all IPO firms in the pertaining age group category in

event month t.

In addition to cumulative average adjusted returns, two other measures are

implemented: (1) Cumulative excess (abnormal) holding-period returns (CHP) and (2)

wealth relatives (WR) of stock i relative to benchmark m until the earlier of horizon event

month s or its delisting per age group: CHPis (m) = ]~|(1 + rit) - Y[ 0 + rm,t) where rit is

the raw return of stock i and rm t is benchmark m return as defined previously. This

represents the excess return of a zero-cost strategy that goes long on an IPO and shorts

the benchmark m portfolio. Cumulating these returns for each age group provides the

long-horizon return to this zero-cost strategy.

;'=1

For ease of comparison between cumulative excess holding periods across

different horizons, annualized CHP statistics are computed using the following

transformation: CHPaa™a,bed (m) = (1 + CHPages (m))u,s -1

Wealth relatives (WR) are computed using the month s holding period returns (HPis)

defined as:

15 age

1 + — YHR, "age,* i-t ' ' J

WRavp, = ^ age.s age

^ + — YHPms nage,s i-J m . s

1=1 5 5

where HPis =Y\(l + Ri,,)~l ^^ Hpm,s = E I 0 + R*,>)~*with ^.•,«(,n) b e i n 8 t h e r a w

r=l (=1

return on stock / in event month t and R, the benchmark return in event month t for the

same period and age group. HPis measures the total return from a buy and hold strategy

where a stock is purchased at the first closing market price after going public and held

until the earlier of horizon event month s or its delisting. Wealth relatives greater than

1.00 can be interpreted as IPOs outperforming the benchmark. A wealth relative less than

1.00 indicates that IPOs underperformed the benchmark.

1.4 Results

1.4.A Event-Time Returns

One key finding of this paper is that IPO overall underperformance is

concentrated around young firms, this section presents the analyses of IPO returns in

event time. We start by examining the first day of trading of an IPO and its survival

expectation given its age at the time of the IPO. Delisted firms are classified using

CRSP's delisting code (dlstcd) where 400-499 is assigned to firms that have been

liquidated and 500-599 is assigned to firms that are dropped from the CRSP lists for no

clear reason. Firms that are merged or acquired codes 200-299 are classified as such.

Table 1.5 shows that between 8% and 18% of the IPOs within the age range of 0 and 8

years (young firms) will be delisted by the third year of trading; while for IPOs over 40

16

years {old firms) delisting probability is only between 0.9% and 2.2%. By the fifth year,

between 15% and 31% of young firms will have been liquidated or dropped from the

exchange; in comparison, only 5% of old firms will have such fate. At the same time, the

probability of being acquired or merged is slightly higher for young firms than old ones.

It can be noted that, with the exception of the zero-age group, the probability of a firm

being delisted decreases monotonically as age increases.

It is no surprise that average EPO underperformance is deeply linked to all the

firms that are being delisted and that these firms extremely negative returns weight down

overall perfomance. However, before this study, it was hard to disentangle which firms

would have an ex-ante lower survival rate. Young firms have new products with high

growth potential but at the same time have to prove themselves in the market and break

barriers of entry in a competitive environment which lowers their survival probability.

This paper shows that young firms have a high probability of being liquidated and also

comprise a large percentage of the IPO sample (especially after 1970 when Nasdaq was

introduced). Therefore it is clear that a large source of the observed underperformance

can be attributed to young firms.

The high uncertainty on returns and survival related to young firms should be

reflected to an extent in the underwriters price discount of the offering, the IPO's first day

of trading return (jump) measured as the difference between the bidding price and the

closing price of the day can be used as a proxy for this discount. Panel C of Table 1.5

shows that young IPOs have an average jump of more than 5.5% on the first day while

old firms jump less than 2.7%. We would expect for young firms to have a larger

discount than old firms because of the uncertainty related to these firms. At the 75th

17

percentile young firm's discount is about 7.5% while old firms discount is about 2.7%.

However, it may be that this discount is not large enough for young firms given their risk

profile. It is worth emphasizing that this is not the total first day return that an investor

could attain by buying the shares at the offer price from the underwriter which tends to be

lower. The reason for using the bidding price is that for firms not recorded on SDG

(before 1970) the offer price is not available.

The three and five year cumulative abnormal returns computed for each of the

twelve age categories, as well as for all IPOs is presented in Table 1.6, panels A and B.

Delisted IPOs receive the delisting return as explained previously, and thereafter, the firm

and its benchmark are excluded from all computations. The left columns present the

CAR's for the six selected benchmarks as delineated previously, the right columns report

t-statistics computed following Ritter (1991).5 In general, by the third year firms have a

20% underperformance relative to the diverse benchmarks. However, once we observe

the different age categories, it is clear that the underperformance is attributed to younger

firms rather than older ones. Underperformance of IPOs relative to benchmarks

monotonically decreases from an average 50% for extremely young EPOs (zero and one

years old) to a mere 2% (statistically insignificant) for firms older than 56 years old. It is

also important to note that by the fifth year, old firms increase their underperformance up

to 20% in some benchmarks. Young IPOs remain the major losers underperforming up to

75%, but the firms between 13 and 40 years old {mature firms) seem to improve their

performance after surviving the third year. It would seem that once the mature EPOs are

able to establish themselves as good firms (having removed all bad EPOs) these firms

These t-statistics must be interpreted with care, since Barber and Lyon (1997) show that the small sample distributions for the CAR statistics are severely skewed compared to the Ritter (1991) asymptotic distributions.

18

offer better growth opportunities and performance than older firms which are not be able

to grow at the same pace nor outperform the benchmarks.

Since a high percentage of young firms suffer the fate of being delisted

(liquidated), panels C and D present the three and five year cumulative returns, excluding

firms that will be delisted. The goal is to test whether the out-performance of old firms

relative to young firms would hold for those firms that do not fail during the first years of

the IPO. By excluding delisted firms, the three year underpeformance relative to

benchmarks becomes insignificantly different than zero for all age groups, except the

zero-age firms. Five years after the IPO, mature firms that would be classified between

10 and 20 years old are outperforming the benchmarks. Meanwhile, the performance of

old firms is not strong.

In order to investigate the importance of firm age in IPO performance in contrast

to size and book-to-market characteristics as suggested by Brav and Gompers, we

compute cumulative abnormal returns sorting firms into seventy five groups based on

size, book-to-market & age. Panel E sorts firms into size and book-to-market quintiles

based on the information available on the first day of trading, as in Table 4, and

subsequently into three age groups6 and then computes the cumulative abnormal returns

relative to the NYSE, Amex & Nasdaq value weighted benchmark. The results show that

IPO underperformance is mostly attributed to firm age rather than just pure size and

book-to-market characteristics. All of the young firm's twenty five size & book-to-

market groups have a negative three performance ranging from -5.9% to -85.5% (sixteen

of the twenty five groups are statistically significant). In contrast mature and old firms

6 1) Finns between 0 and 8 years (young); 2) Firms between 9 and 40 years (mature); 3) Firms 41 and above (Old).

19

show mixed results of underperformance (some are positive and some negative) and with

a lower magnitude, only seven of the mature firms groups that show underperformance

are statistically significant and only three of the old firms groups have significant

underperformance.

Panel F classifies firms into Fama and French groups based on the information

available seven months after the first day of trading, thus we have a better classification

in the book-to-market ratio as we incorporate firm's book information rather than the

industries average as explained earlier in Section II. The results are consistent with Panel

E, most of the statistically significant underperformance is concentrated in young firm's

category (fifteen groups), particularly in the first three small and mid size groups with no

difference in the book-to-market ratios. These results seem to indicate that EPO

underperformance can be attributed to young-small firms rather than being a size and

book-to-market characteristics effect.

In Panel G we present total market value after the first day of trading adjusted to

2002 dollars for all IPOs within their size and book-to-market category. The panel also

presents the percentage that each group represents of overall IPO market value and the

average IPO market cap for each category. Young small & mid cap IPOs represent about

8% of the total EPO market value captured on the first day by the entire sample, while

they comprise 36% of the sample firms. Consistent with Barberis and Huang young firms

are positively skewed, offering potential high growth rates with a small probability of

extremely high returns, given their small supply it is very plausible that investors

overestimate their success probabilities and overprice these stocks. Hence it may not be

20

surprising to find that IPO underperformace is linked with the vast number of "lottery

like" young firms which impact is magnified in equal weighed portfolios.

Table 1.7, panels A and B, present three and five year annualized cumulative

excess holding-period returns for each age category. On average, IPOs underperform

relative to their benchmarks by an annual 4.5% and 3.5% on a three year and five year

buy and hold strategy, respectively. However, when underperformance per age group

over a three year period is closely examined, again, most is concentrated among young

firms ranging from -4.9% to -15.5% annual excess returns, depending on the benchmark

and age group that is taken. Buy and hold returns for old firms over a three year period

range from -1.2% to 3.8% (all of which are statistically insignificant), depending on the

benchmark that is observed; in effect, firms above 40 years show a zero

underperformance. For all benchmarks, underperformance decreases monotonically as we

increase the age of the firm. As was observed in the previous table, when considering a

five year holding period, mature firms start to show better performance as older firms'

performance declines; meanwhile, young firms keep showing strong underperformance,

particularly in the extreme young firms group. Figures 1.2, 1.3 and 1.4 graph the

cumulative abnormal returns, buy and hold, and wealth relatives for each age group in

relative to the benchmarks.

Table 1.8 presents additional evidence of underperformance for young firms vs.

old firms. This table shows the three and five year wealth relatives for each of the twelve

age categories. Young firms show a loss in investors' wealth between 20% and 35% over

the first three years and between 23% and 51% during a five year period. Clearly,

investors would have done much better by investing in the benchmarks. At the same time,

21

old firms range in a wealth loss of 7% up to a gain of 4%, depending on the benchmark

and age group during the three year period, and a loss between 13% and 2% on a five

year period. Again, investors' wealth relative measures increase monotonically as firm

age increases.

The previous results show that young firms will on average underperform. Such

underperformance is attributed mostly to the high percentage of young firms that delist

prior to their 5l year anniversary. In order for investors to invest in these firms that have

higher uncertainty and lower survival probability, these firms must also offer high growth

perspectives and returns when they are successful. As shown earlier, most of the young

firms are classified in technology intensive industrial sectors, such as health, where

innovation carries high rewards for the firms that are able to ensure themselves market

share. Table 1.9, presents the three year holding period return distribution (percentile

breakpoints) per age group. As expected, we observe that in the bottom 2nd and 5th

percentile points (the major losers) young firms have lower returns than older firms,

which decreases monotonically. In the 2 percentile, young firms have lost up to 98% of

their value, while old firms only present a loss between 76% and 90%. In the 5th

percentile, young firms have a loss of 93%, while old firms will have lost only between

63% and 76%. In the 40th percentile, young firms show a strong loss of 40% to 49%,

while old firms start showing a small out-performance. When we look at the top winners

(star IPOs) in the 95th and 98th percentiles, in Figure 1.6, we can observe that the returns

for young firms are much higher than for older firms. In the top 95th percentile, young

firms will have returns between 350% and 400%, while star old firms will have more

moderate returns around 240%. In the top 98th percentile of extreme success stories (such

22

as Yahoo, Microsoft, and Google), three year holding period returns for young firms is

between 584% and 737%. Older super star firms also show amazing returns between

300% and 395% but these returns are significantly lower than those offered by the supper

star young firms.

1.4.B Calendar-Time Returns

In addition to the previous event-time performance tests, this paper presents

calendar-time tests. Schultz (2003) argues that there is no evidence of IPO

underperformance in calendar-time. Consistent with the results presented by Ang, Gu,

and Hochberg, the results presented in this paper show that contrary to Schultz's claims,

IPO underperformance is also observed in calendar time. Furthermore, by classifying

IPOs according to the listing exchange, it is apparent that most of the underperformance

is attributed to the vast number of young firms listed in the Nasdaq.

The paper by Schultz focuses only on one-month holding-period returns. In order

to examine calendar-time returns, this paper looks at holding periods longer than one

month, by reporting 12, 36 and 60-month holding periods. In month t, IPO portfolios are

formed placing an equal amount of money in all IPOs that have gone public over the last

year. This portfolio will be held from time t to M-12, 36, or 60-months and will be

rebalanced to only hold IPO's that have gone public over the last year. Therefore, the

calendar-time IPO portfolio returns represent the returns on an equally-weighted portfolio

of IPO's, with each IPO occurring no later than one year. After computing the calendar-

time IPO raw returns, the NYSE, Amex, and Nasdaq value weighted returns are

subtracted to obtain the benchmark-adjusted holding-period returns in calendar time.

23

Table 1.10, panel A, presents the total number of IPOs realized during five year

periods across time, the sample is also broken into three main age groups: young (firms

under 9 years), mature (firms from 9 to 40 years), and old (firms above 40 years). Each

age group is subsequently broken into three groups depending upon which exchange

market lists each (NYSE, Amex, or Nasdaq). A very small fraction of firms are listed in

other exchanges. Panel B presents the same distribution as a percentage of IPOs realized

during the time period. The table shows how at least in this sample, all IPOs listed

previous to 1960 occurred in the NYSE. During these 25 years, old and mature firms

dominated the IPO market as a percentage of listed IPOs. However, in the 60s the

American Exchange became a key market in which to list; with more lenient rules than

the NYSE, the number of IPOs more than doubled, consisting mainly of young and

mature IPOs that listed on the Amex. The percentage of listing young firms increased

from an average of 10% previous to 1960 to 20%. Although mature firms still comprised

43%> of the listings, half of the listings occurred on the Amex.

Post-1971, when the Nasdaq was introduced, the number of IPOs in the Amex

decreased significantly as firms opted to list on the Nasdaq. Young firms' listing

percentage rose to become 50% of the listings by the end of the 70s. During the 80s, the

IPO market quadrupled as even more firms began to list in the Nasdaq: young firms made

up 52% of all listings, mature firms composed 30% to 36%, and old firms only accounted

for 12% to 17%. During the 90s (the dot-com era), the IPO market once again almost

doubled, averaging 2,350 IPOs every 5 year period: young IPOs made up between 52%

and 59% of all IPOs, mature firms were 34% to 38% of the IPOs listed, and old firms

shrunk to less than 10%. Post Bubble-Bust in 2001, the IPO market shrunk severely: the

24

percentage of old firms rose to 18%, and the percentage of firms listed on NYSE doubled

the previous decade percentages.

Calendar-time IPO portfolio returns observed over 12, 36 and 60-month holding

periods are reported in Table 1.11, Panel A. The portfolios are formed based on the firm's

age and the exchange in which it is listed. The left side presents the mean using

overlapping observations of annualized portfolios raw returns, the right side presents

annualized excess returns. The reported f-statistics are computed with Newey-West

(1987) standard errors, using a lag length of one less than the holding-period horizon.

Thus the statistic corrects for moving average errors induced by the overlapping

observations. Panel B presents portfolio returns for the time period from 1935 to 1970;

Panel C presents returns for the post-1971 period; Panel D presents returns for portfolios

formed from 1962 to 1970 when Amex was a very popular stock exchange.

Inspecting Panel A, it can be observed that across the entire time period, excess

returns are positive and it would seem that there is no underperformance relative to the

market. Nevertheless, it can be noted that, on average for all firms, returns increase as

firm age increases, and especially for firms listed on the NYSE; a less clear relation can

be established for firms listed on the Nasdaq and Amex, which will be explained in the

other three panels.

In Panel B, IPOs listed before 1971, in general all stocks listed on the NYSE

performed similarly regardless of their age group. However, stocks listed on the Amex

show outstanding performance, with average excess returns of 15.65%, 19.25%, and

15.88% for 12, 36, and 60-month holdings, respectively. Since IPOs listed on the Amex

occurred only after 1962, Panel D, compares NYSE and Amex over the exact same

25

period. It can clearly be seen that Amex IPOs outperform NYSE firms. Further,

investigation of the firms listed on the Amex over the same period, sheds light onto why

these firms outperformed others so impressively: during the 1964-1966 time period, some

young and mature firms backed with big government contracts were listed in the Amex

and as a result significantly outperformed the market given the special conditions of their

projects.

Panel C shows that, with the exception of the particular period of the 60s, post-

1971 when the Nasdaq is introduced, young firms underperform significantly and old

firms outperform the benchmark. Nasdaq & Amex listed firms perform better than NYSE

IPOs, during this period even NYSE mature firms show underperformance. However, its

important to keep in mind that NYSE stocks represent a very small percentage of the

post-1971 sample and particularly during the 70's and 80's there are very few young and

mature firms; hence, a few bad firms might be down-skewing results.

Calendar-timer results show that IPO underperformance is stronger in the post-

1971 era mainly because of two factors: 1) A dramatic explosion of young firm IPOs

which inherently carry a higher potential for growth and returns when successful; when

not successful, however, there is a higher probability of failure and delisting, and also a

tendency towards less reliable information being conveyed to investors. 2) The

introduction of the Nasdaq stock exchange allowed younger firms to be listed in a less

regulated exchange. Along with Brav and Gompers (1997), it would be interesting to

consider these young firms and analyze them based on being backed by VCs or not, it

may be that VC backed firms wait longer to perform the IPO and hence, are more

successful.

26

1.4. C Regression Results

The event-time and calendar-time patterns documented above are not necessarily

independent of each other. Table 1.12 reports the results of a multiple regression using

the raw three year total return on IPOs as the dependant variable. The explanatory

variables are the logarithm of one plus age, the three year total return on the market

(NYSE, Amex, and Nasdaq value weighted), the three year return on matching Fama and

French size and book-to-market quintile portfolio, the three year return on matching

Fama and French twelve industry portfolios, the logarithm of market value and matching

average three SIC industry book-to-market ratio after the first day of trading, dummy

variables to identify the three main stock exchanges, dummy variables to identify each of

the twelve industry categories, and dummy variables to identify five year time periods.

These results support the conclusions from earlier tables. The intercept shows an

underperformance of 40% to 60%. The first regression, using only age and the market as

explanatory variables, shows IPOs underperform by 43%; however, firm age significantly

improves performance (12.43% times each logarithmic age year) which implies that firms

older than 32 years would show a zero underperformance. The other regressions show

that firm age improves IPO performance regardless of the firm's size and book-to-market

characteristics. Firm size only marginally increases the after market performance,

however, the firm book-to-market ratio does improve performance significantly (about

10% times the B-M ratio). By looking at the exchange dummy variables we note that

NYSE and Nasdaq underperformance was very similar (about 60% on average), but

Amex shows less underperformance (about 42%).

27

Additionally, the industry dummy variables revealed that in most cases the

industry was not relevant to the IPOs performance, except for business equipment, health,

and finance sectors which showed a significant better performance. Finally, the five year

time period dummy variables show that with the exception of the time period 1976 to

1980, in general the year when the IPO occurred did not contribute significantly to its

performance. It is worth noting that IPOs realized during the height of the dot com era

(1996 to 2000) decreased in performance by 28%. Those realized after the bust increased

in performance by 23%. In all the regressions, age remains an ex-ante known variable

that significantly improves the ex-post IPO performance.

1.5 Conclusions

Beginning with Ritter (1991) researchers have found that on average IPO's appear

to be overpriced and tend to underperform relative to multiple benchmarks subsequent to

their issuance for three and five year horizons. This paper analyzes IPO post-issuance

performance in terms of firm maturity {age) at the time of IPO. Expected IPO

performance has never before been thought of in terms of firm maturity; to my

knowledge this is the first time that such link is established. The data suggests that most

of the IPO underperformance can be primarily attributed to young firms (less than 9 years

old). These firms also have a high probability of being delisted before their fifth year

anniversary, up to 32%. The dramatic difference in underperformance of young firms

relative to old firms (above 40 years) is significant and robust to several benchmarks:

market indexes, size and book-to-market matched portfolios, and industry portfolios.

28

Brav and Gompers (1997) suggest that IPO underperformance occurs primarily in

small non-venture-backed IPOs; additionally, underperformance is related to small firms

with low book-to-market rations regardless of being an IPO. This paper establishes a

clear connection between firm age, size, and book-to-market ratios, showing that age

monotonically increases as size and book-to-market increases. However, our results show

that underperformance is mainly attributed to young firms, particularly to small and mid

sized ones within the young age category. Mature and old firms show very few cases of

significant underperformance, and the instances where underperformance is identified the

magnitude is significantly smaller and cannot be attributed to any particular size or book-

to-market quintile.

Gompers and Lemer (2003) show in an earlier sample, from 1935 to 1972, EPOs

do not underperform benchmarks on the aggregate, implying that observed

underperformance may simply be the result of a small sample. The results presented are

consistent with their results. Prior to 1971 there is no statistically significant

underperformance. However, prior to 1970, the percentage of young firms was very

small; post-1971, with the introduction of the Nasdaq, the percentage of young firms

listing grew dramatically to account for 50% of all new listings. Therefore, it seems that

post-1971 IPO underperformance is linked to the explosion of young firms listed both on

the Nasdaq and NYSE. Fama-French (2004) argue that in the 80's and 90's profitability

becomes more left skewed and growth more right skewed; the result leads to a sharp

decline in new list survival rates due to delisting caused by poor performance. An

expansion of weaker firms with low short-term profitability and high growth would

increase the number of failures and overall IPO underperformance. Indeed, young firms

29

have both high growth rates and longer profitability terms than older firms. Hence, the

increase during the 80's and 90's in the number of young firms going public would cause

a higher probability of being delisted. Consistent with the Fama-French findings, there

was a significant decline in firm survival rates. Our results show that IPO

underperformance is mainly attributed to a large group of young firms that in contrast to

older firms, offer very high returns in their star state (highly successful firms) making

them as a group highly positively skewed while at the same time have very high failure

incidence.

This effect may exist because young-small firms become "lottery-like" stocks to

investors who are overoptimistic about their growth perspectives and overprice these

stocks while undermining the loss probabilities. Given their small percentage of the

overall IPO market value, under cumulative prospect theory these stocks might support a

heterogeneous holdings equilibrium as proposed by Barberis and Huang (2007) and

therefore would not be socially wealthfare destroying. Additionally, analysts may

contribute to the overall over-optimism by systematically ascribing extremely high

growth rates to young firms, given that they do not have enough information and/or

history to differentiate good firms from bad ones.

Our results suggest that investors might overprice young-small IPOs thinking that

they may hold the next "Ebay" while underweighting their failure/delisting probabilities;

implying that the observed ex-post high IPO underperformance, which can be attributed

to young firms, may not be so "puzzling". Particularly once we account for the large

number of young firms which constitute more than 36% of the sample, we would expect

to observe overall IPO underperformance.

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34

Table 1.3: Initial Public Offerings Characteristics by Industry

The sample is composed of 9,400 identified IPOs from 1935 through 2002, grouped in 12 industry categories following Fama and French classification: 1) Consumer Non-Durables - food, tobacco, textiles, apparel, leather, toys; 2) Consumer Durables - cars, tv's, furniture, household appliances; 3) Manufacturing - machinery, trucks, planes, office furniture, paper, commercial printing; 4) Energy - oil, gas, and coal extraction and products; 5) Chemicals and allied products; 6) Business Equipment - computers, software, and electronic equipment; 7) Telecommunications; 8) Utilities; 9) Shops -wholesale, retail, and some services; 10) Healthcare - medical equipment, and drugs; 11) Finance; 12) Other - mines, construction, building materials, transportation, hotels, business services, and entertainment. Panel A presents the number of IPOs, sample percentage, average, median, 25th percentile, and 75th percentile, and ranking on the mean of the firm's age at the time of the IPO for each industry category. Panel B presents the number of IPOs, sample percentage, average, median, 25th percentile, and 75th percentile, and ranking on the median of market value at the close of the first day of trading (in constant 2002 dollars adjusting for the Consumer Price Index) for each industry category. Panel C presents the number of IPOs, sample percentage, average, median, 25th percentile, and 75th percentile, and ranking on the median of the ratio of book to market equity value at the close of the first day of trading for each industry category. For each firm the ratio is set to the average of the three SIC digit industry book to market ratios. Panel D presents the number of IPOs, sample percentage, average, median, 25th percentile, and 75th percentile, and ranking on the median of the ratio of book to market equity value for surviving firms one year after the IPO for each industry category. For each firm the ratio is computed using Compustat data for the book value when available or set to the average of the three SIC digit industry book to market ratios at the time.

Panel A: Firm Age (On the first day of trading)

Industry

Non-Durables

Durables

Manufacturing

Energy

Chemicals

Business Equipment

Telecommunications

Utilities

Shops

Health

Finance

Other

# of IPO's

666

214

801

238

156

2,240

313

128

1,231

988

1,055

1,370

Sample %

7.09%

2.28%

8.52%

2.53%

1.66%

23.83%

3.33%

1.36%

13.10%

10.51%

11.22%

14.57%

25th %

7.00

5.00

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5.00

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10.00

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Median

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9.00

15.00

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15.00

8.00

Mean

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31.19

18.17

26.42

10.89

11.48

36.36

19.90

9.61

34.84

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75th %

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31.00

49.00

23.00

36.00

13.00

13.00

53.00

27.00

10.00

58.00

18.00

Ranking

10

7

9

5

8

2

3

12

6

1

11

4

Total/Avg 9,400 100% 4.83 13.38 22.60 31.92

35

Panel B: Market Value (On the first day of trading)

Industry # of IPO's Sample % 25th % Median Mean 75th % Ranking

Non-Durables

Durables

Manufacturing

Energy

Chemicals

Business Equipment

Telecommunications

Utilities

Shops

Health

Finance

Other

666

214

801

238

156

2,240

313

128

1,231

988

1,055

1,370

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2.28%

8.52%

2.53%

1.66%

23.83%

3.33%

1.36%

13.10%

10.51%

11.22%

14.57%

37.09

41.36

43.19

61.64

45.49

46.24

73.98

169.68

33.54

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33.65

40.53

98.34

83.35

104.85

130.61

131.06

127.70

203.98

359.17

83.41

84.81

80.38

104.96

314.98

245.64

214.56

403.59

425.57

457.00

614.11

712.99

194.21

229.21

448.56

297.63

305.04

182.47

216.68

330.03

441.70

335.52

539.93

645.71

195.03

188.27

249.06

252.18

5

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9

10

8

11

12

3

4

1

7

Total/Avg 9,400 100% 54.98 132.72 379.84 323.47

Panel C: Book-to-Market (On the first day of trading)


Non-Durables

Durables

Manufacturing

Energy

Chemicals

Business Equipment

Telecommunications

Utilities

Shops

Health

Finance

Other

665

214

801

238

156

2,240

313

128

1,225

985

1,053

1,313

7.13%

2.29%

8.58%

2.55%

1.67%

24.01%

3.35%

1.37%

13.13%

10.56%

11.28%

14.07%

0.60

0.59

0.67

0.48

0.55

0.42

0.41

0.68

0.58

0.37

0.72

0.46

0.77

0.70

0.84

0.68

0.68

0.50

0.49

0.84

0.71

0.44

0.98

0.61

0.88

0.80

0.93

0.71

0.73

0.54

0.57

1.02

0.79

0.46

1.06

0.72

1.05

0.95

1.07

0.88

0.87

0.61

0.60

1.11

0.90

0.51

1.22

0.87

9

7

10

6

5

3

2

11

8

1

12

4

Total/Avg 9,331 100% 0.54 0.69 0.77 0.89

Panel D: Book-to-Market (One year after IPO)


Non-Durables

Durables

Manufacturing

Energy

Chemicals

Business Equipment

Telecommunications

Utilities

Shops

Health

Finance

Other

656

214

792

238

156

2,212

308

127

1,215

983

1,050

1,340

7.06%

2.30%

8.52%

2.56%

1.68%

23.81%

3.32%

1.37%

13.08%

10.58%

11.30%

14.42%

0.32

0.27

0.31

0.33

0.30

0.18

0.23

0.54

0.26

0.22

0.52

0.23

0.53

0.43

0.54

0.52

0.49

0.30

0.40

0.68

0.43

0.32

0.87

0.41

0.66

0.54

0.64

0.57

0.51

0.38

0.58

0.79

0.55

0.36

1.00

0.54

0.85

0.69

0.88

0.71

0.66

0.49

0.63

0.88

0.66

0.45

1.17

0.67

9

6

10

8

7

1

3

11

5

2

12

4

Total/Avg 9,291 100% 0.31 0.49 0.60 0.73

36

Table 1.4: IPO characteristics using Fama French 25 portfolio classification

The sample is composed of 9,400 identified IPOs from 1935 through 2002, IPOs are sorted based on Fama French size and book-to-market quintiles. Panel A presents the number of IPOs, sample percentage, firm age median and average using information available at the close of the first day of trading. For each firm the book to market ratio is set to the average of the three SIC digit industry book to market ratios. Panel B presents the number of IPOs, sample percentage, and firm age median and average using information available one year after the IPO. For each firm the ratio is computed using Compustat data for the book value when available or set to the average of the three SIC digit industry book to market ratios at the time.

Panel A: Fama French Portfolios Sorted based

Low B/Mkt

2

3

4

High B/Mkt

Low B/Mkt

2

3

4

High B/Mkt

Panel B

Low B/Mkt

2

3

4

High B/Mkt

Low B/Mkt

2

3

4

High B/Mkt

Small

46

494

519

381

138

Small

7

5

6

7

12

Number of IPO

2

95

684

735

610

182

3

92

908

898

638

205

I'S

4

78

669

697

422

160

Median Age of the IPO

2

9

7

9

11

20

3

8

7

10

13

15

4

17

7

9

15

23

on Information Available after the First Day of Trading

Big

35

208

236

152

49

Big

38

8

9

45

49

: Fama French Portfolios Sorted based on

Small

473

521

409

305

199

Small

5

5

7

7

11

Number of IPO

2

595

641

442

358

211

Median

2

6

8

11

13

24

3

841

735

427

261

140

I'S

4

953

470

262

180

90

Age of the IPO

3

8

9

12

13

38

4

8

13

14

24

42

Big

349

127

124

68

58

Big

8

26

25

51

61

Small

0.49%

5.29%

5.56%

4.08%

1.48%

Small

10.85

7.79

11.33

15.52

30.61

Percentage of Sample

2

1.02%

7.33%

7.88%

6.54%

1.95%

3

0.99%

9.73%

9.62%

6.84%

2.20%

4

0.84%

7.17%

7.47%

4.52%

1.71%

Average Age of the IPO

2

16.12

11.50

17.22

22.20

34.02

3

15.73

13.24

18.48

28.15

32.44

4

27.55

14.96

19.61

28.59

35.04

Big

0.38%

2.23%

2.53%

1.63%

0.53%

Big

43.11

22.22

27.12

51.51

55.51

Information Available One Year After the IPO

Small

5.07%

5.58%

4.38%

3.27%

2.13%

Small

7.85

9.16

12.37

17.61

23.45

Percentage of Sample

2

6.38%

6.87%

4.74%

3.84%

2.26%

3

9.01%

7.88%

4.58%

2.80%

1.50%

4

10.21%

5.04%

2.81%

1.93%

0.96%

Average Age of the IPO

2

10.66

13.63

18.95

25.70

37.15

3

14.44

17.24

22.08

29.41

47.06

4

14.83

25.30

27.99

36.61

46.53

Big

3.74%

1.36%

1.33%

0.73%

0.62%

Big

21.29

37.37

45.73

54.51

64.53

37

Table 1.5: First Day of Trading Return and Survival Analysis

The sample is composed of 9,400 identified IPOs from 1935 through 2002, grouped in 12 age categories. Panel A and B present the number of delisted and merged firms as well as delisted, merged, and survival percentages after three and five years respectively. Panel C presents the average, median, 25th percentile, 75th percentile, maximum, and standard deviation for the first day of trading return per age category, the return is computed using the low and closing prices recorded in CRSP.

Panel A: Firm Survival 3 Years After IPO

Firm Initial # Delisted Merged Delisted Merged Survival

Age

0

1

(2-3)

(4-5)

(6-8)

(9-12)

(13-18)

(19-27)

(28-40)

(41-55)

(56-75)

75+

Total

of IPO's

207

578

1290

1175

1332

1104

1013

733

551

450

430

537

9400

Firms

26

104

212

130

109

71

56

30

15

10

4

12

779

Firms

14

54

124

141

138

110

87

66

50

31

34

40

889

%

12.56%

17.99%

16.43%

11.06%

8.18%

6.43%

5.53%

4.09%

2.72%

2.22%

0.93%

2.23%

8.29%

%

6.76%

9.34%

9.61%

12.00%

10.36%

9.96%

8.59%

9.00%

9.07%

6.89%

7.91%

7.45%

9.46%

%

87.44%

82.01%

83.57%

88.94%

91.82%

93.57%

94.47%

95.91%

97.28%

97.78%

99.07%

97.77%

91.71%

Panel B: Firm Survival 5 Years After IPO

Firm Initial # Delisted Merged Delisted Merged Survival

Age

0

1

(2-3)

(4-5)

(6-8)

(9-12)

(13-18)

(19-27)

(28-40)

(41-55)

(56-75)

75+

Total

of IPO's

207

578

1290

1175

1332

1104

1013

733

551

450

430

537

9400

Firms

51

184

357

217

197

134

104

60

29

31

19

27

1410

Firms

29

105

211

236

262

203

175

130

85

64

68

75

1643

%

24.64%

31.83%

27.67%

18.47%

14.79%

12.14%

10.27%

8.19%

5.26%

6.89%

4.42%

5.03%

15.00%

%

14.01%

18.17%

16.36%

20.09%

19.67%

18.39%

17.28%

17.74%

15.43%

14.22%

15.81%

13.97%

17.48%

%

75.36%

68.17%

72.33%

81.53%

85.21%

87.86%

89.73%

91.81%

94.74%

93.11%

95.58%

94.97%

85.00%

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39

Table 1.6: Cumulative Abnormal Returns by Age Group

The sample is composed of 9,400 identified IPOs from 1935 through 2002, grouped in 12 age categories. Panel A presents the three-year cumulative abnormal returns relative to six value weighted benchmarks: 1) Fama and French size and book-to-market quintiles portfolio; 2) Fama and French 12 industry portfolios; 3) NYSE, Amex and Nasdaq stocks; 4) Nasdaq stocks; 5) NYSE small stocks; and, 6) S&P500 stocks. The left columns present the cumulative abnormal returns and the right columns the corresponding t-statistics which are computed following Ritter's 1991 paper. Panel B presents the five-year cumulative abnormal returns. Panel C and D present three and five year cumulative abnormal returns excluding the firms that ex-post will be delisted.

Panel A: Three Year Cumulative Abnormal Returns

Firm Age

0 1

(2-3)

(4-5) (6-8)

0-12) (13-18)

(19-27)

(28-40)

(41-55)

(56-75) 75+

All IPO's

FF5x5

-54.3% -49.4%

-40.0% -30.4%

-28.3%

-13.2%

-15.5%

-9.6%

-7.8%

-13.1%

-9.1% -6.2%

-23.2%

IND12

-53.8%

-54.5%

-37.7%

-22.3%

-24.9%

-13.4%

-13.3%

-8.9%

-3.0%

-9.0%

-4.8% -2.1%

-20.5%

NY/A/N

-50.8%

-47.6%

-34.2%

-19.7%

-19.7%

-6.2%

-8.5%

-3.0%

0.9%

-6.2%

-1.8%

0.7%

-15.9%

Nasdaq

-53.9%

-52.9%

-34.5%

-22.0%

-24.8%

-13.6%

-12.5%

-6.4%

-7.0%

-12.8%

-0.1%

-3.9%

-21.1%

Small

-42.3% -41.7%

-37.1%

-26.1% -23.8%

-9.5%

-10.6%

-4.0%

-6.2%

-8.5%

-4.1%

0.8%

-18.5%

S&P500 -54.6%

-51.5%

-37.3%

-22.2%

-22.2%

-8.8%

-11.3%

-5.6%

-0.9%

-8.6%

-4.0% -1.7%

-18.6%

FF5x5

-4.6

-7.1

-8.2

-5.9 -7.2

-3.4

-4.0

-2.4

-2.2

-3.5

-2.5

-1.9

-16.7

IND12 NY/A/N

-4.5 -7.7

-7.8

-4.3 -6.4

-3.4

-3.4

-2.2

-0.8

-2.4

-1.3 -0.6

-14.7

-4.2

-6.8

-7.0

-3.8 -5.0

-1.6

-2.2

-0.7

0.3

-1.6

-0.5 0.2

-11.4

Nasdaq -4.1

-7.0

-6.9 -4.2

-6.1

-3.2

-2.9

-1.3

-1.3

-2.2

0.0

-0.9

-13.3

Small -3.6

-6.0

-7.6

-5.1 -6.1

-2.4

-2.7

-1.0

-1.7

-2.2

-1.1 0.2

-13.3

S&P500 -4.5

-7.3

-7.6

-4.3 -5.6

-2.2

-2.9 -1.4

-0.2

-2.2

-1.1

-0.5

-13.2

Panel B: Five Year Cumulative Abnormal Returns

Firm Age 0

1

(2-3) (4-5)

(6-8)

(9-12)

(13-18) (19-27)

(28-40)

(41-55)

(56-75)

75+

All IPO's

FF5x5

-75.5%

-71.8%

-50.6%

-23.1%

-27.9%

-12.5%

-16.1%

-16.2%

-14.5%

-19.3%

-23.8%

-20.1%

-28.1%

IND12

-70.1%

-70.5%

-42.6%

-11.8%

-22.0%

-8.5%

-6.4% -8.7%

-5.2%

-9.6%

-15.9%

-9.9% -20.5%

NY/A/N -63.1%

-60.4%

-36.0%

-2.0% -10.5%

3.9%

1.9% -0.6%

1.4%

-5.2%

-9.7%

-6.2%

-11.9%

Nasdaq -70.4%

-72.4%

-39.7%

-8.3%

-18.8% -7.1%

-4.6%

-5.9%

-11.0%

-19.5%

-16.8%

-20.2%

-20.7%

Small S&P500 -51.0% -56.1%

^2.8% -13.4%

-17.5%

-3.8%

-6.1% -5.4%

-10.5%

-12.6%

-16.4%

-8.5% -18.4%

-69.1%

-65.3%

-40.0% -4.9%

-14.0%

0.1%

-1.8%

-4.4%

-1.5%

-9.0%

-13.1% -9.7%

-15.6%

FF5x5 -5.0

-7.3

-7.1

-3.3 -4.9

-2.2

-3.3

-2.8 -2.9

-3.3

-4.0

-4.2 -14.4

IND12 NY/A/N

-4.8 -7.3

-6.1

-1.8 -4.0

-1.8

-2.1

-1.8

-1.3

-1.6

-2.5 -2.2

-11.3

-4.4

-6.3

-5.2 -0.7

-2.1

0.4

-0.4

-0.4

0.0

-0.9

-1.5

-1.6 -7.1

Nasdaq -4.3

-6.7

-5.4

-1.5

-3.5

-1.6

-1.5

-1.3

-1.6

-2.0

-1.7

-3.1

-9.9

Small

-3.8

-5.8

-6.0 -1.9 -3.1

-0.8

-1.6 -1.0

-2.0

-1.8

-2.4

-1.8

-9.6

S&P500 -4.7

-6.8

-5.7

-1.1 -2.7

-0.3

-1.1

-1.0

-0.5

-1.6

-2.2

-2.3

-8.9

40 Panel C: Three Year Cumulative Abnormal Returns (Excluding Firms that will be Delisted)

Firm Age 0 1

(2-3) (4-5)

(6-8) (9-12) (13-18) (19-27)

(28-40)

(41-55) (56-75) 75+

FF5x5 -34.9%

-5.1%

5.0% -3.6%

-4.8% 1.9%

-3.7%

1.9%

-1.1% -3.6% -1.8% 1.7%

IND12 -34.2%

-11.0%

6.3% 3.6%

-1.8% 1.7%

-1.6% 2.6%

3.3%

0.9% 2.2%

5.3%

NY/A/N -31.4%

-3.3%

9.9% 6.4% 3.7%

9.1% 3.5% 8.5% 7.2%

3.8% 5.6% 8.6%

Nasdaq -32.7%

-5.5% 12.4%

5.8% -0.6% 3.4% 1.6% 8.3%

2.7%

3.5% 12.0%

6.9%

Small -24.2%

1.9%

5.6% -0.4% -1.4%

4.5% 0.7% 6.7%

-0.2% 0.4% 2.8%

8.2%

S&P500 -35.1%

-7.1%

6.9% 4.0% 1.4% 6.7% 0.8%

5.9% 5.4%

1.5% 3.4%

6.3%

FF5x5 -2.8 -0.7

1.0

-0.8 -1.2

0.5

-1.0 0.5

-0.3 -1.0 -0.5

0.5

IND12 -2.8

-1.6

1.3

0.8

-0.5

0.5 -0.4

0.7

0.9

0.2

0.6

1.6

NY/A/N -2.6

-0.5

2.0 1.4

0.9

2.3 0.9

2.2

2.0

1.0 1.6

2.6

Nasdaq Small -2.4

-0.7

2.5 1.2

-0.2

0.8 0.4

1.8

0.5

0.6 2.3

1.6

-2.0 0.3

1.2

-0.1 -0.4

1.2 0.2

1.7

-0.1 0.1 0.8

2.5

S&P500 -2.8

-1.0 1.4

0.8

0.4

1.7

0.2

1.5

1.5

0.4 1.0

1.9

Panel D: Five Year Cumulative Abnormal Returns (Excluding Firms that will be Delisted)

Firm Age 0 1

(2-3) (4-5) (6-8) (9-12) (13-18)

(19-27) (28-40)

(41-55)

(56-75) 75+

FF5x5 -37.3%

-5.8% 9.9%

14.9% 4.9%

10.7% 2.4%

0.0% -5.5% -6.6%

-9.8%

-9.4%

IND12 -31.5%

-5.1% 15.3% 24.8%

10.2%

14.0% 11.7%

7.2%

3.3% 3.3%

-2.4%

0.2%

NY/A/N -24.7%

5.2% 22.4% 35.2%

22.3% 26.6% 20.3%

15.3%

9.9% 7.7%

4.2%

4.3%

Nasdaq -28.7%

-1.5%

21.8% 30.7%

15.0%

17.9% 16.9%

15.2% 2.5% 1.7%

6.2%

-5.6%

Small -14.0%

8.6% 14.8% 23.6%

14.1% 17.9% 11.7%

9.7%

-2.4%

-0.9% -3.4%

1.6%

S&P500 -30.4%

0.4% 18.6% 32.3%

18.9% 22.9% 16.7%

11.6% 7.1% 4.1%

0.9%

1.0%

FF5x5 -3.0 -0.9 1.9 2.2

1.3

2.2 0.3

0.2

-1.0 -0.5

-1.3

-2.0

IND12 -2.8 -1.0 2.5 3.7

2.1 2.4 1.4

1.1

0.6

1.2

0.1

0.0

NY/A/N -2.4 0.1

3.6 5.0

4.2 4.7

3.2

2.7

1.9

1.9

1.3

0.7

Nasdaq Small -2.3 -0.4 3.5 4.3

2.7 2.8

2.3

1.9

0.3

1.0

1.3

-0.9

-1.8 0.6

2.6 3.7

2.9 3.3 1.9

1.9

-0.3 0.7

0.1

0.4

S&P500 -2.7 -0.5 3.0

4.5

3.5

4.0 2.4

2.0

1.4

1.2

0.6

-0.1

Pan

el E

: Thr

ee Y

ear

Cum

ulat

ive

Abn

orm

al R

etur

ns in

exc

ess

to N

YS

E, A

mex

, & N

asda

q

Fam

a Fr

ench

Por

tfol

ios

Sor

ted

bas

ed o

n In

form

atio

n A

vaila

ble

afte

r th

e Fi

rst

Day

of T

radi

ng

Low

B/M

kt

2 3 4 H

igh

B/M

kt

Low

B/M

kt

2 3 4 H

igh

B/M

kt

Low

B/M

kt

2 3 4 H

igh

B/M

kt

Sm

all

-66.

1%

-31.

3%

-56.

7%

-51.

0%

-6.2

%

Sm

all

-1.7

%

5.6%

-2

8.0%

-3

.0%

35

.5%

Sm

all

-34.

1%

6.3%

-2

7.9%

3.

3%

5.6%

Yo

un

g F

irm

s (

0 to

8 y

ears

)

2 -6

3.2%

-2

9.6%

-3

8.8%

-3

4.8%

-3

1.2%

3 -3

9.4%

-1

7.0%

-1

2.6%

-4

1.8%

-3

0.3%

4 -5

.9%

-1

6.1%

-1

7.3%

-3

4.2%

-1

9.7%

Mat

ure

Fir

ms

( 9 t

o 40

yea

rs )

2

-52.

3%

-3.8

%

0.2%

-9

.6%

-4

1.9%

3 -4

2.8%

1.

4%

1.9%

-1

5.5%

-3

1.0%

4 -4

3.7%

11

.4%

-1

3.0%

4.

3%

21.4

%

Old

Fir

ms

(41

+ye

ars

)

2 -5

.3%

0.

4%

-23.

8%

-1.8

%

-3.9

%

3 -4

8.8%

16

.0%

-1

3.6%

0.

7%

-29.

0%

4 -8

.7%

12

.7%

-6

.4%

2.

2%

-12.

4%

Big

-4

8.2%

-7

1.6%

-3

1.9%

-8

5.5%

-8

2.6%

Big

-4

0.9%

-3

3.3%

-2

.9%

-5

.6%

8.

6%

Big

-1

4.8%

0.

0%

20.0

%

-5.9

%

19.3

%

Sm

all

-1.9

2 -3

.26

-4.8

3 -4

.14

-0.2

9

Sm

all

-0.0

7 0.

51

-2.5

1 -0

.28

1.91

Sm

all

-0.2

6 0.

14

-1.4

8 0.

24

0.43

2 -3

.35

-3.5

2 -4

.55

-3.8

3 -1

.63

2 -3

.31

-0.5

0 0.

02

-1.4

8 -3

.16

2 -0

.21

0.02

-2

.84

-0.2

3 -0

.33

T-S

tatis

tics

3 -2

.09

-2.3

8 -1

.21

-4.7

4 -1

.82

T-S

tatis

tics

3 -2

.32

0.20

0.

33

-2.4

6 -2

.84

T-S

tatls

tics

3 -1

.62

1.56

-1

.73

0.11

-2

.38

4 -0

.20

-1.8

7 -1

.89

-3.0

6 -1

.22

4 -2

.83

1.11

-1

.74

0.47

1.

60

4 -0

.60

1.56

-0

.91

0.32

-0

.96

Big

-1

.70

-3.3

2 -1

.53

-2.6

9 -1

.46

Big

-1

.69

-1.8

3 -0

.24

-0.4

1 0.

48

Big

-1

.07

0.00

2.

26

-0.7

9 1.

72

Sm

all

26

342

311

214

62

Sm

all

21

140

169

123

32

Sm

all

2 9 30

44

45

1 2 45

40

6 35

1 26

0 56

I 2 39

25

0 28

4 23

2 65

I 2 9 31

95

11

4 59

slum

ber

of I

PO

' 3 46

49

5 36

6 22

2 60

dum

ber

of IP

O"

3 37

341

393

239

80

dum

ber

of

IPO

' 3 8 58

10

9 15

5 60

s 4 26

36

9 29

2 15

1 47

s 4 33

19

6 23

3 14

4 48

s 4 19

66

10

7 10

7 51

Big

10

10

0 95

33

9 Big

8 55

62

32

10

Big

15

33

51

74

23

-fc.

Pan

el F

: Thr

ee Y

ear

Cum

ulat

ive

Abn

orm

al R

etur

ns in

exc

ess

to N

YS

E, A

mex

, & N

asda

q

Fam

a Fr

ench

Por

tfol

ios

Sor

ted

bas

ed o

n In

form

atio

n A

vaila

ble

Sev

en M

onth

s af

ter

the

Firs

t D

ay o

f Tra

ding

Lo

w B

/Mkt

2 3 4

Hig

h B

/Mkt

Low

B/M

kt

2 3 4 H

igh

B/M

kt

Low

B/M

kt

2 3 4 H

igh

B/M

kt

Sm

all

-67.

4%

-61.

9%

-48.

2%

-52.

4%

-37.

5%

Sm

all

-17.

8%

-20.

0%

-23.

0%

-23.

3%

-22.

2%

Sm

all

-51.

0%

-71.

9%

-42.

3%

16.0

%

-10.

2%

Yo

un

g F

irm

s ( 0

to

8 ye

ars

) 2

-36.

8%

-38.

5%

-36.

4%

-41.

0%

-21.

8%

3 -2

2.6%

-1

6.4%

-2

1.3%

-4

9.5%

-7

4.4%

4 -3

.1%

-2

1.4%

-6

.8%

3.

7%

-31.

9%

Mat

ure

Fir

ms

( 9

to 4

0 ye

ars

) 2

-35.

7%

-14.

0%

-12.

3%

-2.1

%

-21.

6%

3 4.

0%

-5.5

%

6.5%

-2

1.7%

-2

8.8%

4 7.

7%

2.9%

15

.7%

18

.4%

1.

7%

Old

Fir

ms

(41

+ye

ars

)

2 -1

3.4%

17

.9%

-1

4.4%

-1

5.8%

-9

.1%

3 -3

0.5%

-1

2.6%

-6

.8%

2.

8%

-12.

5%

4 12

.7%

18

.1%

5.

0%

-6.6

%

-9.3

%

Big

32

.9%

-1

4.7%

-1

2.1%

-3

9.1%

-7

1.6%

Big

1.

2%

22.5

%

1.0%

-3

.5%

-7

.8%

Big

0.

5%

2.1%

12

.6%

5.

5%

18.3

%

Sm

all

-4.8

0 -6

.04

-3.1

0 -3

.33

-1.9

9

Sm

all

-1.1

8 -1

.69

-2.0

2 -1

.97

-1.3

7

Sm

all

-0.9

4 -2

.55

-1.8

2 1.

17

-0.7

7

2 -3

.43

-4.8

4 -3

.50

-3.9

4 -1

.10

2 -3

.08

-2.0

1 -1

.65

-0.2

7 -1

.38

2 -0

.60

1.15

-1

.69

-1.8

6 -0

.90

T-S

tatis

tics

3 -2

.55

-2.2

4 -2

.15

-4.4

9 -4

.42

T-S

tatis

tics

3 0.

45

-0.8

0 0.

92

-2.7

0 -2

.23

T-S

tatis

tics

3 -2

.40

-1.4

0 -0

.81

0.38

-1

.10

4 -0

.31

-2.3

0 -0

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0.29

-1

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4 0.

89

0.33

1.

90

1.81

0.

11

4 1.26

2.

00

0.71

-0

.79

-0.8

1

Big

1.

77

-0.7

2 -0

.44

-1.4

9 -1

.43

Big

0.

07

1.14

0.

07

-0.2

2 -0

.41

Big

0.

05

0.22

1.

31

0.68

1.

62

Sm

all

191

369

265

170

58

Sm

all

74

155

148

116

43

Sm

all

3 15

28

45

38

Num

ber

of

IPO

' 2 225

396

230

173

51

3 277

439

239

156

44

Num

ber

of

IPO

" 2 130

260

238

163

49

3 192

329

257

165

52

Num

ber

of

IPO

' 2 18

42

71

95

78

3 42

78

94

115

60

s 4 255

277

201

101

24

s 4 19

8 19

9 16

5 10

6 35

s 4 46

66

89

81

54

Big

10

6 79

44

22

6 Big

59

45

45

28

15

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37

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44

Table 1.7: Cumulative Excess Buy and Hold Returns by Age Group

The sample is composed of 9,400 identified IPOs from 1935 through 2002, grouped in 12 age categories. Panel A presents the three-year cumulative excess buy and hold return (annualized) relative to six value weighted benchmarks: 1) Fama and French size and book-to-market quintiles portfolio; 2) Fama and French 12 industry portfolios; 3) NYSE, Amex and Nasdaq stocks; 4) Nasdaq stocks; 5) NYSE small stocks; and, 6) S&P500 stocks. The left columns present the cumulative abnormal returns and the right columns the corresponding t-statistics. Panel B presents the five-year cumulative excess buy and hold returns. Panel C and D present three and five yer cumulative excess buy and hold returns excluding all firms that will be delisted.

Panel A: Three Year Annualized Cumulative Excess Buy and Hold Returns

Firm Age

0 1 (2-3) (4-5) (6-8) (9-12) (13-18) (19-27)

(28-40)

(41-55) (56-75) 75+ All IPO's

FF5x5 -13.9%

-13.1% -7.6% -8.5% -6.0%

-3.1% -2.0% -2.1% -2.1%

-1.2% -1.2% -0.6% -4.9%

IND12 -14.5%

-15.5%

-9.6% -7.5% -5.9%

-4.3% -3.2% -1.4%

-1.0% -0.6% -0.6% 0.3%

-5.2%

NY/A/N

-12.9%

-13.0% -7.1% -6.7%

-4.2% -1.7% -0.7% -0.2%

0.1% 0.4%

1.1% 1.8%

-3.4%

Nasdaq^ -12.8% -13.8%

-6.9% -6.9%

-6.0% -3.6% -2.1%

-0.3%

-1.9% 0.5% 3.6% 1.5%

-4.5%

Small -11.0% -10.4%

-5.3% -7.0%

-5.2% -2.4% -0.6% -0.7%

-1.7%

-0.1% 0.0% 1.3%

-3.5%

S&P500

-14.0% -14.2%

-8.6% -7.6%

-4.9%

-2.6% -1.7% -1.0% -0.4% -0.4%

0.5% 1.1%

-4.3%

FF5x5

-4.97

-7.95 -4.69

-6.99

-4.78

-2.55 -0.98 -1.67

-2.07

-1.03

-1.08

-0.60

-10.85

IND12

-6.16

-11.16

-6.91 -6.05

-4.85

-3.78 -2.06

-1.16

-1.01

-0.54

-0.55 0.27

-12.88

NY/A/N

-4.89

-8.00 -4.12

-5.26

-3.22

-1.42

-0.38 -0.17

0.13 0.31 0.89

1.69

-7.48

Nasdaq

-4.29 -8.47

-3.96 -5.41

-4.82

-2.79 -1.21 -0.20

-1.44

0.30

1.76 1.04

-9.21

Small

-3.95

-5.55 -2.54 -5.46

-4.08

-1.96 -0.27

-0.57

-1.51

-0.11 0.00

1.30

-6.98

S&P500

-5.49 -9.04 -5.35

-6.04

-3.75

-2.17

-0.92

-0.82

-0.38

-0.30 0.37

1.05

-9.71

Panel B: Five Year Annualized Cumulative Excess Buy and Hold Returns

Firm Age

0 1

(2-3) (4-5) (6-8) (9-12)

(13-18) (19-27) (28-40) (41-55) (56-75) 75+

FF5x5 -12.7%

-11.6% -7.1%

-5.8% -4.4% -1.8%

-3.1% -0.8% -1.9% -2.1% -2.7% -1.8%

IND12 -12.9%

-12.1%

-7.5% -5.1%

-3.8% -2.4%

-2.8% -0.9% -0.5% -1.1% -1.9% -0.7%

NY/A/N -11.5% -10.3%

-6.1% -3.4%

-2.2% -0.2%

-1.0% 0.9% 0.6%

-0.1% -0.6% 0.5%

Nasdaq -11.3% -12.0%

-5.8% -4.2%

-3.9% -2.0% -2.3% 1.0%

-1.4% -1.9%

-0.1% -0.9%

Small -8.6% -8.9% -6.0% -4.1% -3.3%

-1.6% -2.4% 1.2%

-2.0% -1.5% -2.2% -0.5%

S&P500 -12.6% -11.0%

-7.0% -4.1%

-2.8%

-0.8%

-1.6%

0.0% 0.3%

-0.6%

-1.0% 0.1%

FF5x5

-7.56 -12.14

-6.84

-6.61

-4.21

-1.76

-2.79

-0.33 -2.09

-2.38

-3.43

-2.59

IND12

-7.88

-13.35

-7.75

-6.13

-3.69

-2.49 -3.03

-0.57

-0.51

-1.30 -2.54

-0.96

NY/A/N

-6.67

-9.90 -5.61

-3.69

-2.14

-0.17

-0.97

0.41

0.56 -0.12 -0.64

0.67

Nasdaq

-5.05 -12.32

-5.08

-4.49

-3.79

-2.01

-2.12

0.42 -1.09

-1.59

-0.10 -0.87

Small

-3.49 -7.79

-4.78 -4.21

-2.96

-1.61 -2.08

0.41

-2.29

-1.78 -2.97

-0.71

S&P500

-7.74 -10.72

-6.81

-4.48

-2.68

-0.82

-1.55

0.00 0.26

-0.60

-1.06

0.13 All IPO's -4.2% -3.9% -2.3% -3.5% -3.1% -2.9% -11.52 -12.20 -6.41 -9.05 -7.50 -8.43

45 Panel C: Three Year Annualized Cumulative Excess Buy and Hold Returns (Excluding Delisting Firms)

Firm Age

0 1

(2-3)

(4-5)

(6-8) (9-12)

(13-18) (19-27)

(28-40) (41-55) (56-75)

75+

FF5x5 -9.6% -4.4%

0.9%

-3.8% -2.2%

-0.1% 0.5%

-0.1%

-0.9% 0.3%

-0.1% 0.8%

IND12 -10.7%

-7.1%

-1.3% -2.5%

-2.1%

-1.3% -0.7%

0.5% 0.2% 1.0% 0.5%

1.6%

NY/A/N

-9.0% -4.2%

1.5% -1.8%

-0.3%

1.4% 1.8%

1.8%

1.3% 2.0% 2.3% 3.2%

Nasdaq

-8.2%

-4.4% 2.1%

-1.6%

-2.1%

-0.3% 0.7%

2.2% -0.2% 3.0% 5.5% 3.4%

Small -6.9%

-1.3% 3.4%

-2.3%

-1.4%

0.5% 1.9%

1.3%

-0.6% 1.3% 1.1% 2.7%

S&P500 -10.3%

-5.5% -0.1% -2.8%

-0.9%

0.5% 0.9%

1.0% 0.8% 1.2% 1.6%

2.5%

FF5x5 -2.9

-2.1 0.5

-2.9

-1.7

-0.1 0.2

-0.1

-0.9 0.3

-0.1 0.8

IND12

-3.9

-4.1 -0.8 -1.8

-1.6

-1.1 -0.4

0.4

0.1 0.8 0.5 1.7

NY/A/N -3.0

-2.1 0.7

-1.3

-0.2

1.0 0.9

1.4

1.2 1.6 1.8

3.1

Nasdaq -2.3

-2.1

1.0 -1.1

-1.6 -0.2 0.4

1.5 -0.2 1.7 2.6 2.4

Small ! -2.1

-0.6 1.3

-1.7

-1.0 0.4

0.8

0.9

-0.5 1.1 0.9 2.7

S&P500

-3.5

-2.8 0.0

-2.0

-0.7

0.4

0.5

0.8

0.7 1.0

1.3 2.4

Panel D: Five Year Annualized Cumulative Excess Buy and Hold Returns (Excluding Delisting Firms)

Firm Age 0 1

(2-3) (4-5)

(6-8) (9-12) (13-18) (19-27) (28-40) (41-55) (56-75) 75+

FF5x5 -8.9% -5.8% -1.5% -2.2%

-1.6%

0.3% -1.5% 0.7%

-1.1% -0.9% -1.9%

-0.9%

IND12

-9.1% -6.3% -2.0% -1.5% -1.0%

-0.3% -1.1% 0.4% 0.3% 0.1%

-1.1% 0.2%

NY/A/N -7.6% -4.2% -0.5% 0.2%

0.6%

2.0% 0.7%

2.3% 1.4%

1.1% 0.3% 1.4%

Nasdaq -7.0%

-5.8% 0.1%

-0.5%

-1.0% 0.3%

-0.4%

2.7% -0.2% 0.0% 1.1% 0.3%

Small -4.4% -2.8% -0.4% -0.5%

-0.5% 0.5%

-0.8%

2.6% -1.2% -0.4% -1.4%

0.3%

S&P500 -8.7%

-5.0% -1.5% -0.5%

0.0% 1.3% 0.1% 1.4%

1.1% 0.6%

-0.2%

1.0%

FF5x5 -4.5 -4.9 -1.2 -2.3 -1.4

0.3 -1.2

0.3 -1.2 -1.0 -2.4

-1.3

IND12 -4.8 -5.7 -1.7 -1.7

-0.9 -0.3 -1.2

0.3 0.3 0.1

-1.5 0.3

NY/A/N -3.7

-3.3 -0.4 0.2

0.5 1.8 0.6

1.0 1.3 1.2 0.3 1.9

Nasdaq -2.6

-4.8 0.1

-0.5

-0.9 0.2

-0.3

1.1 -0.2 0.0

0.8 0.3

Small : -1.5 -2.0 -0.3 -0.5 -0.4

0.5 -0.6

0.9 -1.4 -0.4 -1.9 0.4

S&P500 -4.6 -4.0 -1.2

-0.5 0.0 1.2 0.1 0.7

0.9 0.6

-0.2

1.3

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Age Group

-*— NY/Am/Nas - Nasdaq - * — Small —•—S&P500

Figure 1.2: 3-Year Cumulative Abnormal Returns by Age Group

4% -,

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1 (2-3) (4-5) (6-8) (9-12) (13-18) (19-27) (28-40) (41-55) (56-75) 75+

Age Group

—»-FF5x5 ~*-IND12 -*~NY/A/N * Nasdaq - * -Sma l l -•—S&P500

Figure 1.3: 3-Year Buy and Hold Abnormal Returns by Age Group

1.1

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Age Group

—•— FF5x5 * - I N D 1 2 - * - - NY/AM Nasdaq - * — Small - •—S&P500

Figure 1.4: 3-Year Wealth Relatives by Age Group

48

Table 1.9: 3 Year Holding Period Return Distribution

The sample is composed of 9,400 identified IPOs from 1935 through 2002, grouped in 12 age categories. Panel A presents three-year holding period return percentile breakpoints, from bottom 2nd and 5th percentile points (the biggest losers) to top 95th and 98th % the star performers. The returns are measured from the first aftermarket closing price to the earlier of the three year anniversary or its CRSP delisting. Panel B presents the monthly geometric mean return breakpoints.

Pane A: 3 Year Holding Period Return Distribution Percentile Breakpoints

Firm Age 2% Pctp 5% Pctp 10% Pctp 20% Pctp 40% Pctp 60% Pctp 80% Pctp 90% Pctp 95% Pctp 98% Pctp 0 1 (2-3) (4-5) (6-8) (9-12) (13-18) (19-27) (28-40) (41-55) (56-75) 75+

-92% -96% -98% -97%

-95% -94% -93% -88% -85% -91% -81% -76%

-89% -90% -94% -93%

-91% -86% -86% -80% -74% -76% -76% -63%

-86% -82% -87% -86%

-83% -79% -76% -71% -63% -62% -61% -52%

-77% -70% -77% -75% -69% -64% -60% -55% -42% -34% -39% -25%

-46% -40% -49% -47%

-41% -28% -32% -22% -5% 2% -3% 9%

-6% - 1 % -5% 4%

7% 27% 6%

24% 38% 42% 42% 48%

44% 94%

69% 92%

97% 120% 86% 107% 88% 102% 97% 104%

88% 203% 211% 203%

207% 263% 192% 198% 160% 188% 161%

166%

218% 322% 401% 350%

369% 407% 330% 341% 246% 253% 215% 242%

871% 557% 737% 587%

584% 621% 694% 530% 389% 395% 377% 309%

Pane B: 3 Year Holding Period Return Distribution (monthly geometric mean)

Firm Age 2% Pctp 5% Pctp 10% Pctp 20% Pctp 40% Pctp 60% Pctp 80% Pctp 90% Pctp 95% Pctp 98% Pctp 0 1 (2-3) (4-5) (6-8) (9-12) (13-18) (19-27) (28-40) (41-55) (56-75) 75+

-6.74% -8.48%

-10.27% -9.11% -8.05% -7.41% -7.24% -5.74% -5.18% -6.35% -4.54% -3.85%

-5.84% -6.22% -7.43% -7.02% -6.48% -5.31% -5.31% -4.31% -3.65% -3.85% -3.90%

-2.72%

-5.26% -4.68% -5.57% -5.29% -4.73% -4.20% -3.90% -3.35% -2.75%

-2.63% -2.58% -2.01%

-3.97%

-3.29% -3.98% -3.80% -3.19%

-2.81% -2.49% -2.20% -1.53% -1.14% -1.35% -0.79%

-1.70% -1.39% -1.85% -1.75% -1.43% -0.91% -1.06% -0.67% -0.14% 0.04% -0.08% 0.25%

-0.18% -0.04% -0.15% 0.12% 0.18% 0.67% 0.15% 0.60% 0.90% 0.97% 0.97% 1.10%

1.01% 1.85% 1.47% 1.82% 1.90% 2.21% 1.74% 2.04% 1.77% 1.97% 1.90% 2.00%

1.76% 3.12% 3.20% 3.13% 3.17% 3.64% 3.02% 3.08% 2.69% 2.99% 2.70% 2.76%

3.26% 4.08% 4.58% 4.26% 4.38% 4.61% 4.14% 4.21% 3.51% 3.56% 3.24% 3.48%

6.52% 5.37%

6.08% 5.50% 5.49% 5.64% 5.92% 5.24% 4.51% 4.54%

4.43% 3.99%

Monthly Return Distribution per Age Group

2% Pctp

5% Pctp

10% Pctp

20% Pctp

40% Pctp

60% Pctp

80% Pctp

90% Pctp

95% Pctp

98% Pctp

Age Group

Figure 1.5: Monthly Return Distribution by Age Group

Tab

le 1

.10:

Dis

trib

utio

n of

Ini

tial P

ublic

Off

erin

gs p

er Y

ear

Coh

orts

and

Lis

ting

Exc

hang

e M

arke

t

The

sam

ple

is 9

,400

iden

tifie

d IP

Os

from

193

5 th

roug

h 20

02. P

anel

A, s

umm

ariz

es th

e nu

mbe

r of

IPO

s re

aliz

ed d

urin

g fi

ve y

ear

perio

ds a

nd

clas

sifie

d by

the

listin

g st

ock

exch

ange

. Pan

el B

, pre

sent

s th

e di

strib

utio

n as

a p

erce

ntag

e of

tota

l IPO

s re

aliz

ed d

urin

g th

e co

ncur

rent

per

iod.

Age

Gro

up \

Yea

r

Yo

un

g

Mat

ure

Old

NY

SE

Am

ex

Nas

daq

NY

SE

Am

ex

Nas

daq

NY

SE

Am

ex

Nas

daq

Oth

er e

xch

ang

es

To

tal

•35

- "4

0

10 - -

28 - -

17 - - .

55

Pan

el A

•41

- '4

5 6 - -11

- -17

- - . 34

,: N

umbe

r of

Initi

al P

ublic

Off

erin

g b

y A

ge

Gro

up

, Sto

ck E

xcha

nge,

and

Fiv

e-ye

ar P

erio

ds

•46

- '5

0 3 - . 59

- . 53

- . . 11

5

"51

- '55

10 - -

39 - -

44 - . -

93

•56

-'6

0

14 - -

69 - -

54 - . .

137

•61

- '6

5

19

44 -

63

52 -

68

14 - .

260

•66

- 7

0

19

55 -

51

101 .

90

60 . .

376

71

- 7

5 2 32

72

11

45

58

25

16

23 8

292

76

-'8

0 3 2

159 - 2

122 1 1

28 7

325

•81

- '8

5 4 12

720 15

11

482 10

4

160 1

1,41

9

•86

- "9

0

18

38

700 40

36

349 30

16

203 -

1,43

0

"91

- '95

61

21

1,12

8

115 17

754 95

6

170 .

2,36

7

•96

- "0

0

84

37

1,23

9

112 21

660 84

2 94 .

2,33

3

•01

- '02

11 3 52

21 3

44

25 - 5 -

164

To

tal

264

244

4,07

0

634

288

2,46

9

613

119

683 16

9,40

0

Pan

el B

: In

itial

Pub

lic O

ffer

ing

Dis

trib

utio

n b

y A

ge

Gro

up

, Sto

ck E

xch

ang

e, a

nd

Fiv

e-ye

ar P

erio

ds (

Per

cen

tag

e o

f T

ota

l Off

erin

gs p

er P

erio

d)

Ag

e G

roup

\ Y

ear

Yo

un

g

Mat

ure

Old

NY

SE

Am

ex

Nas

daq

NY

SE

Am

ex

Nas

daq

NY

SE

Am

ex

Nas

daq

Oth

er e

xch

ang

es

"35

- "4

0

18%

0%

0%

51%

0%

0%

31%

0%

0%

0%

•41

- '4

5

18%

0%

0%

32%

0%

0%

50%

0%

0%

0%

"46

- '5

0

3%

0%

0%

51%

0%

0%

46%

0%

0%

0%

•51

- '5

5

11%

0%

0%

42%

0%

0%

47%

0%

0%

0%

•56

- *6

0

10%

0%

0%

50%

0%

0%

39%

0%

0%

0%

'61

- '6

5

7%

17%

0%

24%

20%

0%

26%

5%

0%

0%

"66

- 7

0

5%

15%

0%

14%

27%

0%

24%

16%

0%

0%

71

- 7

5

1%

11%

25%

4%

15%

20%

9%

5%

8%

3%

76

- '8

0

1%

1%

49%

0%

1%

38%

0%

0%

9%

2%

•81

- *8

5

0%

1%

51%

1%

1%

34%

1%

0%

11%

0%

•86

- '9

0

1%

3%

49%

3%

3%

24%

2%

1%

14%

0%

"91

-'9

5

3%

1%

48%

5%

1%

32%

4%

0%

7%

0%

•96

- '0

0

4%

2%

53%

5%

1%

28%

4%

0%

4%

0%

"01

- '02

7%

2%

32%

13%

2%

27%

15%

0%

3%

0%

To

tal

3%

3%

43%

7%

3%

26%

7%

1%

7%

0%

Tot

al

100%

10

0%

100%

10

0%

100%

10

0%

100%

10

0%

100%

10

0%

100%

10

0%

100%

10

0%

100%

Tab

le 1

.11:

Cal

enda

r Po

rtfo

lio R

etur

ns f

or I

PO

's b

y A

ge G

roup

and

Lis

ting

Exc

hang

e

The

sam

ple

is 9

,400

iden

tifie

d IP

Os

from

193

5 th

roug

h 20

02. I

POs

that

hav

e go

ne p

ublic

ove

r th

e la

st y

ear

are

grou

ped

in p

ortfo

lios

base

d on

th

eir

age

(you

ng,

mat

ure,

old

) an

d/or

lis

ting

stoc

k ex

chan

ge,

thes

e po

rtfol

ios

are

held

for

12,

36

and

60 m

onth

per

iods

. T

he m

eans

are

co

mpu

ted

with

ove

rlapp

ing

obse

rvat

ions

, the

refo

re t

-sta

tistic

s ar

e co

mpu

ted

with

New

ey-W

est

(198

7) s

tand

ard

erro

rs w

ith a

lag

leng

th o

f one

le

ss th

an th

e ho

ldin

g pe

riod

horiz

on in

mon

ths.

All

the

retu

rns

are

repo

rted

in a

nnua

lized

term

s.

Pan

el A

: H

old

ing

Per

iod

Ret

urn

s (1

93

5 -

2002

)

Yo

un

g

Mat

ure

O

ld

All

Yo

un

g

Mat

ure

O

ld

&

NY

SE

| N

asd

aq &

Am

ex

?! A

ll

13.1

8%

13.5

8%

16.5

3%

15.3

5%

4.20

12

.12%

14

.56%

16

.28%

13

.32%

2.

82

14.2

0%

15.7

7%

19.0

1%

16.3

4%

4.26

All

5.12

5.

57

5.58

4.

20

4.67

3.

65

5.86

6.

58

5.53

Exc

ess

Ret

urn

s (N

YS

E/N

asd

aq/A

mex

) (1

93

5-2

00

2 )

Yo

un

g

Mat

ure

O

ld

All

Yo

un

g

Mat

ure

O

ld

0.12

%

1.82

%

0.08

%

2.53

%

1.77

%

3.48

%

4.31

%

4.26

%

6.21

%

3.30

%

1.28

%

3.74

%

0.04

0.02

0.68

0.93

1.03

1.83

All

1.94

1.

61

1.59

0.

49

2.91

1.

74

° N

asd

aq &

Am

ex

3 A

ll

11.8

9%

12.3

9%

13.6

4%

13.3

0%

6.02

9.

90

10.0

5 12

.03

0.39

%

1.24

%

2.41

%

1.97

%

0.20

1.

15

2.04

2.

36

14.5

6%

15.3

6%

14.9

6%

14.4

5%

4.17

7.

03

7.35

5.

97

3.72

%

4.68

%

4.19

%

3.58

%

0.97

2.

07

2.09

1.

40

13.6

5%

14.6

0%

15.8

2%

14.1

3%

5.65

10

.31

11.5

1 10

.11

2.53

%

3.41

%

4.54

%

2.61

%

1.00

2.

54

3.77

2.

02

| N

YS

E

° N

asd

aq &

Am

ex

S A

ll

11.8

9%

12.5

5%

13.0

2%

13.1

1%

7.27

12

.60

11.6

8 15

.05

13.9

7%

15.4

9%

13.8

2%

14.3

3%

4.51

6.

50

6.58

5.

81

13.5

1%

14.8

9%

14.6

9%

14.3

1%

6.20

10

.25

11.0

9 10

.09

0.85

%

1.77

%

3.87

%

6.03

%

2.81

%

4.37

%

2.24

%

3.64

%

3.99

%

2.26

%

4.39

%

3.50

%

0.43

1.

03

1.05

1.73

2.

25

2.73

1.89

2.

59

1.53

1.

51

2.78

2.

16

| N

YS

E

o

5 N iths 1 8 ths Mon

o

<0

Am

ex

All

NY

SE

Am

ex

All

NY

SE

Am

ex

All

Pan

el B

: H

old

ing

Per

iod

Ret

urn

s (1

93

5 -1

97

0)

Yo

un

g

Mat

ure

O

ld

All

Yo

un

g

Mat

ure

O

ld

All

Exc

ess

Ret

urn

s (N

YS

E/N

asd

aq/A

mex

) (1

93

5 -1

97

0 )

Yo

un

g

Mat

ure

O

ld

All

Yo

un

g

Mat

ure

O

ld

19.2

3%

18.2

6%

19.2

3%

20.8

2%

30.1

7%

22.1

9%

24.1

7%

25.6

9%

22.8

5%

18.9

1%

19.9

4%

21.9

1%

13.2

8%

14.5

9%

14.7

3%

14.9

2%

31.4

6%

23.6

4%

15.5

2%

25.2

1%

18.4

9%

15.9

2%

14.8

8%

16.3

3%

13.4

0%

14.2

5%

14.2

2%

14.4

8%

21.0

8%

19.0

2%

13.5

4%

19.5

3%

15.3

8%

15.2

3%

14.4

3%

15.4

8%

All

4.17

2.72

4.56

4.81

2.96

4.53

6.09

3.01

5.74

4.92

2.75

4.90

9.02

4.71

9.24

10.7

3

3.08

9.41

4.36

2.93

4.48

9.28

3.85

9.44

9.77

3.13

9.80

4.85

3.09

4.98

10.1

4

3.84

9.50

12.2

6

3.17

10.5

7

6.92

%

20.1

4%

10.5

6%

3.16

%

26.0

9%

9.35

%

4.13

%

17.6

4%

6.88

%

6.20

%

12.1

6%

6.86

%

3.98

%

17.5

1%

5.60

%

4.12

%

15.3

1%

5.53

%

6.17

%

14.1

3%

6.88

%

3.43

%

8.42

%

3.62

%

3.93

%

8.98

%

4.23

%

8.15

%

15.6

5%

9.25

%

3.95

%

19.2

5%

5.66

%

4.41

%

15.8

8%

5.83

%

1.88

2.02

2.59

1.24

2.39

2.24

1.61

2.55

2.19

2.31

2.00

2.47

2.92

3.66

3.72

3.34

2.67

3.41

1.92

2.44

2.12

3.48

2.17

3.67

3.45

2.36

3.58

2.54

2.43

2.80

4.54

2.95

4.13

4.75

2.72

4.09

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19

Tab

le 1

.12:

Reg

ress

ion

Coe

ffic

ient

s fo

r In

itial

Pub

lic O

ffer

ings

Thr

ee Y

ear

Ret

urns

The

sam

ple

is 9

,400

iden

tifie

d IP

Os

from

193

5 th

roug

h 20

02. T

he t

able

pre

sent

s th

e co

effi

cien

ts o

f th

e re

gres

sion

: R

etun

ii =

/3 +

Log

(l+

Age

) +

M

arke

tj +

FF5

x5; +

Ind

l2j

+ L

og(M

E)

+ B

M_R

atio

+ E

xcha

nge-

Dum

mie

s +

Ind

ustr

y-D

umm

ies

+ T

ime-

Peri

od-D

umm

ies

+ e

j. W

here

, R

etun

ij is

the

thr

ee-y

ear

raw

ret

urn,

mea

sure

d fr

om t

he f

irst

aft

erm

arke

t cl

osin

g pr

ice

to t

he e

arlie

r of

the

thre

e-ye

ar a

nniv

ersa

ry o

r its

CR

SP d

elis

ting

date

. M

arke

tj is

the

CR

SP v

alue

-wei

ghte

d m

arke

t re

turn

, F

F5x

5 ; a

nd I

ndl2

; are

the

val

ue-w

eigh

ted

mat

chin

g Fa

ma

and

Fren

ch s

ize

and

book

-to

-mar

ket

quin

tile,

and

, tw

elve

ind

ustr

y cl

assi

fica

tion

port

folio

ret

urns

res

pect

ivel

y fo

r th

e sa

me

retu

rn i

nter

val

as t

he d

epen

dant

var

iabl

e.

Log

(ME

) is

the

log

of

the

IPO

's m

arke

t va

lue

afte

r th

e fi

rst

day

of t

radi

ng,

BM

_Rat

io i

s th

e av

erag

e of

the

thr

ee S

IC d

igit

indu

stry

boo

k-to

-m

arke

t ra

tios

assi

gned

to

each

IP

O. E

xcha

nge,

Ind

ustr

y an

d T

ime

Peri

od d

umm

ies

iden

tify

the

listin

g ex

chan

ge,

indu

stry

gro

up a

nd f

ive

year

tim

e pe

riod

res

pect

ivel

y.

Reg

ress

ion

Co

effi

cien

ts f

or

3 ye

ar H

old

ing

Per

iod

IP

O R

etu

rns

Ro

-0.4

346

(8.4

4)

•0.6

077

(7.0

2)

-0.0

158

(0.0

3)

-0.1

508

(0.3

3)

-0.2

665

(0.5

0)

-0.4

746

(9.1

8)

-0.7

029

(8.1

3)

-0.3

056

(0.6

7)

-0.4

077

(0.8

9)

-0.5

425

(1.0

3)

Lo

g A

ge

0.12

47

(7.4

2)

0.10

70

(6.0

7)

0.10

26

(5.5

9)

0.11

52

(6.1

3)

0.10

68

(5.6

6)

0.12

00

(7.2

1)

0.10

20

(5.8

8)

0.09

89

(5.4

7)

0.10

70

(5.7

8)

0.09

57

(5.1

4)

NY

/A/N

0.92

26

(15.

66)

0.95

38

(15.

63)

0.98

04

(15.

88)

0.97

63

(15.

82)

1.01

43

(14.

53)

-0.4

781

(4.8

8)

-0.4

358

(4.4

2)

-0.4

074

(4.1

1)

-0.3

883

(3.8

9)

-0.4

139

(3.8

0)

FF

5x5

0.45

03

(7.5

7)

0.45

11

(7.4

4)

0.44

63

(7.3

4)

0.45

65

(7.4

7)

0.35

94

(5.5

8)

IND

12

0.86

60

(15.

50)

0.86

87

(15.

55)

0.86

66

(15.

51)

0.84

28

(14.

78)

0.91

39

(15.

63)

Lo

g M

E

0.02

55

(1.7

8)

0.02

86

(1.8

3)

0.01

46

(0.9

2)

0.02

55

(1.5

0)

0.04

03

(2.8

4)

0.04

31

(2.7

9)

0.03

24

(2.0

6)

0.05

95

(3.5

2)

BV

ME

0.11

24

(3.2

9)

0.11

07

(3.2

2)

0.14

21

(3.9

6)

0.09

02

(2.4

4)

0.08

49

(2.5

0)

0.08

44

(2.4

7)

0.10

38

(2.9

2)

0.05

05

(1.3

8)

Lis

tin

g E

xch

ang

e

NY

SE

-0.6

070

(1.3

2)

-0.4

701

(1.0

2)

-0.2

514

(0.5

4)

-0.4

169

(0.9

2)

-0.3

258

(0.7

2)

-0.2

116

(0.4

6)

Am

ex

-0.4

211

(0.9

1)

-0.2

822

(0.6

1)

-0.1

223

(0.2

6)

-0.2

525

(0.5

6)

-0.1

560

(0.3

4)

-0.0

935

(0.2

0)

Nas

daq

-0.6

235

(1.3

7)

-0.5

589

(1.2

3)

-0.2

231

(0.4

9)

-0.4

254

(0.9

5)

-0.3

814

(0.8

5)

-0.1

643

(0.3

6)

No

n-D

ur

-0.1

149

(1.3

1)

-0.1

447

(1.6

5)

-0.1

313

(1.5

2)

-0.1

727

(2.0

0)

Ind

ust

ry

B-E

qu

ip

0.22

19

(3.4

7)

0.19

91

(3.1

1)

0.17

80

(2.8

1)

0.13

91

(2.2

0)

Hea

lth

0.17

45

(2.2

5)

0.14

51

(1.8

7)

0.09

87

(1.2

9)

0.04

37

(0.5

7)

Fin

ance

0.15

35

(2.0

1)

0.15

81

(2.0

6)

0.11

64

(1.5

4)

0.08

71

(1.1

5)

'61

- "6

5

0.38

81

(1.4

0)

0.39

40

(1.4

3)

Tim

e P

erio

d

76

- '8

0

0.58

11

(2.0

9)

0.63

22

(2.3

0)

•96

- '0

0

-0.2

894

(1.1

0)

-0.2

706

(1.0

4)

'01

- '0

2

0.22

50

(0.7

7)

0.30

21

(1.0

5)

R2

0.03

02

0.03

14

0.03

23

0.0

36

6

0.04

66

0.06

19

0.06

32

0.06

39

0.06

63

0.07

54

1.6 References

53

1. Ang, Andrew, Li Gu and Yael V. Hochberg, working paper 2005, "Is IPO Underperformance a Peso Problem?"

2. Barberis, Nicholas and Ming Huang, working paper 2007, "Stocks as Lotteries: The Implications of Probability Weighting for Security Prices"

3. Brav, Alon, 2000, "Inference in Long-Horizon Event Studies: A Bayesian Approach with Application to Initial Public Offerings," Journal of Finance, 55, 5, 1979-2016.

4. Brav, Alon, Geczy, C. and Gompers, P. A., 2000, "Is the Abnormal Return Following Equity Issuances Anomalous?" Journal of Finance, 56, 2, 209-249.

5. Brav, Alon and Paul A. Gompers, 1997, "Myth or Reality? The Long-Run Underperformance of Initial Public Offerings: Evidence from Venture and Nonventure Capital-Backed Companies," Journal of Finance, 52, 5, 1791-1821.

6. Daniel, Kent, Hirshleifer D., and Subrahmanyam A., 1998, "Investor Psychology and Security Market Under- and Overreactions", Journal of Finance, 53, 6, 1839-1885.

7. Daniel, Kent, Hirshleifer D., and Subrahmanyam A., 2001, "Overconfidence, Arbitrage, and Equilibrium Asset Pricing", Journal of Finance, 56, 3, 921- 965.

8. Davis, James L., Eugene F. Fama and Kenneth R. French, 2000, "Characteristics, Covariances and Average Returns: 1929-1997," Journal of Finance, 55, 389-406.

9. Dealers' Digest Publishing Company, 1961, "Corporate Financing, 1950-1960" (Dealers' Digest Publishing Company, New York).

10. Dean, Arthur H., William Piel Jr., and Row H. Steyer, 1951, "Issuer Summaries: Securities Issues in the United States - July 26, 1933 to December31, 1949" (privately printed, New York).

11. Eckbo, B. E. and Norli, O., 2005, "Leverage, Liquidity and Long-Run IPO Returns," Journal of Corporate Finance, 11, 1-35.

12. Evans, David S. , 1987, "The relationship between firm growth, size, and age: Estimates for 100 Manufacturing Industries," Journal of Industrial Economics, 35, 4, 567-581.

13. Fama, E. and French K., 1992, "The Cross-Section of Expected Stock Returns," Journal of Finance, 47, 2, 427-466.

54

14. Fama, E. and French K., 1993, "Common Risk Factors in the Returns on Stock and Bonds," Journal of Financial Economics, 33, 1, 3-56.

15. Fama, E. and French K., 1996, "Multifactor Explanations of Asset Pricing Anomalies," Journal of Finance, 51, 1,55-84.

16. Fama, E. and French K., 2004, "New Lists: Fundamentals and Survival Rates," Journal of Financial Economics, 73, 229-269.

17. Field, Laura C. and Jonathan Karpoff, 2002, "Takeover Defenses of IPO Firms," Journal of Finance, 57,5,1857-1889.

18. Fink, Jason, Fink, K. E., Grullon, G. and Weston, J., working paper 2005, "IPO Vintage and the Rise of Idiosyncratic Risk."

19. Gompers, Paul A. and Josh Lerner, 2003, "The Really Long-Run Performance of Initial Public Offerings: The Pre-Nasdaq Evidence," Journal of Finance, 58, 4, 1355-1392.

20. Hillstrom, Roger, and Robert King, 1970, "A Decade of Corporate and International Finance: 1960-1969" (Investment Dealers Digest, New York).

21. "International Directory of Company Histories, " St. James Press, Vols. 1 to 82.

22. Jain, B. A. and Kini, O., 1994, "The Post-Issue Operating Performance of IPO Firms," Journal of Finance, 49, 5, 1699-1726.

23. Jovanovic, Boyan and Rousseau, P. L., 2001, "Why Wait? A Century of Life Before IPO," AEA Papers and Proceedigs, 91,2, 336-341.

24. Kelley, M. Etna, 1954, "The Business Founding Date Directory", Morgan & Morgan Publishers, New York.

25. Konthari, S. P. and Warner, J. B., 1997, "Measuring Long-Horizon Security Price Performance," Journal of Financial Economics, 43, 3, 301-339.

26. Lerner, Joshua, 1994, "Venture Capitalists and the Decision to Go Public," Journal of Financial Economics, 35, 293-316.

27. Loughran, Tim, and Jay R. Ritter, 1995, "The New Issues Puzzle," Journal of Finance, 50, 1, 23-51.

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28. Loughran, Tim, and Jay R. Ritter, 2000, "Uniformly Least Powerful Tests of Market Efficiency," Journal of Financial Economics, 55, 3, 361-389.

29. Loughran, Tim, and Jay R. Ritter, 2004, "Why has IPO Underpricing Changed Over Time," Financial Management, 33, 3, 5-37.

30. Lowry, Michelle, and Schwert, W., 2002, "IPO Market Cycles, Bubbles or Sequential Learning?" Journal of Finance, 57, 3, 2002.

31. Lyon, J. D., Barber, B. M. and Tsai, C-L, 1999, "Improved Methods for Tests of Long-Run Abnormal Stock Returns," Journal of Finance, 54, 1, 165-201.

32. Moody's Industrial Manuals, various dates (Moody's Investor Services, New York).

33. Newey, Whitney K. and Kenneth D. West, 1987, "A Simple Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, 55, 3, 703-708.

34. Pagano, M., Fabio Panetta, and Luigi Zingales, 1998, "Why do Companies Go Public? An Empirical Analysis," Journal of Finance, 53, 1, 27-64.

35. Rajan, Raghuram and Servaes, H., 1997, "Analyst Following of Initial Public Offerings," Journal of Finance, 52, 2, 507-529.

36. Ritter, Jay R., 1991, "The Long-Run Performance of Initial Public Offerings," Journal of Finance, 46, 1, 3-27.

37. Ritter, Jay R. and Ivo Welch, 2002, "A Review of IPO Activity, Pricing, and Allocations", The Journal of Finance, 57, 4, 1795-1828.

38. Shiller, Robert J., 1990, "Speculative Prices and Popular Models," Journal of Economic Perspectives, 4, 55-65.

39. Shumway, T, 1997, "The Delisting Bias in CRSP's Data," Journal of Finance, 52, 1, 327-340.

40. Shumway, T., and Warmer, V. A., 1999, "The Delisting Bias in CRSP's Nasdaq and its Implications for the Size Effect," Journal of Finance, 54, 6, 2361-2379.

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56

Chapter 2: Life Cycle of Public Firms: Firm Maturity and

Post IPO Performance in Fundamentals and Returns

2.1 Introduction

This paper links industrial economics to finance by exploring the effects of a

firm's age on realized returns and firm fundamentals for the first time. Using a unique

dataset with the founding and/or incorporation dates for 14,665 firms, we describe the

evolution through time (1965 -2006) of public firms as they mature in terms of their

growth potential, innovative edge, process efficiency, liquidity issues, cash flow risks,

default risk, and, profitability. We show that an equal weighted portfolio composed of

mature firms earns between 20 and 30 monthly basis points in excess to industry

portfolios and Fama & French size and value portfolios. Analyzing firms' age cycle can

help us understand the challenges that the firm overcame, but more importantly, we can

asses what its' future might be in a systematic form.

We divide the time path of a company in three stages: 1) youth, 2) maturity, and

3) old age.8 We find that in their youth firms are in a highly uncertain stage, from which

only the strongest ones survive and become mature. Innovation drives young firms; they

develop a limited number of products that are often highly original, allowing them to

differentiate themselves from well established firms. Successful young firms grow their

assets and sales quickly, about 50% to 100% faster than their industry peers. Initially,

We define mature firms as firms in their mid teen years, twenties, and up to their mid thirties.

We consider firms below 12 years as young, firms in their late teens and twenties as mature and aging firms as those older than 55 years. We define specific age groups in detail later.

57

young firms have very high Tobin's Q's on average 2.8 and as high as 7.7, which

declines to an average 1.7 as they mature and enter their early-twenties.

The desire for young firms to innovate and establish brand name along with a

tendency towards having a small market share causes these firms to incur in high

expenses resulting in low and even negative profitability, ROA is on average 6% below

comparable industry peers. At the same time, we find that on average young firms

finance themselves with a higher proportion of short term debt relative to total debt than

older firms, 34% vs. 24%. Short term obligations, along with higher illiquidity and low

return on invested capital contribute to a high delisting and default probabilities. On

average Ohlson's default is 2.8% vs. 1.3% for old firms (more than twice) and actual

annual delisting probability is 7% vs. 2%. Average credit ratings increase from BBB- to

BBB+ as firms become old, while bond yields decrease from an average 8.65 for young

firms to 8% for old ones. In addition, on average young firms require investors to sign for

a long-term investment horizon. This is to say, measured in terms of equity duration, cash

flows will be further in time than for younger firms.

Nevertheless, the story becomes sunny if young firms9 survive to maturity and

develop well established product lines, brand, and market share. They consolidate growth

opportunities and become process efficient. Their gross margins double from an average

14% to 32% as they reach their twentieth anniversary. Meanwhile, mature firms are able

to maintain an innovative edge and, having proven themselves as reliable, significantly

decrease default and delisting probabilities, while increasing dividend distribution. In

maturity, firms can be seen as being "star performers," and their investors will certainly

9 As shown in the companion paper "Firm Maturity and IPO Underperformance", about 30 percent of young IPO's never reach their fifth year anniversary before being delisted.

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bear fruit in return space. Mature firms yield average monthly alphas between 40 and 50

basis points in excess of an standard four factor model capturing market returns, size,

value, momentum.

Alas, outperformance is not sustainable for an infinite time period. At some point

these firms will begin an aging process. Innovation and growth opportunities will drop

significantly below industry averages. The firms will start to resemble cash cows as their

cash distributions substantially increase, dividend yield for old firms is on average 3.3%

and up to 6.3% in the top 95th percentile, while mature firms have a yield of 1.1% and

young firms yield 0.7%. Overall, old firms are highly liquid and stable with very low

default probabilities and low return uncertainty (low variance), Sharpe ratios for these

firms are close to 1. After a while, as these firms age and become too old they will most

likely face the following choices: 1) re-invent themselves into a different company; 2)

partition themselves into several entities; 3) perish.

The chapter is divided as follows: Section 2.2 establishes the relevance of age and

summarizes key models developed to capture firm age and/or firms' product life-cycles.

Section 2.3 describes the dataset. Section 2.4 links firm age with expected returns.

Section 2.5 describes key fundamental characteristics and their relation with firm age.

Section 2.6 describes investment opportunities. Finally, Section 2.7 concludes.

2.2 Firm Age Models and the Relevance of Maturity

Boyan Jovanovic 1982 proposes a theory of "noisy" selection in which firms learn

about their efficiency as they operate in their industry. The efficient firms grow and

survive, while the inefficient ones fail and cease to exist. The theory partially captures

59

firm's evolution in time, along with firm size and industry concentration effects and

derives the following: 1) Small firms will grow faster and will be less likely to survive. 2)

Firm size and concentration are positively related to rates of return. 3) The correlation

over time of rates of return will be higher for big firm and those in a concentrated

industry. 4) Concentrated industries will have higher variance in their rates of return. 5)

Average profits will rise as the industry matures and firms become larger, this is a result

of the unprofitable firms leaving while profitable ones are able to stay and grow;

therefore, overall profitability increases as long as product prices do not fall.

David Evans presented the first industrial economics paper to directly study firm

dynamics linked with age in 1987. In his paper, looking at a sample of firms in

manufacturing industries, Evans finds that firm growth, growth variability, and the

probability of failure decrease with firm age. In addition to the age effect, growth

decreases with firm size as survival rate increases. Evans concludes that "firm age is an

important determinant of firm dynamics."

Steven Klepper develops a model to capture innovation over the product life-

cycle of technologically progressive industries from birth through maturity. The model

emphasizes the differences in innovation capabilities among firms as well as the

importance of firm size. Klepper's model predicts that over time product R&D of firms

will decrease while process R&D will increase. The life-cycle is described in the

following form: 1) when industries are young, firm entry will be high and firms will offer

many versions of their industries' product, innovation is very high. 2) Subsequently,

entry slows, exit overtakes entry and there is a "shakeout" in the number of producers;

product innovation and diversity of competing versions decline as "the depletion of

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opportunities to improve the product coupled with locked-in of the dominant design leads

to a decrease in product innovation." 3) Finally, firms switch gears to improve the

production process; firms that are inefficient in the production of the dominant design

perish and efficient ones increase benefits form less competition. Agarwal and Gort

present a similar model relating firm survival probabilities and product life-cycle. They

conclude that firm survival will be dependent on both product and firm life-cycles.

Pastor and Veronesi develop a valuation model that captures investors learning

about firm's profitability over time. Their model predicts that market-to-book ratio will

increase with uncertainty about firm's average profitability, particularly for firms that do

not pay dividends. The ratio is predicted to decline over a firm's lifetime as uncertainty

declines due to investors learning. Investors attempting to value newly listed firms will be

challenged with substantial uncertainty of the firm's future profitability and growth rates.

In their model market-to-book ratio will increase with the uncertainty about book equity

growth rates due to the convex relation between the growth rate and terminal value. They

present empirical results to their model, however when estimating firm age "a crucial

variable in our empirical investigation" they follow Fama and French 2004 in which they

consider each firm as "born" in the year of its first appearance in the CRSP database.

Fama and French's proxy firm age by the company's listing date. They do not

find that firm age is reliably related to financial characteristics. However, when we

measure age by incorporation and/or founding date, we find that firm age is related to

fundamental measures and expected returns. In addition by using our firm age measure

we are able to differentiate age effects, listing year effects, and year effects.

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Figure 2.1 shows that young firms only yield an annual expected return of 15%

equal weighted and 6.2% value weighted. As the firm matures (in its' late-teens and

twenties) expected returns climb to 20.5% equal weighed, 14.5% value weighted. After

maturity, as firms age, returns gradually decline back to 15% equal weighted returns or

12% value weighted. The return premium between mature firms and young/old firms is

on average 5.5% annualized.

When we look at Figure 2.2, the portfolios annualized standard deviation along

with Figure 2.1 causes the curvature to become puzzling. Contrary to predictions of a

classical asset pricing model following Harrison and Kreps, a portfolio of young firms

has very low returns and very high volatility. The classical model would predict that for

higher volatility we should observe proper compensation and have higher returns, which

is not the case. Young firms offer low returns although they have very high variance.

After firms reach maturity between their mid-teens and early-twenties, returns climb

while variance decreases. From this point on, returns behave consistently with a rational

pricing model. From mature to old firms, returns and volatility experience a gradual

decline with firm ageing. Potential reasons for the puzzling failure of the rational model

in which young firms do not offer compensation for their risk is explored in the following

sections.

Consistent with Pastor and Veronesi's model Figure 2.3 shows that young firms

have lower book-to-market ratios, which increase monotonically as firms mature from an

average of 0.68 for young firms to 0.94 for aging firms, the median rises 0.52 to 0.83.

The average and median ratios slightly decline from the peak when we reach the oldest

age group, it could be that after firms in these age groups we capture some firms that

62

have reinvented themselves and hence become more "growth" like, thus decreasing the

average and median ratios. It is important to note that within each age group there is a

very wide spread between growth and value stocks that ranges from 0.15 for the bottom

5th percentile up to about 2.0 for the top 95th percentile.

2.3 Data

2.3. A Measuring Firm Age

This paper is the first to relate returns and fundamental measures to an accurate

measure of firm age; previous papers such as Fama French 2004, computed firm age as

the difference between year t and the year when the firm becomes listed on CRSP for the

first time. In this paper firm age is computed as the difference between year / and the

earliest of either its founding or incorporation date. The difference can be substantial: for

example, under the Fama French 2004 age measure in June of 2005 a firm such as

Goldman Sachs that went public and was listed in CRSP in 1999 would appear to be 6

years old, and Yahoo, which went public in 1996 would appear to be 3 years older than

Goldman. In reality, Goldman was founded in 1869 making it 136 years old and Yahoo

founded in 1994, was 11 years old by 2005. In addition, under the Fama and French age

measure it is impossible to differentiate age effects from listing year effects.

Founding dates and/or incorporation dates used to compute firm age were

obtained from the compilation of mainly two datasets and several additions/modifications

done by hand: (1) The extended Jovanovich and Rousseau (2001) dataset. This dataset

was extended by Fink, Fink, Grullon and Weston (2005); the dataset contains the date of

first incorporation and/or founding date for a sample of publicly traded firms between

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1925 and 2005. (2) The Field-Ritter dataset of company founding dates, used in Field and

Karpoff (2002) and Loughran and Ritter (2004). This dataset contains the founding dates

for 8,309 firms that went public in the U.S. during 1975-2005. All founding dates that

were not in agreement between both datasets were verified by hand. The final Founding

Dates dataset received more than 1,700 corrections and additions10 and contains

incorporation and/or founding dates for 14,665 firms listed in CRSP from 1925 to 2005.

2.3.B Sample Selection

The sample includes all NYSE-, AMEX-, and NASDAQ-listed securities with

share codes 10 or 11 (exclude REITs, ADRs, LP's, and Close-end Funds) that are

contained in the intersection of the CRSP monthly returns file, the COMPUSTAT

industrial annual file, and the Founding Dates Dataset, between July 1963 and December

2006. To avoid delisting bias and extreme negative returns from young firm IPO's as

shown in the companion paper "Firm Maturity and IPO Underperformance" in which

mainly young firms are unable to survive beyond their 3rd year post-IPO anniversary, we

restrict the sample firms to those that will be listed at least 3 years after their first listing

year on CRSP. Furthermore, firms less than 1.5 years of age are also excluded from the

sample. The final sample is composed of 141,865 firm-years; table 2.1 lists the number

of firms per year and the percentage of the total CRSP firms and market value that these

firms represent. The number of firms per year ranges from 1,360 firms in 1965 to 5,598

in 1998, and as can be observed in Figure 2.4, the sample covers at least 90% of the total

We had 846 additions and over 900 corrections/verifications. A detailed description of the sources used for all additions and corrections can be found in the companion paper "Firm Maturity and IPO Underperformance"

64

market value for every year (except during 1973 when the NASDAQ listed several

thousands of young/small firms).

To ensure that accounting information is already impounded into stock prices, we

match CRSP stock return data from July of year t to June of year t+1 with accounting

information for fiscal year ending in year t-1, as in Fama and French (1992). To be

included in the return tests, a firm must have CRSP stock price, shares outstanding and

age data for June of year t.

2.3.C Fundamental Characteristics

This paper focuses on thirteen fundamental measures which are classified in six

categories: 1) Growth; 2) Innovation; 3) Efficiency; 4) Liquidity and Cash Flow Risk; 5)

Default and Debt Structure; and 6) Profitability. Appendix A has a full description of

fundamental variables used. This paper will show the evolution of these fundamental

characteristics as firms mature, tying them together and suggesting how such

characteristics impact expected returns and can be used for investment purposes. In order

to avoid outliers and data errors influencing results, measures will be winsorized for each

year at the top/bottom 1%.

Throughout our analysis of returns and/or fundamental characteristics, we will use

ten Age portfolios in which firms are classified every year based on their current age: 1)

young firms (four or less years old); 2) five to seven years (young*); 3) eight to twelve

years; 4) thirteen to eighteen years (mature); 5) nineteen to twenty five years (mature*);

6) twenty six to thirty five years (mature**); 7) thirty six to fifty five; 8) fifty six to

65

seventy five years; 9) seventy six to one hundred years; 10) above one hundred years

(old.) Firms older than 35 years are considered to be in an aging stage.

When expected returns are analyzed spreads between mature vs. young (M-Y)

firms and mature vs. old (M-O) firms will be computed. In general, throughout the

analysis spreads between the extreme young firms (4 or less years) and the early mature

firms (13 to 18 years) will be examined. In some instances, particularly if performing a

double sort, the youngest 2 age groups may be assembled together to add to the number

of firms and add power to our results. Additionally, in some very rare instances, maturity

groups might be coupled together or spreads computed using an older maturity group,

which will be indicated with and asterisk.

2.4 Firm Maturity and the Cross-Section of Returns

2.4.A The Firm Maturity Return Spread

As shown earlier, the average expected return for these portfolios present a

humped curve, where returns are low for young firms, peak for mature firms and then

decrease as firms age. Table 2.2 presents average returns from 1965 to 2006 for the ten

age groups and the spreads between mature vs. young (M-Y) firms and mature vs. old

(M-O) firms. Panel A presents equal weighted returns, the average monthly spreads for

mature minus young firms is 45 basis points (bps) and 47 for mature minus old firms both

statistically significant, this represents an annualized return of about 5.5% on these zero

cost strategies. Furthermore, consistent with the predictions in the model by Pastor and

Veronesi, as firms mature, overall risk (return variance) decreases; the Sharpe ratio

increases almost strictly monotonically from .525 for young firms to .954 for old firms.

66

Young firms have higher maximum monthly returns but also have a higher failure rate

and a higher default probability.11 Not surprisingly, firm size increases as firms mature,

however there is presence of "sma//" firms all across age groups.

Panel B presents value weighted portfolio returns, which are lower than equal

weighted returns for all firms, however the difference is greater for young firms. As the

contribution to the portfolio's return from small firms decreases, the spread between

mature and young firms increases to 69 basis points or an annualized return of 8.24%;

Implying that big young firms tend to big underperformers. The size effect that small

firms carry over portfolio returns decreases as firms mature, the difference between equal

and value weighting is low for aging firms.

Panel C presents skewness and kurtosis for the return distribution of the ten age

portfolios. Young firms have a positive skew, which implies that some returns in the right

tail would not be expected under normality which might point to periods where investors

are overly optimistic of young firms or that some small firms experience sharp increases

in fundamental value; as firms age, skewness changes from positive to negative.

Observing the excess kurtosis, on average 2.9, all age portfolios have very high

excess kurtosis, which in fact indicates that there are several extreme months in the return

distribution that would be considered 'extremely infrequent' under normality. Kurtosis

for equal weighted portfolios increases as firms mature, implying that extreme returns are

more likely for older firms. With the intention of identifying how much the return

distribution would change if we did not have 'infrequent'' months, we eliminated the top

and bottom 1% returns for each portfolio's return distribution and report skeweness,

It is important to consider that this sample already mitigates the delisting bias for young firms by excluding all firms that will delist before their third year IPO anniversary from the sample.

67

kurtosis and Sharpe ratio for the adjusted distribution. The Sharpe ratio increases slightly

as portfolio variance decreases slightly. Overall skewness remains very similar and

excess kurtosis drops below 1.

Given that firm age is positively correlated with size and book-to-market (B-M)

ratios, an important question is whether the return curvature based on firm age (so far

described) can be explained by standard size and value (B-M) portfolio returns or if this

age effect is independent of size and value. Panel D presents average raw returns and

excess returns to size-value portfolios. Following Daniel et al. 1997, we form five size

portfolios and then subsequently within each size portfolio, divide each portfolio into

portfolios based on the firm's book to market ratio, an then subtract the corresponding

size-value portfolio's return from each firm pertaining to the size-value group. The results

show that on average older firms have negative (statistically insignificant) excess returns

to the size-value factors. Young firms underperform matched size-value benchmarks by

33 basis points (statistically insignificant); however, mature firms show a strong

outperformance between 19 and 28 monthly basis points all statistically significant.

When we take a look at the spreads for mature vs. young/old firms we find impressive

annualized excess returns of 7.33% and 3.71% respectively.

A check for robustness of the results presented so far is to group firms in age

deciles. Instead of keeping the age buckets fixed, every year we group firms in ten age

deciles each containing the same number of firms. Therefore in years prior to the creation

of the Nasdaq, when the number of young firms is low, there are fewer young firms in the

young firm decile. Panel E presents returns and excess returns for each age decile and the

resulting average age for each of the deciles. Figures 2.5 and 2.6 show previously

68

presented results hold to be true for the curvature in both returns and excess returns for

size-value portfolios. The spreads between mature firms (third bucket) and young/old

firms are still between 30 and 40 basis points. The returns for mature firms and their

spreads are a bit lower because by deciling puts some firms in their early teen years in the

youngest firm bucket (increasing that bucket's return) while at the same time the mature

buckets include firms that otherwise we would consider as being in their early aging

stages.

Next we see if the results differ by industry. Panel F presents excess returns to

industry portfolios using Fama and French 2000 industry/sector classification based on

12 industrial sectors and 48 industries. Each year we group firms based on sector/industry

and compute excess returns to sector/industry portfolio average. The return curvature

show on Figure 2.7 is consistent with our previous results for both industry and sector:

mature firms earn between 16 and 23 monthly basis points in excess to its sector/industry

portfolio. The spread between young - mature - old firms is between 7.08% and 3.63%

annualized excess to sector/industry returns.

To further examine the return curvature by industry, as well as industry age

distribution, in Panel E presents returns, Sharpe ratios, and average number of firms for

each of the twelve industrial-sectors and classifies firms in five age categories.12

Consistent with previous results the return curvature is present in all 12 sectors and the

Sharpe ratio increases as firms mature. However, some sectors have a flatter curve

(Health and Energy), while others (Business Equipment, Shops, and Manufacturing)

The number of categories was shrunk; otherwise, very few firms would remain in each bin. The categories are: I)young, 9 or less years old; 2) mature, between 10 to 20 years; 3) mature*, between 21 and 35 years; 4) between 36 and 55 years; 5) old, above 55 years. We consider two maturity ranges because some industries seem to peak later than others.

69

show a more pronounced curve. Nevertheless, the average monthly spreads are always

positive and range from 18bps to 80bps for mature minus young firms, and between

12bps and 74bps for mature minus old firms.

We look at the average number of firms per age group within each sector and

classify industrial sectors based on their tilt towards being dominated by young or old

firms. The Business Equipment sector (computers, software, etc.) is heavily dominated

by young firms. Quintiles are formed13 and it is found that average age for the youngest

and oldest quintile firms is 8 and 61 years. In contrast, Utilities' youngest quintile

average age is 30 years and oldest is 129 years: this sector is heavily tilted towards older

firms. The average sector firm age is 45 years, the youngest sectors in ascending order

are Business Equipment, Health, Telecom and Other with an average firms age of 34

years; the oldest sectors are Manufacturing, Chemicals, Non-Durables, and Utilities with

and average age of 62 years, all other sectors have average age.

So far we have showed that age return curvature persists and cannot be attributed

to either an industry effect or a size-value effect. Panel H analyses the age portfolios in 4

time periods,14 in all cases the curvature is present, mature firms between 13 and 35 years

earn a premium with respect to younger and older firms. The mature firms' excess returns

to size & value portfolios yield on average between 23 and 32 monthly bps, and excess

returns to 48-industry portfolios between 15 and 23 monthly bps, all periods have

statistical significance for mature firms.

In addition to the reported Age-Industry breakout, we also performed a sort where every year firms were quintiled according to age within their industrial sector, thus having a uniform distribution of firms. The results were very similar to reported ones. We used the reported table because it provides clear age cut points.

14 1) 1965 - 1975; 2) 1976 - 1985; 3) 1986 - 1995; 4) 1996 - 2006.

70

A simple zero cost trading strategy that buys mature and sells young/old firms

yields consistent excess returns to size & value between 30 and 53bps, and between 15

and 53bps in excess to the industry peers. However, the spreads are not statistically

significant for all periods. The zero-cost strategy would have failed significantly in some

years when young or old firms seem to get the upper hand with respect to mature ones. In

the past last two decades (1986 - 2006), as the number of listing young firms grew, the

zero cost strategy between mature and young firms became more consistent, yielding raw

and excess returns between 37 and 58 monthly bps all with statistical significance to the

99th percentile. Further investing implications of our results are discussed in Section 2.6.

2.4.B Time Series of Returns

To further examine the relation between firm maturity and stock returns, we

construct monthly time series returns for each age group portfolio and regress excess

returns (Rp - Rf) with the excess return to the market (RMKT - Rf), Fama and French size

and value factor returns (RSMB and RHML), and Carhart's momentum factor (RUMD)-

Table 3, presents alphas, factor betas, and R2 for each age group portfolio for the

following specified regressions.

Rp - Rf - a + / W • (RMKT - Rf) + eP [1]

Rp - Rf = a + /3MKT ' (RMKT - Rf) + /?SMB ' RSMB + 0HML * RHML + £p [2]

Rp - Rf = a + /3MKT ' (RMKT - Rf) + 0SMB • RSMB + 0HML • RHML + 0UMD ' RUMD + £P [3]

71

The first panel in Table 2.3 presents the resulting alphas and betas to the market

and reveals a curvature in the portfolio alphas. Alphas for young firms are statistically

insignificant from zero. Mature firms have an alpha of 58 bps, while the average old firm

alpha decreases to about 33 bps, both statistically significant. The long/short portfolio of

mature minus young firms yields an impressive alpha of 68 bps with a beta and R equal

to zero. This implies that portfolio returns are market neutral i.e. unexplained by returns

in excess to the market. Mature vs. old firms alpha is 25 bps and statistically

insignificant. At the same time, we introduce a new portfolio that longs mature firms and

shorts 50% of young and 50% of old firms; this portfolio gives an alpha of 46 bps, which

is statistically significant. As expected the portfolio betas with respect to the market

decrease monotonically from 1.36 for the young firms to 0.90 for the oldest firms.

The second panel includes in the regressions Fama and French size (SMB) and

value (HML) factors. By including these factors, the alphas of young and old firms'

portfolios become statistically insignificant; however, mature firms' alpha remains large

and statistically significant, between 24 and 29 bps. Market beta for each portfolio

approaches unity while the loading on size decreases monotonically and the loading on

value increases monotonically as firms mature. As expected, young firms load as small-

growth firms and old ones load as big-value firms. Mature firms load as mid-sized

slightly value firms and remain unexplained by either of the two factors. Furthermore, all

three zero cost strategies yield statistical significant alphas ranging from 28 to 56bps.

The third panel adds Carhart's momentum factor (UMD) in the regressions, which

increases the magnitude of mature portfolios alphas to a range between 37 and 50 bps.

The alpha of the three zero cost strategies becomes 40 bps for all three strategies. An

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interesting result is that all portfolios load negatively on momentum (behave as losers)

and loading decreases monotonically as firms mature. As a robustness check for previous

results, in the forth panel firms are grouped in age deciles instead of fixed age groups as

previously done, and regress the age decile portfolios with the four factor model. The

results hold and yield significant alphas in the range of 42 to 50 bps for portfolios with an

average age between 11.7 and 29.9 years. The zero cost strategies have alphas between

27 bps and 39 bps, and the mature vs. young portfolio remains the hardest to explain by

the four factors, R2 and very low loadings.

Finally, we take a look into the persistence of mature firms' alpha over time. Each

year we estimate the alpha of a portfolio of firms between 13 and 25 years old {mature)

using the four factor model. Figure 2.8 shows the estimated monthly alphas for each year.

The average monthly alpha is 33bps. Alphas are positive in 30 out of 42 years (71.5%)

with increasing significance and consistency over the past two decades.

2.5 What is Age?

We have shown that over a four decade time span, mature firms present

continuous excess returns to size & value, or industry portfolio returns that range between

20 and 50 monthly bps. This section aims to characterize the effects of aging and relate

them to several firm fundamental measures which we will then relate to the return

curvature. The section is subdivided in six parts: a) Firm Growth Rates; b) Firm's

Innovation Edge; c) Firm's Process Efficiency; d) Firm's Liquidity and Cash Flow Risk;

e) Default, Delisting and Debt Structure; and f) Firm's Profitability. In most of these

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subsections we will characterize the fundamental measure under analysis in the following

manner:

1) A descriptive panel that presents the average, standard deviation, bottom 5l

percentile, median, and top 95th percentile from 1965 to 2006 of each age-group's yearly

average of the measure. In addition, to show that results presented are not dominated by a

particular industry, every year we compute the industries' average of the fundamental

measure and subtract it from each firm within the industry; then we group the firms by

age and compute the average deviation of the fundamental measure by age group. The

seventh and eighth columns of the panel have these averages across time by industry.

Finally, the ninth column presents the average number of firms per year in each age

group.16

1 7

2) We decompose the measure's excess to its industry average into firm age ,

listing decade cohorts,18 and year effects as described in Deaton 1997. Additionally, in

our regressions as a robustness check we include the log of market value, log of book-to-

market ratio and interaction variables of these two characteristics with age. The resulting

coefficients of the decomposition19 are used to model the effects of age on the

We classify each firm in one of 48 industries based on Fama and French's 2000 industry classification, as described in Kenneth French's website.

1 The number of firms may vary slightly depending on data availability from Compustat.

To estimate the age effect we use the logarithm of age and age squared as our parameters.

18 Listing cohorts are defined based on the year in which the firm is first listed on CRSP, following Deaton 1997 the first cohort (firms listed prior to 1940) is dropped, then after we have a cohort for every decade, 1940 to 1949, 1950 to 1959,...., and 2000 to 2006. Listing cohort will capture the common effects only to the firms that are listed in that cohort and will be independent of the year effects which can affect all firms in the regression.

The decomposition used for the model is the one that does not include size or book-to-market variables.

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fundamental measure, which are summarized in three graphs which show firm age, listing

cohort, and year effects.

3) In addition we present Fama MacBeth average estimates of yearly cross-

sectional regressions. For these regressions we also include the log of market value, log

of book-to-market ratio, interactions, and cohort effects as robustness checks. In all our

regressions the fundamental measures being analyzed carries no industry effects since our

dependant variable is the excess to its yearly industry mean.

2.5.A Firm Age and Growth

We begin with a description of a firm's evolution in terms of its growth. For this

we use three fundamental measures: a) 1 year Asset Growth; b) 1 year Sales Growth; c)

Tobin's Q proxy (Market value of Assets / Book Value of Assets). In the early years of a

firm (less than seven years old) assets grow on average at a rate of up to 30%, or 10% in

excess to the industry mean, the top 95th percentile (top growth firms) grow at more than

145% per year. As firms mature growth rates decrease monotonically to a growth rate of

10% for the oldest group or 3.2% under the industry average. The spread between top

growers and asset destructors (bottom 5th percentile) also narrows significantly as firms

mature. In youth the spread is 180%, bottom 5th percentile firms shrink at rates of up to -

34%o, while outperform growers in the top 95th percentile grow at rates of 145%. As firms

become old the spread is reduced to 45.5%; the bottom only shrinks at rates of 8.7% and

the top growers are only able to grow only at rates of 36.8%. The dispersion between

growth rates is reduced to a third as firms mature from young to old, or from a standard

deviation of 52.4% to 17.9%. All this is consistent with predictions of Pastor and

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Veronessi's model and Evan's 1987 findings; uncertainty in growth rates shrink as firms

mature.

Similarly, when firms are young they are able to grow sales at an average rate of

33.9% or 15.6% in excess to the industry average, and as they age, sales growth drops to

9.5% or 3.6% below industry average. The spread between the bottom 5th percentiles and

top 95th percentiles also shrinks as firms mature from 158.3% to 46.2%. It is also worth

noting that asset and sales growth is stronger (above industry average) until the firm's

twentieth anniversary; afterwards it flattens and then drops to -3.5% from the industry

average. The yearly dispersion (standard deviation) of the fundamental measures also

drops significantly after the firm's twentieth anniversary. We believe results are very

robust as the sample size for each age bin and year is sufficient, each bin usually has 300

or more firm, except for the very young age groups.

The model derived form the regression estimates of asset growth in excess to the

industry suggests that extreme young firms (1 year old) grow at an average rate of 50%

above the industry mean, however this extremely fast paced growth rate quickly declines

to zero by the firms 20th anniversary and then after remains slightly below industry mean.

The cohort decomposition shows, not surprisingly, that firms listed during the 1990's

experienced an additional 8% average growth rate, which corresponds to the dot-com era

when many technology oriented firms were indeed able to increase their assets and grow

sales at very fast paces. However, in contrast to the newly listed firms of the 1990's that

grew at very fast pace, in general, all other firms during that decade grew 2% below

industry average. After the bubble burst in 2001, newly listed firms' asset growth

decreased to 6% above industry peers.

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An interesting result is that once we take into consideration the age effects, size

has a positive effect on growth. In other words larger firms are able to grow faster than

smaller ones of the same age. Furthermore, interaction coefficients show that the size

effect dissipates in older age groups. Book-to-market as expected has a negative

coefficient implying that low book-to-market firms {growth) are indeed growing at faster

paces than value firms.

The sales growth measure exhibits very similar behavior to the asset growth

measure: young firms are able to double sales in their early years, but by the late teen

years these firms drop below the industry average growth rate and remain beneath it as

they age. Size and book-to-market contribute in the same form to sales growth, bigger

firms relative to their age group grow faster and high B-M firms experience less growth.

Another way to proxy expected growth opportunities is Tobin's Q; in equilibrium,

Tobin's Q measure should equal 1, a Q higher than one implies that profitable growth

opportunities underlie and hence the firm could invest in new projects and grow its

assets. Consistent with previous results, the Q drops as firms mature, the ratio of market

value to book value of assets decreases from 2.8 for young firms to 1.3 for older firms.

Hence, as firms mature their Q approaches unity. If we look at the top 95th Q percentiles

young firms have Q ratios of up to 7.7 market-to-book value of assets the ratio decreases

to 2.7 for old firms. The median of firms older than 55 years shows a Q that is very close

to unity, implying that growth opportunities are limited for old firms.

Taking a look at the regression estimates of the Q-measure in excess to industry

average, it is important to note the significance of the interaction between age and book-

to-market ratio. It is not surprising that low book-to-market firms will have a higher Q-

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ratio, however it is a bit surprising that the interaction between age and B-to-M is

positive, as it implies that while keeping book-to-market constant older firms will have a

higher Q-ratio. Firm size seems to have no relevance for potential growth opportunities.

Firms listed after the 1980's, and particularly during the last decades had significant

above industry growth opportunities, while their competitors had relatively low

opportunities. However, this could be a result of the high valuations given by the market

to newly listed firms during the 1990's which fed into the construction of the Q-measure.

2.5.B Firm Age and Innovation Perspectives

It turns out that not only growth rates decrease as firms mature, but alongside the

innovation capabilities of firms decrease as well. Obviously a decline in the creative edge

of a firm would result in fewer new projects to undertake and shrinkage of growth

opportunities. It is possible that once firms establish their primary product lines

investment in new products decreases, turning their R&D focus into incremental

innovations of current products and resources towards being process efficient. The

average expense in research and development as a percentage of total assets declines

from 10% for young firms to 2.5% for the oldest age group or a decrease from 2.2%

above industry average to 1.6% below average. During the early years, firms less than

twenty years old, young and mature firms spend a median 6% of total asset value in R&D

and a maximum of 33.3%. Old firms top 95th percentile R&D spenders only go as high as

8.3%, and on the median spend 2% on R&D of total assets.

The regressions yield interesting results, as estimates predict a decline of R&D

expenditure with firm age, although this is not statistically significant. However, once we

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interact age with size and book-to-market variables, age provides additional information

indicating that aging firms decrease R&D expenditure. As expected, smaller and 'growth'

(low book-to-market) firms tend invest more heavily on R&D. Additionally, firms listed

during and after the 1990's have a higher expense in innovation than earlier listing firms,

this effect is counter-balanced by overall under-investment in R&D during the 1990's.

To further explain what happens to firms' innovative edge as they age, we

combine our sample with the U.S. granted patents dataset compiled by Hall, Jaffe, and

Tratjenberg 2001. Their data is comprised of detailed information on almost 3 million

U.S. patents granted between January 1963 and December 1999, all citations made to

these patents between 1975 and 1999 (over 16 million), and a reasonable match of

patents to Compustat. Even though the data yields interesting results, it is important to

take into consideration that there are three biases in the dataset20 which will tend to give

stronger results for old established firms. 1) CUSIP match is based on the 1989 universe

of companies, imposing a big limit on the sample of young firms that we are able to

match. 2) The measure of Patent Originality uses the number of citations made to

previous granted patents in the sample. Therefore, in the early years originality will be

overestimated. 3) The measure of Patent Generality uses the number of citations received

by other patents. Hence, generality for patents granted during the 1990's will be biased

downwards. 4) Additionally, the sample only covers granted patents, therefore we do not

have any information on overall patent fillings that young firms may have done and either

patents were not granted (abandoned the claims) due to the firm going under.

To further understand patent variables construction and the inherited biases refer to Hall, B. H., A. B. Jaffe, and M. Tratjenberg (2001).

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However, even after considering all the biases and the relatively small portion of

the full sample that we are able to match to the patent dataset (on average only 500 firms

are matched) and limiting our sample from 1980 to 1999,21 the results obtained shed light

on what may be occurring during the innovation process. Our results propose that young

firms tend to be granted fewer patents than older firms; however, these patents tend to be

more original and will be later referenced more frequently (higher generality). Hence, it

seems that in youth, firms invest in an original idea that will provide the base and

structure for later incremental improvements. For example, a young firm may develop a

new high tech cell phone and then as the product becomes successful file several patents

on the same core item, which protect for multiple configurations. Older firms, even

though they file and are granted a larger number of patents, often undergo more

incremental changes to already granted patents in their youth or of acquired young firms.

2.5.C Firm Age and Process Efficiency

The story described so far indicates that as firms age their innovative edge and

growth opportunities decrease. However, while growth potential decreases, firms work

hard on becoming more efficient. Efficiency dramatically increases from youth to

maturity, peaking when firms are between twenty and thirty five years old, then after

efficiency slightly decreases as aging firms become old. The average gross margin

increases from 15.3% in youth to 32% when the firms are in their twenties, then remains

steady at about 30%. However, if we consider gross margin in excess to the industry, it

increases from being 12.7% below industry average to 5.6% above average in maturity

21 We limited the sample to begin in 1980, because prior to 1975 patents do not have links to citations received which underestimates originality. Additionally, we wanted to reasonably capture young firms effects, which is limited to being matched to Compustat universe in 1989.

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and decreases to 1.7% above industry. The regressions indicate that gross margins

increase not only with age, but also with size and B-M ratio. As firms become bigger, for

example Walmart, they gain purchasing power which allows them to have higher gross

margins. We also note that over the last two decades margins have increased for all firms,

while for firms that listed during the 21st century margins have been lower.

Our second efficiency measure proxies for firms having an advantage over the

market in terms of the margins they charge to consumers and their efficient process.

Firms with high net operating profit after tax (NOPAT) margin are not only able to

charge higher margins to their products as indicated by gross margin but also have under

control their administration process. NOP AT margins as well as gross margins increase

with maturity from 8.6% to 13.9%. Margins flatten slightly above industry average when

the firm turns 18 years. In contrast to young firms that can have negative margins of up to

11.9% for the bottom 5% underperformers, old firms' lowest margin is only 4.8%.

Margins at the top 95% performers are very similar across all age groups around 27%.

Our third measure for efficiency is invested capital turnover, fueled by the belief

that firms with high turnover can be thought of as having a production advantage, which

allows for higher sales in comparison to peers with similar capital investments. Figures

2.9 and 2.10 show curvatures of invested capital turnover which increase from 1.73 for

young firms to 2.09 for firms in their teens and twenties, which later decreases to 1.45 for

old firms. Turnover in excess to industry average is 16.1% below average for young

firms, peaks at 11.7% above average and decreases back to 16.4% below industry

average as firms become old. The efficiency curve seems to be consistent with the return

curvature, the low returns associated with young and old firms is consistent with below

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industry average efficiency. Contrary to our previous two efficiency measures, the

regression results show that small and growth firms tend to have higher turnover ratios.

This points towards a shift in efficiency along with size and value; as firms grow in size

and become value firms, efficiency shifts from a production advantage (higher invested

capital turnover) to purchasing power advantage (gross margins) aligned with a more

competitive administrative process.

2.5.D Liquidity and Cash Flow Risk

When thinking of the return curvature, two questions arise: 1) whether the curve

is able to be explained in terms of a liquidity premium, and 2) whether it is related to cash

flow risk. To answer the first question, Amihud's illiquidity measure is used. Every year

at the end of June, the illiquidity of stocks is computed based on the prior year's trading

activity. As firm uncertainty decreases liquidity will most likely increase as more

investors trade on the stock. The first panel of Table 7 shows that, indeed as firms

mature, illiquidity decreases substantially, particularly after hitting the 35 year old mark.

In youth average illiquidity measures about 1 with a median of 0.78, our measure for

illiquidity then drops strictly monotonically on the median to 0.15 for the eldest group.

Regressions show that liquidity is extremely positively correlated with firm size

and age, consistent with previous studies smaller firms tend to be more illiquid than

larger ones. Surprisingly, when we combine age and firm size we find that the interaction

coefficient is positive; implying that among similarly sized firms, older firms will tend to

be more illiquid. At the present time, we cannot conjecture an explanation as to why

older firms would become more illiquid once we account for firm size. At the same time,

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value firms, which tend to be older and better established, tend to be more illiquid. The

interaction between value and age shows that liquidity increases for older firms once

value is taken into account. This suggests that "young" value firms are illiquid when in a

state of distress, while older value firms are well established cash cows with good

liquidity. Another unexpected result is that since 1996 stocks in general have become

more illiquid, particularly due to the fact that during the late 1990's and post-2003 the

stock market was highly liquid; at the same time, overall new listing firms tend to have

higher levels of illiquidity.

The relation between firm age and liquidity is explored further by sorting stocks

into three liquidity groups and age groups. As described in the literature, low liquidity

firms command higher returns and have significantly lower market value than their peers.

High liquidity firms' average market value is 1 billion dollars for young firms, 2 billion

for mature, and 6.2 billion for old firms. In contrast, low liquidity stocks average market

value ranges from 30 to 40 million dollars among young and old firms.

Mature firms command a premium on top of their liquidity group for all three

liquidity groups. Figure 2.11 shows the return curvature is present in all liquidity groups

for mature firms between 13 and 35 years old. The age effect is more predominant for

low and medium liquidity groups where the percentage of young and mature firms is

higher compared to their age peers with high liquidity. Across all liquidity groups mature

firms are the only group that has significantly positive excess returns to Fama and French

size and book-to-market portfolios; excess returns for mature firms range between 20 and

51 basis points. Consistent with our previous results, Sharpe ratios increase

monotonically with firm age regardless of liquidity constraints. Older firms have lower

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uncertainty and higher return-volatility payout; however, within age groups Shape ratios

are higher for low liquidity stocks.

Unexplainably, young firms that belong to high and medium liquidity groups are

significantly punished by the market, earning raw returns between -30 and 65 bps per

month or between 49 and 120 bps below comparable size & value stocks. It may be that

overall these medium-high liquidity young firms create a great amount of hype causing

significant trade volumes and high market valuations; however when these firms fail to

establish product lines their returns plummet. As liquidity decreases, excess returns to

size and value portfolios significantly increase for mature firms. In the most illiquid

tercile, mature firms yield between 28 and 51 monthly bps in excess to size and value.

Low liquidity young and old firms have high returns relative to other liquidity returns but

returns are below their liquidity group average. Even though the age effect is clearly

more pronounced for low liquidity firms, returns are consistently higher for mature firms

across liquidity groups, which indicate that age has relevance on top of liquidity.

In addition to liquidity, we look into trading frequency: the percentage of non-

traded days is on average 19.7% for young firms and decreases monotonically to only

5.5% for the oldest firms. The median of non-trading days decreases from 15.2% to zero,

and even for the most non-traded stocks (top 95 th percentile) young firms have twice the

percentage 39.6% vs. 21.1% for old non-traded stocks. Regression results show that non-

traded days decrease as firms age and as they become larger. Consistent with liquidity

results, it seems that in the last decade overall firms non-traded days have increased by

6%, this is a counterintuitive result since we would expect that as markets became more

integrated and moved to electronic platforms, overall trading would have increased. We

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observe post-Nasdaq introduction in 1972, there was a sharp decline in the percentage of

non-traded days to 5% below industry average; afterwards, the percentage of average

non-traded increased gradually to 6% above industry average.

Finally, we use equity duration as proposed by Dechow, Sloan, and Soliman, to

proxy for the time that it would take cash flows to pay back investors. A higher duration

implies that it will take more years to achieve a pay back and therefore, investors should

have a longer investment horizon in mind. If high duration is accompanied with high

default probabilities there would be a very high risk that future cash flows would not be

realized, hence we can also think of equity duration as a proxy for cash flow risk. Not

surprisingly, duration is higher for young firms on average 17 and decreases as firms

mature to 14.5. For the top 95th percentile, a young firm's duration is up to 23 years,

compared to 17 for old firms. Therefore, firm maturity lowers the risk of cash flows not

being received and reduces the investment horizon. A firm's duration drops below

industry average after the firm's twenty-fifth anniversary. As we would expect,

regressions show that duration is significantly negatively correlated to both size and

value: bigger firms will offer a shorter investment horizon and growth stocks will have

higher duration or longer investment horizons. We also observe that firms listed during

and after the 1990's have higher durations, most likely because young technology

oriented firms require longer investment horizons. However, we did not find a direct link

of equity duration to the return curvature.

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2.5.E Default and Debt Structure

Up to this point, we have found that young firms offer great growth opportunities

and are innovation driven, while mature firms retain some growth opportunities as they

become more efficient and old firms seem to offer security in terms of higher liquidity

and lower equity duration. In order to identify a source of observed underperformance of

young firms, we examine default probability estimated using Ohlson's 1980 default

measure. Table 2.8 shows the probability of default for young companies is on average

2.8% and climbs up to 7.7% for the top 95th percentile of riskier firms. As firms become

older the probability of default shrinks in half, or on average 1.3% and the probability of

risky firms decreases to only 2.7%. After the firm's eighteenth anniversary, average

default probabilities fall below the industry mean.

Regression results show that once size and book-to-market are controlled, default

probability will decrease based on both variables but will increase with firm age. This

implies that younger, big value firms will have lower default probability than older firms

of the same size and similar book-to-market ratio, which is a bit counterintuitive.

Additionally, it is found that default probability has been on the rise for firms listing

during the 1980's and peaked for firms listed in the 1990's. Default could be one of the

underlying factors that explain low returns of young firms, investors could underestimate

the probability of such firms perishing. Particularly the failure of highly liquid young

firms may be underestimated by investors, while the returns of young "star performers"

are not high enough to compensate for the vast number of failing youths that default and

cease to exist.

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Actual delisting is further inspected by forming a table that tracks firms delisting

in time. Firms are grouped according to initial age group when the firm is first listed,

surviving firms are classified in the subsequent age group into three categories using

CRSP delisting codes, survivals, delisted because of a merger or acquisition (codes 200),

and delisted because of death or dropped by unexplained reasons (codes 400 and 500).

The delisting probability is scaled within each age group by year number in which the

delisting occurs; thus, if the delisting is in the first year the annual probability is 1, if it

occurs in year 5 the annual probability is going to be 0.2, presenting annualized delisting

probability. By scaling we avoid overestimating the probabilities for older age groups

that have several more years within their age bucket than younger age buckets.

Delisting probabilities show that young firms have a higher probability of being

delisted in their earlier initial years ranging between 7.1% and 10.5% annual probability

for firms under 18 years. This probability substantially decreases to range between 2.0%

to 3.8% for firms over 55 years old. The table shows that overall annualized probability is

indifferent to whether a firm has been listed longer or not in the stock market. In sum,

firm's age matters the most. While delisting probability decreases with age, merging

probability is very similar across age groups, the lowest probability is for young firms

and ranges from 5.2% to 8.5%, as firms mature merging probability increases to range

between 7.5% and 10%, or on average about 9%; these results are consistent with

previous results regarding IPO survival obtained in a companion paper.

A firm's debt structure is further examined by computing the percentage of long

term debt to total debt. It is found that on average young firms have lower percentage of

long term debt compared to older firms, 66% vs. 75%. Young firms' long term debt

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access might be relatively limited and hence have a lower percentage of long term debt in

relation to overall debt structure. At the bottom 5 th percentile, young firms have only 10%

of long term debt, which implies that these firms are financing themselves almost solely

on short term debt which expires in less than one year. In comparison, the bottom 5th

percentile of old firms have between 25% and 33% of long term debt. The higher

percentage of short term debt in young firms explains at least in part the higher estimated

default probability. However, unreported regression results show that age is secondary to

firm size and book-to-market ratios and age coefficients become statistically significant

only when age is interacted with these characteristics.

FISD bond data was used to obtain ratings and bond yields which was matched to

our firms using the following procedure: 1) According to industry and debt-to-capital

ratio we matched our firms to similar firms rated bonds; 2) Converted the ratings into

numeric values (See Appendix B); 3) Used the weighed average of bond yields with

similar ratings. Results only cover the time period from 1985 to 2006 because prior to

1985 bond data is very limited. Corporate yields decrease as firms mature from an

average of 8.63% to 8.02% and from a median of 8.7% to 7.7%. The top 95th and bottom

5 percentiles show that high and low yields are very similar for all age groups.

Conversely, as yields decrease with maturity, debt ratings improve with maturity.

Old firms, on average, have better credit ratings than younger firms, which is consistent

with older firms having lower default rates. The median rating for young firms is 6.86

which is equivalent to a BBB- rating and increase to 7.4 for old firms, equivalent to a

BBB+ rating. At the top 95th percentile all age groups seem to have a rating of 8.2, or

equivalent to an A+ rating, likewise at the bottom 5th percentile all age groups ratings are

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about 5.4 which is equivalent to B+ rating. Additionally, every year we estimate the cost

of equity using a three factor model (Mkt-Rf, SMB, and HML) with sixty months of prior

returns, and compute the weighted average cost of capital (WACC). The cost of capital

decreases with age from 13.7% to 12%, the decrease is slightly less significant when the

median is observed, which drops from 11.5% to 10.5%. The bottom and top 5th

percentiles show that in all age groups there are firms with very low and very high cost of

capital: the spread is as low as 5.6% for old firms and as large as 25.9% for young firms.

2.5.F Profitability and Cash Flow Distribution

Finally, the analysis is rounded out by examining profitability and cash flow

distribution. Results in Table 2.9 show that young firms have on average - 1 % ROA, or

6% below industry average, which increases to an average ROA of 8.1% or 1.3% above

the industry mean. During youth, a firm is likely to present losses, as market share is

small and expenses to develop an innovative product and create a brand are high. Young

firms ROA in the bottom 5th percentile can be as low as -48%, compared with only - 1 %

for old underperformers. However, as firms mature, and conditional on surviving their

youth stage, returns on assets increase as prior investments bear fruit. As firms mature

ROA will be above industry average as long as firms remain competitive. The upside for

the top 95' percentile performers is significantly higher for young firms which have an

ROA of 24% while top old performers only yield a return of 17%. When regression

estimates are examined further we find that ROA increases not only with age but also as

firms become bigger and as book-to-market ratios increase. The decomposition shows

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that ROA for firms listed during and post the 1990's declined significantly below

industry peers.

In addition to analyzing profitability in terms of ROA, cash flow distributions to

shareholders are measured using firm's dividend yield. The yield differential among

young and old firms increases monotonically both on the average and the median from

0.7% to 3.3% and 0.2% to 3.1% respectively. Old firms big dividend distributors (top 95th

percentile) give as much as 6.4% of market value in dividends per year, event old firms in

the bottom 5l percentile will give at least 0.6%. When firms reach their thirty-sixth

birthday, average dividend distribution is above the industry mean. Regressions show that

dividend yield increases with size and book-to-market ratio as well. Dividend yield has

been declining through the decades for newly listed firms, hitting a low for firms listed

during the 1980's. This is consistent with general observations of newly listed stocks that

tend to offer low dividend yields in order to grow by reinvesting cash flows in new

projects, an example of this is Microsoft.

2.6 Firm Age and Investment Opportunities

Up to this point mature firms have been shown to posses excess returns to both

industry portfolios and Fama and French size and book-to-market portfolios. Monthly

excess returns for firms between 13 and 35 years old range from 17 to 28 basis points.

Furthermore, when the intersection between liquidity groups and firm age is examined

we find that, consistent with the literature, illiquid stocks carry a significant premium

over liquid stocks, and this premium is accentuated by high returns (an average 2% per

month) pertaining to mature illiquid firms.

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Table 2.10 categorizes firms according to the level of concentration in their

particular industry and firm age. We follow Hou and Robinson 2006 and compute the

Herfindahl index of a firm's assets and sales as a percentage of total industry assets and

sales, a three-digit SIC number is used to classify the industries. A higher Herfindahl

indicates a more concentrated industry and similarly a low index indicates a highly

fragmented industry. The industry Herfindahl is averaged over three years to avoid one

year errors influencing results. Hou and Robinson describe concentrated industries as

"innovation-poor, profit-rich industries with high barriers to entry" and find that on

average low concentration industries have higher returns that highly concentrated

industries. Within our sample a significant spread between high concentrated and low

concentrated industries is not found, however excess returns to Fama and French

portfolios is 10 bps statistically significant for low concentrated industries.

Industry sales concentration and firm age portfolios show the maturity effect is

persistent for all levels of concentration, the spread between mature and young/old is

between 33 to 58 monthly basis points, or between 24 and 58 bps in excess to Fama and

French portfolios. The spread for concentrated industries when measured using assets is

slightly higher, between 40 and 64 bps on raw returns. Interestingly, returns are higher

for young firms in low concentration industries: after firms reach maturity and begin the

aging process (after their thirty-fifth birthday) returns increase for firms in concentrated

industries.

Furthermore, young and mature firms in highly competitive industries (low

concentration) are on average bigger than age peers in highly concentrated industries. It is

probable that young firms competing in a very concentrated industry will incur a very

91

high costs to establish themselves as competitors and will have a high probability of

failure because the barriers to entry are very high, hence we observe low returns for these

firms. In comparison young firms in a fragmented industry will have a higher probability

of success and this is reflected in overall returns and market value. As firms mature and

establish product lines as well as improve efficiency, the level of industry concentration

is no longer relevant since these firms reap the fruits of competitive products, an efficient

process, and continue to exploit growth opportunities.

When a firm begins the aging process it is better off in a concentrated industry

where the cost of entry is higher, allowing it to maintain substantial market share even if

products are no longer as competitive. Therefore, aging/old firms in medium to highly

concentrated industries perceive slightly higher returns than fellow aging firms in low

concentration industries. Consistent with the predictions of Jovanovic's model, firms in

highly concentrated industries have higher variance in their returns, however returns are

not higher for bigger firms in a concentrated industry as suggested by Jovanovic 1982,

but rather for mature firms enjoying the fruits of establishing successful products, while

being efficient and retaining some growth opportunities.

In Table 2.11 intersection of Invested Capital Turnover (efficiency) and firm age

is pinpointed. Every June stocks are sorted into Turnover terciles and firm age. On

average, efficient firms (high turnover) earn higher returns 1.59% vs. 1.18% compared to

low efficiency firms. The raw return difference between mature vs. young/old firms'

portfolios is between 26 and 49 basis points, or between 18 and 38 basis points in excess

to Fama and French. Furthermore, mature efficient firms have significantly higher returns

than firms with low turnover, excess returns for Fama and French portfolios are between

92

21 and 41 bps. Consistent with previous regression results, average firm size of high

turnover firms is smaller than that of low turnover firms. The fact that inefficient mature

firms have higher rates of return than comparable inefficient firms that are either

younger/older, may be because mature firms are less prone to delisting while being able

to profit on established product lines and growth opportunities; however, not necessarily

in the most efficient way.

Firms are sorted even further using the difference between return on invested

capital (ROIC) and the weighted average cost of capital (WACC), and firm age.

Shareholder value is created if firms are able to generate returns on investment in excess

to their cost of capital. Hence, a positive spread between ROIC and WACC is a form of

"Value Creation" while a negative spread depicts "Value Destruction." ROIC by is

estimated by multiplying Net Operating Profit After Tax (NOPAT) and Invested Capital

Turnover.

Table 2.12 sorts stocks into three "Value Creation" buckets and firm age every

June and examines portfolios average monthly returns. Surprisingly, overall returns

within "Value Creation" remain fairly constant across terciles. However, only the high

"Value Creation" tercile has positive significant excess returns to Fama and French

portfolios, 22 basis points. The average spread between ROIC and WACC for low Low

value creators (actually "value destructors") is -20%, young firms destruct at rates up to -

29.3%. As firms mature, average destruction declines monotonically to -10.69% for old

firms. Medium value creators earn an average spread of 1% and the average spread of

high value creators is 17.60% which is as high as 20.45% for young and mature firms and

declines to 15% for old firms.

93

Results become even more interesting when looking at young firms (seven or less

years old). In such an instance, high value creators earn high returns, 1.63% monthly raw

return or 44 basis points in excess to Fama and French portfolios. In contrast, mid-value

creators and value destructors earn a low raw return of .94% or -.39 basis points in excess

of Fama and French portfolios. The spread between young value creators and value

destructors is a raw 70 monthly bps of 83 bps in excess of Fama and French portfolios. It

appears that value destruction affects young firms the most because such firms are not

strong enough to survive an inability to cover their cost of capital; hence, they will likely

default and/or be delisted. It is important to take into consideration that these firms have a

very high value destruction rate of-26.8%.

Once firms move towards maturity and progress in age further, returns for value

creators vs. value destructors are not that different. Moreover, in some cases, it would

even seem that value destructors earn higher returns. However upon taking a closer look

three key aspects can be identified: 1) Value creators are the only firms to earn significant

excess returns to Fama and French portfolios, in contrast young and old value destructors

have significant negative excess returns. 2) Sharpe ratios are consistently higher for value

creators than value destructors across all age groups. 3) Value creator firms have

consistently higher average market values. Mature and aging firms that are value

destructors may be experiencing one very bad year where they experience big losses and

write downs. However, they are strong enough to withstand such a year and

simultaneously rebound the following year showing good returns for the holding period.

Combining all results to this point can lead to multiple investing strategies. In the

simplest form a portfolio manager would overweight mature firms vs. young/old ones

94

when forming a portfolio. Furthermore, the relation of firm age portfolio returns to

industry, size, book-to-market, efficiency, default/delisting probabilities, industry

concentration, and liquidity constraints, would allow investors to form long-short

strategies enhancing traditional factors that carry premiums such as size and value.

A more ambitious project would be to incorporate all previously defined

fundamental variables into a systematic valuation approach: for example combining age,

size and value to estimate a firm's sales growth rate, gross margins, default probability,

etc. Table 2.13 simply illustrates the benefits of incorporating sales growth estimates in a

systematic form to re-estimate equity duration. Instead of merely assuming sales growth

to mean reversal over the next ten years as suggested by Dechow et al. when computing

equity duration, sales growth rate estimates are used (having been computed based on

firm's age and industry group).

Results yield a larger spread on average equity duration, which decreases

monotonically from 35.85 for young firms to 13.87 for old ones, compared to the

previous equity duration spread that was between 17 and 14.6. The dramatic increase in

equity duration for young firms is not surprising as this is linked with high growth rates,

and implies that for young firms duration will tend to be higher since the horizon in

which an investor receives cash flows is farther apart. As expected our decomposition

shows equity duration decreases with size (bigger firms also tend to pay higher dividend

yields which would shorten the duration). Finally, "new " equity duration increases with

book-to-market ratio, this could be due to "new" duration capturing distresses effects

from value firms.

95

2.7 Conclusions

Throughout this paper we showed that firm age is relevant to understand firm's

evolution in time and changes in its fundamentals as well as expected returns. As firms

mature returns have a curvature that classical finance models are not be able to explain.

Age portfolios yield lower returns when firms are in their youth, returns rise as firms

mature and then gradually decline as they begin their aging process, while volatility is

high for young firms and gradually declines with aging. Over four decades mature firms

have persistent excess returns to size & value, or industry portfolio returns that range

between 20 and 30 monthly basis points.

In youth firm's present very high uncertainty, their efforts are geared towards

product innovation, developing few highly original products, and growth. Successful

young firms can grow sales and assets at rates of 50% or more. Tobin's Q is on average

2.8 and as high as 7.7 for young firms. Growth comes with a high cost, young firms incur

in high expenses to innovate resulting in low and even negative profitability an average

ROA of 6% below comparable industry peers. Young firm's higher illiquidity, low return

on invested capital, low debt rating, and longer investment horizon, contribute to high

probabilities of delisting and default.

In Maturity firm's consolidate growth opportunities and become process efficient

doubling their gross margins and significantly decreasing their default and delisting

probabilities. As firms begin an aging process, innovation and growth opportunities will

drop significantly below industry averages. Firms will start to resemble cash cows as

their cash distributions substantially increase, dividend yield for old firms is on average

3.3% and up to 6.3%, while their default probability drops significantly.

96

We take a look at the puzzling low returns for young firms, and find that young

firms competing in highly concentrated industries seem to experience lower returns than

young ones competing in fragmented industries where they are able to have bigger

market share. Returns of young firms that are "value destructors" (ROIC - WACC) are

significantly low -39 basis points below comparable size and value portfolios, compared

to 44 basis points in excess for successful young "value creators," an impressive monthly

spread of 84 basis points. Furthermore, medium and high liquidity young firms are the

biggest underperformers by far, between -49 and -120 monthly basis points in excess to

size and value portfolios. It seems that the low performance of young firms can be

attributed to hyped firms (highly liquid) that fail to meet market expectations (unable to

create value above their cost of capital) and that are unable to establish market share

when competing in concentrated industries. This will result in high levels of delisting and

default by these young firms hampering the overall performance of the group.

Finally, we conclude that firm age has deep relevance to produce better forms of

modeling firms in terms of expected sales growth, margins, etc. Traditional valuation

mechanisms try to forecast firms' future cash flows, analysts look at past growth

realizations and extrapolate from all their information future growth rates. However,

analysts usually don't take into consideration the firm's growth potential based on the

firm's maturity, and do not follow a systematic way to estimate growth. Modeling firms

based on their maturity and industry could provide a systematic approach for better

valuations as exemplified in our re-estimated equity duration measure.

97

Graph 1. Annualized Firm Age Portfolios Returns

(ST) (8-E) (B-fl) (&2S) (2635) (36-55) (56-75) (76-UO) «©D

Avg BW Return * Avg VW Return

Graph 2. Annualized Firm Age Portfolios Standard Deviation

(5-7) (8-E) (Q-B) (B-25) (2&35) (36-55) (56-75) (76-00) *C0

— B / V Pbrt Vol — * — V W Fort Vol

Figures 2.1 and 2.2, Annualized Firm Age Portfolio's Returns and Standard Deviations

Graph 3. Firms Book-to-Market Ratio by Firm Age

4<= (5-7) (8-12) (13-18) (19-25) (26-35) (36-55) (56-75) (76-100) +100

Firm Age

—•—Average x Median

Figure 2 3 Firms Book-to-Market Ratio by Firm Age

Graph 4. Firm Sample as a Percentage of Total CRSP Firms

90%

1965 1970 1975 1

• % of Total CRSP Firms - » - % of Total Market Value

Figure 2.4 Firm Sample as a Percentage of Total CRSP Firms

Table 2.1: Firm Sample and Summary Statistics

The sample includes all NYSE-, AMEX-, and NASDAQ-listed securities with share codes 10 or 11 that are contained in the intersection of the CRSP monthly returns file, the COMPUSTAT industrial annual file, and the Founding Dates Dataset, between July 1965 and December 2006. We restrict the sample firms to those that will be listed at least 3 years after their first listing year on CRSP, and exclude firms under 1.5 years old. First column identifies year t which starts in July t and finishes in June of t+1; the second computes the number of firms in the sample; the third computes the total market value of the firms in the sample; finally, the fourth and fifth columns compute the percentage of CRSP firms and percentage of market value covered by our sample.

Year

1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Firms in

Sample 1,360 1,375 1,411 1,415 1,439 1,535 1,581 1,634 1,690 2,891 2,870 2,900 2,903 2,803 2,735 2,678 2,671 2,817 2,832 3,192 3,353 3,410 3,710 3,893 3,884 3,863 3,882 4,006 4,260 4,661 4,971 5.195 5,549 5,598 5,417 5,246 5,033 4,763 4,482 4,221 4,004 3,762

Sample ME

447,275 465,767 518,988 588,447 585,360 445,346 641,453 714,663 680,054 614,751 711,893 787,934 796,921 792,818 869,242 975,777

1,166,045 955,280

1,557,687 1,357,149 1,723,369 2,240,514 2,627,923 2,347,225 2,625,506 2,874,141 2,997,809 3,462,893 4,061,595 4,119,323 5,125,803 6,467,899 8,413,336 11,033,217 13,158,496 14,902,041 13,022,912 10,749,133 10,664,529 12,733,354 13,355,879 13,982,362

% of Total

CRSP Firms 66.21% 66.17% 67.51% 67.80% 66.90% 67.92% 68.32% 66.86% 32.41% 60.27% 62.61% 62.74% 62.14% 61.85% 60.85% 59.38% 55.11% 56.26% 55.49% 55.61% 59.19% 59.42% 60.31% 63.37% 66.07% 67.02% 68.95% 69.26% 70.96% 70.71% 74.29% 73.04% 75.20% 77.17% 80.79% 80.62% 84.83% 88.12% 89.96% 87.65% 84.15% 80.14%

% of Total

Market Value 93.79% 93.19% 93.03% 92.21% 91.58% 93.61% 93.56% 92.90% 83.92% 92.92% 92.68% 92.88% 92.34% 91.69% 91.70% 91.00% 88.92% 89.20% 88.59% 86.50% 89.84% 89.88% 91.05% 91.86% 93.36% 94.03% 93.99% 93.72% 93.24% 92.53% 93.41% 92.24% 93.82% 95.01% 95.61% 94.18% 97.00% 97.12% 96.60% 95.84% 94.71% 93.38%

99

Table 2.2: Expected monthly returns by age group and industry

The sample covers on average 3000 firms per year from 1965 to 2006. Firms are grouped in ten age categories based on the firm's age (current year - founding/incorporation date). Panels A and B present average monthly returns for equal and value weighted portfolios, ^-statistics, standard deviations, maximum and minimum returns, annualized returns, Sharpe ratio (excess return to unit of risk), average number of firms within each group and average firm size. The panel also presents returns to a simple long/short strategy which longs mature firms (13 to 25 years old) and shorts young or old firms. Panel C presents higher moments for both equal and value weighted portfolios, skewness, kurtosis, and an adjusted measure (which excludes the top/bottom 1% of the distribution) for skewness, kurtosis and Sharpe ratio computed with the adjusted average returns and standard deviations. Panel D shows excess returns to size and book-to-market quintile portfolios computed following Daniel and et al 1997. As a robustness check Panel E presents raw and excess returns for age deciled portfolios, instead of using the standard age breakpoints defined in this paper, every year we group firms in deciles based on their age. The first column of Panel E presents the average firm age per decile, all other columns follow previous definitions. Panel F presents industry excess returns to 12 and 48 industry portfolios, firms are classified using the sic classification found in Kenneth French's website. Panel G explores further industry returns by grouping firms in 12 industrial sectors and subsequently classifies each firm in five age groups (we shrinked the number of age groups to allow sufficient firms in young and early maturity firms). The Panel presents average return, ^-statistic, Sharpe ratio, and average number of firms. Finally, Panel H breaks the sample in four time periods and age groups, and presents raw returns, Excess return to Fama and French portfolios (FF Excess), Excess returns to 48 Industry portfolios (Ind Excess), all with their corresponding t-statistics.

Panel A: Equal Weighted Portfolios

Firm Age

4<= (5-7)

(8-12) (13-18) (19-25) (26-35) (36-55) (56-75) (76-100)

+100 Mature - Young

Mature - Old All Firms

Avg Return

1.26% 1.45% 1.52% 1.71% 1.62% 1.55% 1.42% 1.27% 1.24% 1.24% 0.45% 0.47% 1.44%

t

3.36 4.00 4.40 5.31 5.50 5.81 6.06 5.97 6.13 6.14 2.24 2.45 5.56

SD

0.083 0.081 0.077 0.072 0.066 0.059 0.052 0.048 0.045 0.045 0.044 0.043 0.058

Max

44.27% 38.73% 33.27% 35.23% 30.51% 29.41% 27.84% 25.69% 25.64% 21.60% 22.26% 25.90% 29.10%

Min

-30.47% -31.99% -30.94% -30.71% -29.60% -28.64% -27.40% -24.67% -24.25% -23.16% -42.86% -13.36% -28.20%

Annulized

Return 15.13% 17.41% 18.22% 20.49% 19.45% 18.58% 17.08% 15.28% 14.89% 14.89% 5.38% 5.60% 17.22%

Sharpe

Ratio 0.525 0.620 0.683 0.824 0.854 0.902 0.941 0.927 0.952 0.954 0.350 0.380 0.863

Avg # of

Firms 95 189 388 428 365 352 477 376 326 283 521 711

3,278

Avg Size

232 215 227 318 576 711 711

1,388 1,822 3,960 272

2,139 1,005

Panel B: Value Weighted Portfolios

Firm Age

4<= (5-7) (8-12)

(13-18) (19-25) (26-35) (36-55) (56-75) (76-100)

+100 Mature - Young


Avg Return

0.51% 0.99% 0.90% 1.06% 1.20% 1.08% 1.02% 0.93% 0.92% 1.03% 0.69% 0.17% 097%

t

1.39 2.70 269 345 4.06 4.13 4.62 4.65 5.01 5.48 2.92 0.90 4.78

SD

0.082 0.082 0.075 0.068 0.066 0.058 0.049 0.045 0.041 0.042 0.052 0.043 0.045

Max

28.62% 29.70% 25.83% 21.52% 24.11% 21.57% 19.77% 18.52% 16.39% 19.94% 22.54% 24.65% 17.20%

Min

-36.89% -31.18% -31.32% -29.86% -26.19% -25.87% -22.99% -21.94% -21.69% -19.49% -25.11% -19.33% -22.00%

Annulized Return 6.17% 11.94% 10.85% 12.70% 14.44% 12.99% 12.18% 11.19% 11.00% 12.37% 8.24% 2.07% 11.61%

Sharpe

Ratio 0.217 0.419 0.417 0.535 0.630 0.641 0.717 0.722 0.778 0.850 0.457 0.140 0.742

Avg # of

Firms 95 189 388 428 365 352 477 376 326 283 455 648

3,278

Avg Size

232 215 227 318 576 711 711

1.388 1.822 3,960 398

2,268 1,005

Panel C: Higher Moments of Equal & Value Weighted Portfolios

Firm Age

4<= (5-7)

(8-12) (13-18) (19-25) (26-35) (36-55) (56-75)

(76-100) +100

Mature - Young Mature - Old

All Firms

Skew

0.42

0.28 0.13 -0.06

-0.06 -0.23 -0.36

-0.33 -0.34 -0.34 -2.14 0.89 -0.20

Equal Weighted Port

Kurt

2.52

2.13 2.14 2.17 2.37 2.70 3.55

3.66 4.05 2.74

20.78 4.01 2.90

Adj-Skew

0.23

0.13 0.05 -0.07 -0.12 -0.17 -0.35 -0.39 -0.38 -0.24 -0.75 0.51 -0.21

Adj-Kurt

0.82 0.39 0.99 0.41

0.68 0.65 0.84 1.00 0.71

0.50 3.24

1.12 0.49

Adj-SR

0.54

0.64 0.70 0.86 0.88 0.95 0.99 0.97 1.01 1.01 0.43 0.38 0.91

Skew

-0.29 -0.15 -0.32 -0.41 -0.24

-0.36 -0.21 -0.41 -0.31 -0.15 -0.17 0.12 -0.35

Value Weighted

Kurt

2.20 2.06 2.00 1.20 1.31

1.61 1.56 2.15 2.55 2.03 2.84 2.57 1.91

Adj-Skew

-0.05 -0.03 -0.32 -0.26 -0.20 -0.24 -0.08

-0.29 -0.03 -0.09 0.16 0.15 -0.20

Port

Adj-Kurt

0.61 1.17 1.34 0.29 0.62 0.46 0.08

0.79 0.54 0.43 0.57

0.43 0.44

Adj-SR

0.25 0.44

0.42 0.56 0.65 0.68 0.76 0.75 0.83 0.89 0.41

0.02 0.78

Panel D: Equal Weighted Portfolios - Excess Returns to Size and B/M

Firm Age

4<= (5-7)

(8-12) (13-18) (19-25) (26-35) (36-55) (56-75)

(76-100) +100

Mature - Young Mature • Old

All Firms

Avg Return

1.01% 1.29% 1.46% 1.67% 1.61% 1.58% 1.42% 1.24% 1.23% 1.22% 0.66% 0.44% 1.39%

t

2.57 3.43 4.14 5.15 5.45 5.89 6.03 5.84 6.09 6.16 3.01 2.25 5.32

SD

0.088 0.084 0.079 0.072 0.066 0.060 0.052 0.048 0.045 0.044 0.049 0.044 0.058

Excess

Return

-0.33% -0.04% 0 . 1 1 % 0.28% 0.25% 0.19% 0.04% - 0 . 1 1 % -0.08% -0.03% 0 . 6 1 % 0 . 3 1 % 0.06%

* r. „ r Annulized t - Excess SD Excess Excess -1.64 -0.31 1.23 4.44 5.06 4.10 0.82 -1.76 -1.20 -0.49 2.84 2.82 5.61

0.046 0.028 0.019 0.014 0.011 0.010 0.011 0.014 0.016 0.015 0.048 0.024 0.002

-4.02% -0.48% 1.29% 3 .31% 2.96% 2.23% 0.48% -1.28% -1.00% -0.40% 7.33% 3 .71% 0.67%

Avg # of

Firms

100 193 384 419 353 337 443 347 305 265 519 684

3,146

Avg Size

248 218 231 316 579 724 735

1,458 1,904 4,093 282

2,204 1,022

Panel E: Equal Weighted Age Deciles - Excess Returns to Size and B/M

Average Firm Age Avg Return

6.3 11.7 17.5 23.6 29.9 38.6 48.9 63.5 82.9

122.S Mature - Young


1.19% 1.51% 1.59% 1.50% 1.52% 1.41% 1.44% 1.29% 1.20% 1.23% 0.40% 0.37% 1.39%

t

3.25 4.32 4.92 5.13 5.40 5.67 6.17 5.96 5.96 6.21 3.60 1.77 5.32

SD

0.082 0.078 0.072 0.065 0.063 0.055 0.052 0.048 0.045 0.044 0.025 0.046 0.058

Sharpe Ratio 0.505 0.671 0.764 0.796 0.839 0.880 0.958 0.925 0.925 0.963 0.558 0.275 0.825

Excess Return -0.12% 0.18% 0.26% 0.17% 0.14% 0.06% 0.09% -0.07% -0.11% -0.03% 0.38% 0.30% 0.06%

Excess - Excess

Sharpe -1.10 2.07 3.91 3.45 3.09 1.47 1.94

-1.17 -1.61 -0.48 3.67 2.42 5.61

-0.171 0.322 0.607 0.536 0.479 0.228 0.301 -0.182 -0.250 -0.075 0.570 0.347 0.376

Annulized Excess -1.43% 2.12% 3.16% 2.05% 1.67% 0.71% 1.06% -0.81% -1.30% -0.39% 4.58% 3.54% 0.67%

Avg # of

Firms 310 311 312 309 318 318 314 316 319 319 622 631

3,146

Avg Size

231 227 262 410 469 925 810

1,185 1,769 3,847 246

2,054 1,022

Panel F: Equal Weighted Age Deciles - Excess Returns to Industry

Average Firm Age

4<= (5-7)

(8-12) (13-18) (19-25) (26-35) (36-55) (56-75)

(76-100) +100


; Excess

Return -0.38% -0.11% 0.01% 0.21% 0.17% 0.17% 0.04% -0.08% -0.11% -0.09% 0.59% 0.30%

12 Industrial Sectors

-1.78 -0.88 0.10 3.24 3.42 3.35 1.00

-1.55 -1.80 -1.20 2.78 2.43

Excess Annulized

Sharpe -0.276 -0.137 0.016 0.503 0.531 0.521 0.154 -0.241 -0.280 -0.186 0.431 0.378

Excess -4.53% -1.38% 0 12% 2.56% 2.06% 2.07% 0.54% -1.01% -1.35% -1.08% 7.08% 3.63%

Excess

Return -0.33% -0.12% 0.01% 0.23% 0.16% 0.16% 0.04% -0.09% -0.11% -0.11% 0.57% 0.35%

48 Industries

Excess

-1.62 -0.99 0.12 3.81 3.50 3.59 0.91 -1.75 -1.81 -1.63 2.78 3.03

Sharoe -0.252 -0.154 0.018 0.591 0.543 0.558 0.141 -0.272 -0.281 -0.253 0.431 0.471

Annulized

Excess -4.02% -1.46% 0.12% 2.80% 1.89% 1.95% 0.50% -1.09% -1.27% -1.35% 6.82% 4.15%

Avg # of

Firms 100 193 384 419 353 337 443 347 305 265 519 684

Avg Size

248 218 231 316 579 724 735

1,458 1,904 4,093 282

2,204

101

Panel G: Equal Weighted Portfolios, by Industry

Non-Dur*

Durables

Manufac

Energy

Chem*

Buss Equip

Telecom*

Utilities

Shops*

Health

Finance*

Other

Firm Age

Avg Return f

Sharpe Ratio Avg # of Firms

Avg Return f


Avg Return t


Avg Return t


Avg Return f


Avg Return t


Avg Return t


Avg Return t


Avg Return t


Avg Return t


Avg Return t


Avg Return t


9<=

0.89% 2.25

0.3499 21

0.58% 7.76

0.1807 9

0.84% 2.26

0.3514 25

1.45% 3.31

0.5142 19

1.05% 1.89

0.2938 6

1.25% 2.49

0.3861 103

1.46% 2.70

0.4240 19

0.92% 7.69

0.3376 4

0.99% 2.49

0.3862 45

1.42% 3.22

0.5122 71

1.07% 3.66

0.5675 51

1.28% 3.27

0.4982 79

(10-20)

1.26% 4.11

0.6375 40

1.37% 3.29

0.5163 13

1.64% 4.65

0.7224 58

1.64% 4.24

0.6586 28

1.27% 3.54

0.5500 12

1.90% 4.39

0.6811 200

1.64% 4.29

0.6652 21

1.53% 3.97

0.6070 4

1.44% 4.63

0.7183 89

1.66% 4.31

0.6780 103

1.46% 4.95

0.7690 90

1.68% 5.12

0.7948 110

(21-35)

1.37% 4.89

0.7597 45

1.41% 3.20

0.4967 14

1.60% 5.37

0.8332 68

1.73% 4.71

0.7317 26

1.70% 6.14

0.9525 15

1.76% 4.74

0.7355 119

1.95% 5.76

0.8215 9

1.15% 5.04

0.7823 10

1.64% 5.57

0.8651 77

1.83% 5.53

0.8585 41

1.69% 6.08

0.9675 69

1.57% 5.45

0.8458 84

(36-55)

1.21% 5.10

0.7915 55

1.60% 5.22

0.8097 17

1.43% 5.37

0.8237 80

1.44% 4.67

0.7252 24

1.40% 5.30

0.8229 17

1.65% 4.74

0.7351 46

1.29% 3.98

0.6179 5

1.02% 6.04

0.9372 22

1.41% 5.36

0.8319 66

1.69% 5.52

0.8565 11

1.45% 6.04

0.9378 44

1.48% 5.71

0.8857 57

+56

1.25% 5.83

0.9057 157

1.19% 4.49

0.6973 32

1.23% 5.08

0.7883 188

1.32% 4.96

0.7694 32

1.19% 5.44

0.8452 39

1.28% 4.50

0.6988 36

1.21% 5.73

0.7957 11

0.99% 5.92

0.9195 97

1.22% 5.07

0.7869 83

1.36% 6.09

0.9460 22

1.37% 5.97

0.9169 143

1.25% 5.22

0.8107 75

Mature -Younq

0.48% 7.84

0.2861 66

0.72% 7.75

0.2742 23

0.80% 3.84

0.5966 83

0.18% 0.66

0.1027 48

0.65% 7.35

0.2094 20

0.65% 2.98

0.4622 303

0.54% 7.23

0.1983 29

0.79% 7.75

0.2297 9

0.66% 3.03

0.4706 123

0.33% 7.36

0.2165 176

0.48% 2.32

0.3688 122

0.39% 7.88

0.2911 189

Mature -Old

0.12% 0.86

0.1339 202

0.23% 0.87

0.1266 46

0.41% 2.73

0.3311 246

0.31% 7.32

0.2046 61

0.51% 2.87

0.4359 54

0.62% 2.62

0.4063 236

0.74% 2.49

0.3963 21

0.54% 7.50

0.2331 101

0.42% 3.77

0.4828 161

0.30% 7.07

0.1688 126

0.28% 1.67

0.2660 218

0.43% 2.50

0.3885 185

All Firms

1.23% 5.23

0.8117 318

1.28% 4.40

0.6825 86

1.35% 5.77

0.7940 419

1.49% 4.68

0.7268 129

1.30% 5.56

0.8630 88

1.68% 4.26

0.6606 504

1.58% 4.65

0.7219 64

1.03% 6.23

0.9674 136

1.36% 4.97

0.7617 362

1.58% 5.00

0.7760 242

1.40% 5.89

0.9144 394

1.45% 5.76

0.8005 404

Pan

el H

; E

qu

al W

eig

hte

d P

ort

folio

s, b

y T

ime

Per

iod

s

Firm

Age

1965

-197

5 A

vg R

etur

n

f S

harp

e R

atio

FF

Exc

ess

Ret

urn

f-

F

F E

xces

s In

d E

xces

s R

etur

n

f-

Ind

Exc

ess

Avg

# o

f Fi

rms

1976

-198

5 A

vg R

etur

n

t S

harp

e R

atio

FF

Exc

ess

Ret

urn

r-

F

F E

xces

s In

d E

xces

s R

etur

n

(-

Ind

Exc

ess

Avg

# o

f F

irms

1986

-199

5 A

vg R

etur

n

t S

harp

e R

atio

FF

Exc

ess

Ret

urn

t-

F

F E

xces

s In

d E

xces

s R

etur

n

(-

Ind

Exc

ess

Avg

# o

f Fi

rms

1996

-200

6 A

vg R

etu

rn

t S

harp

e R

atio

FF

Exc

ess

Ret

urn

r-

F

F E

xces

s In

d E

xces

s R

etur

n

f-

Ind

Exc

ess

Avg

# o

f Fi

rms

7<=

0.91

%

10

6 0.

3352

-0

.06%

-0

.26

-0.0

2%

-0.0

9 58

1.73

%

2.38

0.

7569

-0

.18%

-0

.65

-0.1

4%

-0.4

7 15

8

0.73

%

1.27

0.

4045

-0

.10%

-0

.78

-0.3

8%

-2.7

5 44

4

1.25

%

1.60

0.

4720

-0

.23%

-0

.81

-0.2

4%

-10

6 48

1

(8-1

2)

1.07

%

1.31

0.

4168

0.

14%

0.

77

0.19

%

0.89

76

1.90

%

2.95

0.

9358

-0

.02%

-0

.15

-0.1

2%

-0.9

2 24

2

1.11

%

19

4 0.

6174

0.

20%

1.

61

-0.0

8%

-0.6

0 50

4

1.64

%

2.18

0.

6431

0.

12%

0.

56

0.06

%

0.30

66

9

(13-

18)

1.24

%

15

5 0.

4929

0.

27%

1

62

0.38

%

19

3 10

3

2.19

%

3.58

1.

1364

0.

23%

1

99

0.19

%

18

4 27

7

1.31

%

2.41

0.

7655

0.

28%

2.

44

0.15

%

18

3 42

1

1.79

%

2.87

0.

8473

0.

30%

3.

01

0.20

%

2.46

81

1

(19-

25)

1.14

%

1.61

0.

5123

0.

31%

2.

42

0.23

%

16

5 10

0

2.24

%

3.84

1.

2184

0.

32%

3.

28

0.20

%

2.32

25

4

1.33

%

2.62

0.

8309

0.

24%

2.

81

0.15

%

2.44

42

5

1.63

%

2.90

0.

8546

0.

14%

1

69

0.07

%

1.10

59

6

(26-

35)

1.13

%

16

3 0.

5178

0.

29%

2.

36

0.22

%

1.91

10

4

2.07

%

3.98

1.

2631

0.

08%

1

21

0.15

%

2.49

30

3

1.28

%

2.72

0.

8640

0.

12%

1

84

0.09

%

13

0 36

6

1.72

%

3.75

1.

1052

0.

23%

2.

44

0.18

%

17

3 54

1

(36-

55)

0.80

%

13

4 0.

4261

-0

.02%

-0

.31

-0.0

5%

-1.0

1 32

0

1.96

%

4.19

1.

3301

0.

06%

1

06

0.06

%

1.10

43

8

1.30

%

3.05

0.

9688

0.

11%

1

39

0.14

%

17

5 41

9

1.52

%

3.83

1.

1306

0.

01%

0.

05

0.02

%

0.17

57

4

(56-

75)

0.64

%

12

2 0.

3880

-0

.11%

-1

.41

-0.1

4%

-18

2 26

2

1.87

%

4.27

1.

3572

0.

02%

0.

27

-0.0

1%

-0.1

2 38

4

1.15

%

2.84

0.

9006

-0

.10%

-1

25

-0.0

3%

-0.3

2 37

8

1.26

%

3.63

1.

0704

-0

.20%

-1

.10

-0.1

6%

-1.1

5 36

0

+76

0.66

%

13

6 0.

4328

-0

.03%

-0

.36

-0.1

2%

-10

0 29

2

1.73

%

4.29

1.

3630

0.

01%

0.

20

-0.1

6%

-16

3 54

2

1.21

%

3.03

0.

9627

-0

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-0

.64

0.01

%

0.07

62

0

1.24

%

3.95

1.

1662

-0

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-0

.73

-0.1

5%

-0.9

8 79

1

Mat

ure

-Y

oung

0.34

%

12

3 0.

3894

0.

33%

1.

22

0.41

%

16

3 16

1

0.46

%

13

2 0.

4185

0.

41%

1

32

0.33

%

11

0 43

5

0.58

%

3.53

1.

1203

0.

37%

2.

52

0.53

%

3.48

86

5

0.53

%

2.10

0.

6202

0.

53%

2.

05

0.44

%

2.16

1,

292

Mat

ure

-O

ld

0.58

%

14

2 0.

4514

0.

30%

1

41

0.50

%

1.71

39

4

0.47

%

16

1 0.

5111

0.

22%

1

49

0.34

%

1.92

81

9

0.10

%

0.40

0.

1276

0.

31%

2.

05

0.15

%

0.99

1,

041

0.55

%

11

7 0.

3450

0.

46%

1.

54

0.35

%

15

8 1,

602

All

Firm

s

0.83

%

1.38

0.

4366

1,31

5

1.92

%

3.84

1.

2182

2,59

9

1.18

%

2.49

0.

7900

3,57

6

1.51

%

2.96

0.

8734

4,82

3

103

20%

19%

• 18%

% 17%

"g 16%

• 15%

< 14%

13%

12%

Graph 5. Firm Age Decile Portfolios

6 3 11.7 17.5 23.6 29.9 38 6 48.9

Average D e d l e Age

63.5 82.9 122.5

Graph 6. Firm Age Deciles Returns in Excess to Size and B/M

4 % -

3%

E 2% -

f 1% 3 M

- i %

/ \ / V_^

6.3 11.7 17 5 23 6 29.9 38 6 48 9 63.5 82.9

Average Decile Age

122.5

Figures 2.5 and 2.6, Firm Age Decile Portfolio Returns Raw and in Excess to Size and Book-Market

0.30% -I

0.20%

0.10% -

0.00% •

-0.10% -

-0.20%

-0.30% -

Graph 7. Monthly Excess Return to Industry

/'S^.. / "«=:==J'S.

••^/- ^ s ^

gr >•**_ _., —-•

/ 4

4<= (5-7) (8-12) (13-18) (19-25) (26-35) (36-55) (56-75) (76-100) +100

• 12 Ind Sectors 48 Industries

Figure 2.7 Firm Age Portfolio Excess to Industry Returns

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106

Table 2.4: Firm Age and Growth Rates

The table examines the relation of firm age with respect to three growth measures: a) 1 year Asset Growth, b) 1 year Sales Growth, c) Tobin's Q proxy (market value of Assets / Book Value of Assets). For each measure we present two panels: The first panel presents the average from 1965 to 2006 of yearly mean, standard deviation, bottom 5th and top 95"1 percentiles, and median estimates of the fundamental measure. In addition the panel presents the measure's excess to industry which is computed in the following form: 1) compute the industry average following Fama and French 2000, 48 industry classification; 2) subtract the industry average at the firm level from the raw measure; 3) group the firms by age and compute the average and median of the of the excess to the industry measure (columns 7 and 8 of the panel). The second panel presents the results of the regression decomposing the measure's excess to the industry by firm age, listing cohorts, and year effects following Deaton 1997, and the Fama MacBeth average estimates for yearly cross-sectional regressions. Finally, for each measure we present graphs of the measure's decomposition into age, listing cohort, and year effect. As a robustness check in all our regressions we add the firms size (log(ME)) and book-to-market ratio (log(BVME)) and interact those variables with the age (log(Age)) variable. T statistics are reported under the coefficient estimates in parenthesis.

Asset Growth

Firm Age

4<= (5-7) (8-12)

(13-18) (19-25) (26-35) (36-55) (56-75)

(76-100) +100

Mean

0.295 0.276 0.215 0.197 0.154 0.142 0.119 0.109 0.101 0.098

SD

0.524 0.506 0.428 0.377 0.311 0.283 0.241 0.216 0.196 0.179

Bottom 5%

-0.230 -0.294 -0.271 -0.233 -0.192 -0.164 -0.135 -0.116 -0.099 -0.087

Med

0.155 0.147 0.111 0.119 0.096 0.091 0.081 0.075 0.071 0.070

Top 95%

1.452 1.454 1.095 0.933 0.704 0.619 0.481 0.441 0.385 0.368

Mean Excess 0.105 0.099 0.044 0.030 -0.006 -0.010 -0.022 -0.026 -0.032 -0.032

Med Excess -0.019 -0.017 -0.041 -0.036 -0.053 -0.050 -0.051 -0.048 -0.052 -0.051

Avg # of Firms

45 146 345 400 349 334 445 346 304 265

Asset Growth Age, Listing Cohorts, & Year Effects Decomposition

Intercept

-0.0672 (3.78)

-0.1497 (7-12)

-0.6617 (26.40)

Log (Age)

08540 (16.58; 0.6431 (12.52) 1.3729 (24.47)

Log (Age2)

-0.4228 ( (7 .22; -0.3293 (13.46) -0.6216 (23.71)

Log (ME)

0.0334 (49.54) 0.1211 (48.15)

Log (BVME)

-0.0312 (22.07) -0.0327 (6.46)

Log (Age) Loo (MEI

-0.0250 (35.65)

Log (Age) Loo. (BVME)

0.0003 (0.17)

ft2

0.0142

0.0517

0.0635

Obs

125.082

121.207

121,207

Cohort

Effects Yes

Yes

Yes

Fama MacBeth Average Estimates

Intercept

0.0099 (0.69)

-0.0440 (2.89)

-0.1566 (6.84;

-0.5864 (9.64)

Log (Age)

0.6151 (5.98; 0.5401 (5.56; 03367 (3.67; 0.9154 (7.11)

Log (Age2)

-0.3130 (6.20;

-O.2709 (5.70;

-0.1789 (3.96)

-0.4026 (6.83;

Log (ME)

0.0267 (9.22) 0.1076 (9.89)

Log (BVME)

-0.0369 ( (0 .9 ) ; -0.0616 (3.85)

Log (Age) Log (ME)

-0.0228 (9.06)

Log(Age) Log (BVME)

0.0069 (1.72)

R2

0.0159

0.0210

0.0750

0.0904

Avg # of Firms 2975

2975

2887

2887

Cohort Effects

No

Yes

Yes

Yes

Gfowth in Exc«ts to Industry by Firm A Listing D*cad« Cohort Effects

- i o n .

/

ISM

- \

1971 1

Y w EfUcta

/V / \

76 1W1 19H

A

1981

\ V V '•» "

1 I 01 3006

Sales Growth

Firm Age

4<= (5-7) (8-12) (13-18) (19-25) (26-35) (36-55) (56-75) (76-100)

+100

Mean

0.339 0.317 0.248 0.205 0.154 0.135 0.118 0.105 0.097 0.095

SD

0.529 0.538 0.455 0.400 0.311 0.279 0.249 0.225 0.200 0.173

Bottom 5%

-0.212 -0.340 -0.304 -0.272 -0.219 -0.208 -0.171 -0.161 -0.130 -0.108

Med

0.194 0.183 0.156 0.137 0.112 0.104 0.091 0.081 0.077 0.077

Top 95%

1.371 1.584 1.128 0.904 0.620 0.548 0.466 0.423 0.372 0.354

Mean Excess 0.156 0.137 0.070 0.031 -0.010 -0.019 -0.024 -0.030 -0.035 -0.036

Med Excess 0.024 0.016 -0.009 -0.026 -0.042 -0.040 -0.043 -0.043 -0.047 -0.045

Avg # of Firms

21 109 301 370 333 321 432 336 296 259

Sales Growth Age, Listing Cohorts, & Year Effects Decomposition

Intercept

-0.1445 (7.65;

-0.2242 (10.17) -0.4560 (17.07)

Log (Age)

1.8503 (25.81) 1.5567 (24.28) 1.7720 (25.49;

Log (Age2)

-0.8136 (26.57; -0.7685 (25.03) -0.8437 (25.77)

Log (ME)

0.0137 (20. (2 ; 0.0485 08.34)

Log (BVME)

-0.0538 (36.82) -0.1559 (28.22)

Log (Age) Loo (ME)

-0.0093 02.78)

Log (Age) Log (BVME)

0.0316 0 9 . 0 6 ;

R2

0.0244

0.0494

0.0566

Obs

116,587

112,974

112,974

uonor t Effects

Yes

Yes

Yes

F a m a M a c B e t h A v e r a g e E s t i m a t e s

Intercept

-0.0718 (2.96)

-0.1151 (4.45)

-0.1981 (6.96;

-0.4612 (9.96)

Log (Age)

1.2963 (7.23; 1.2057 (7.28; 1.0800 (7.00) 1.3482 (7.93)

Log (Age2)

-0.6458 (7.38;

-0.5971 (7.40)

-0.5367

(7.U) -0.6326 (7.83)

Log (ME)

0.0124 (10.52) 0.0567 (10.21)

Log (BVME)

-0.0529 08.42) -0.1494 02.86)

Log (Age) Log (ME)

-0.0119 (8.52;


0.0292 (9.75;

R2

0.0230

0.0263

0.0631

0.0731

Avgf f of F i rms 2773

2773

2691

2691

Cohort Effects

No

Yes

Yes

Yes

Sal«s Growth In E X C M I to Industry by Firm Ag« Listing Docacte Cohort Effects

108 Q (Market Value of Assets / Book Value of Assets)

Firm Age

4<= (5-7) (8-12) (13-18) (19-25) (26-35) (36-55) (56-75) (76-100)

+100

Mean

2.801 2.465 2.187 1.925 1.737 1.548 1.422 1.345 1.302 1.320

SD

3.834 2.721 2.308 1.891 1.529 1.229 0.975 0.998 0.806 0.736

Bottom 5%

0.849 0.798 0.802 0.794 0.773 0.763 0.737 0.750 0.756 0.784

Med

1.785 1.658 1.477 1.371 1.275 1.197 1.153 1.101 1.087 1.096

Top 95%

7.714 6.944 5.732 4.761 4.190 3.434 2.999 2.702 2.564 2.700

Mean Excess 0.922 0.561 0.299 0.067 -0.046 -0.151 -0.161 -0.164 -0.192 -0.145

Med Excess 0.131 -0.037 -0.156 -0.215 -0.246 -0.271 -0.250 -0.213 -0.204 -0.155

Avg # of Firms

81 178 381 425 363 343 452 351 310 270

Q - Market Value of Assets / Book Value of Assets Age, Listing Cohorts, & Year Effects Decomposition

Intercept

0.1358 (1.09)

-1.2611 (11.86) -1.6744 (13.80)

Log (Age)

3.4730 (15.23; 3.1922 (16.27) 12699 (6.05)

Log (Age1)

-1.7683 (16.39; -1.5269 (16.46; -0.5044 (5.20;

Log (ME)

0.0004 (0.11) 0.0156 (»24 ;

Log (BVME)

-1.1287 (152.59; -2.7821

(111.96)

Log (Age) Log (ME)

0.0048 (1.38)


0.5347 (69.47)

R2

0.0132

0.1880

02217

Obs

132.414

129.661

129.661

Cohort Effects

Yes

Yes

Yes


Intercept

0.2178 (1.8S) 0.2077 (1-79)

-1 1776 (7.28)

-1.4850 (6.05;

Log (Age)

25836 (3.5); 21136 (2.98; 2.3577 (3.89; 0.1764 (0.37)

Log (Age2)

-1.3434 (3.75J

-1 0991 (3.18;

-1.1215 (3.87; 00203 (0.09;

Log (ME)

0.0026 (0.38J 0.0149 (0.53)

Log (BVME)

-1.0234 (17.80) -2.2886 (9.21)

Log (Age) Log (ME)

0.0023 (0.34)


0.4135 (5.99)

R2

0.0239

0.0291

0.3292

0.3611

Avg # of

Firms 3150

3150

3088

3088

Cohort Effects

No

Yes

Yes

Yes

Table 2.5: Firm Age and Innovative Edge

The table examines the relation of firm age with respect to three innovation measures: a) R&D over Assets, b) Granted Patent's originality, c) Granted Patent's Generality. For R&D/Assets we present two panels: The first panel presents the average from 1965 to 2006 of yearly mean, standard deviation, bottom 5th and top 95"1 percentiles, and median estimates of the fundamental measure. In addition the panel presents the measure's excess to industry (columns 7 and 8 of the panel). The second panel presents the results of the regression decomposing the measure's excess to the industry by firm age, listing cohorts, and year effects following Deaton 1997, and the Fama MacBeth average estimates for yearly cross-sectional regressions. The Granted Patent's originality and generality measures are summarized by age groups in the first panel, which presents average from 1980 to 1999 of originality, generality, citations received and made citations. For each of the two Patent measures we present results of the regression decomposing the measure by firm age, listing cohorts, and year effects, however this decomposition has biases explained in detail on the paper that should be taken into consideration. T statistics are reported under the coefficient estimates in parenthesis.

R&D / Assets

Firm Age

4<= (5-7) (8-12) (13-18) (19-25) (26-35) (36-55) (56-75) (76-100)

+100

Mean

0.091 0.100 0.104 0.087 0.068 0.047 0.034 0.029 0.026 0.025

SD

0.154 0.146 0.136 0.118 0.084 0.060 0.046 0.031 0.026 0.025

Bottom 5%

0.009 0.005 0.002 0.004 0.003 0.000 0.000 0.001 0.000 0.001

Med

0.044 0.062 0.071 0.058 0.046 0.030 0.021 0.020 0.020 0.017

Top 95%

0.322 0.333 0.327 0.257 0.202 0.156 0.108 0.089 0.075 0.083

Mean Excess 0.020 0.022 0.023 0.009 -0.003 -0.013 -0.014 -0.015 -0.013 -0.016

Med Excess -0.007 -0.004 0.000 -0.005 -0.008 -0.011 -0.010 -0.011 -0.009 -0.009

Avg # of Firms

39 98 218 241 191 167 192 137 123 105

R&D / Assets Age, Listing Cohorts, & Year Effects Decomposition

Intercept

0.0805 (5.73) 0.0530 (3.29) 0.1581 (9.48)

Log (Age)

-0.0310 (1.52) 0.0167 (0.99)

-0.2386 (12.98)

Log (Age2)

0.0060 (0.63)

-0.0091 (1.14) 0.1022 (12.03)

Log (ME)

-0.0114 (39.10) -0.0348 (34.21)

Log (BVME)

-0 0328 (58. (fi) -0.1028 (52.40

Log (Age) LOG (ME)

0.0075 (25.18)

Log (Age) Loo (BVME)

0.0235 (37.33)

R2

0.0198

00764

0.0979

Obs

63,330

61.715

61.715

Cohor t Effects

Yes

Yes

Yes


Intercept

0.0441 (5.17) 0.0518 (4.89) 0.0303 (4.22) 0.1096 (7.20)

Log (Age)

0.0265 (0.93)

-0.0057 (0.17) 0.0658 (2.83)

-0.1620 (5.97)

Log (Age2)

-0.0203 (1.51)

-0.0041 (0.27)

-0 0332 (2.98) 0.0664 (5.13)

Log (ME)

-0.0069 (6.17)

-0.0276 (8.09)

Log (BVM6)

-0.0243 (9.64)

-0.0822 (10.50)

Log (Age) Log (ME)

0.0064 (8.66)


0.0192 (9.63)

R2

0.0319

0.0384

0.0928

0.1209

Avg # of Firms 1505

1505

1470

1470

Cohor t Effects

No

Yes

Yes

Yes

R&D I Assets in Exc*ss to Industry by Firm A g * Listing D*cad« Cohort Effect*

00*

«s*

, t t* t s n we

Y w Effects

.». .** .» , « » 20 C MOS

110 Average US Patents Granted by Firm Age (1980 -1999)

Firm Age Average Age Average Patent

Originality

Average Patent

Generality

Citations Made

Citations Received

Average Patents Granted

Avg # of Firms

4<= (5-7) (8-12) (13-18) (19-25) (26-35) (36-55) (56-75) (76-100)

+100

3.6 6.2 10.4 15.5 21.9 30.3 45.1 65.5 86.8 128.3

44.54% 38.38% 40.07% 40.48% 40.15% 40.29% 39.37% 40.71% 40.86% 39.27%

37.77% 38.39% 36.68% 34.93% 32.44% 31.37% 29.21% 29.24% 29.10% 28.31%

7.74 8.17 11.52 10.85 10.37 11.08 11.23 12.10 11.32 10.95

12.89 13.85 10.34 7.74 6.25 5.80 4.91 4.48 4.71 4.88

8.37 9.70 8.10 12.69 11.03 10.97 14.66 28.46 53.04 53.39

5 12 30 48 55 63 94 97 107 101

Patent Originality (1980 - 1999)

Intercept

0.3069 (12.55) 0.3207 (12.52) 0.4039 (9.78)

Log (Age)

0.1190 (1.04)

-0.0002 (0.00)

-0.1986 (1.46)

A g e , L i s t i n g C o h o r t s , & Y e a r E f f e c t s D e c o m p o s i t i o n

Log (Ager(

-0.0567 (1.02) 0.0020 (0.03) 0.0696 (1.39)

Log (ME)

-0.0006 (0.49)

-0.0156 (2.92)

Log (BVME)

-0.0038 (1.43)

-0 0366 (332)

Log (Age) Log (Age)

Log (ME) Log (BVME)

0.0040 0.0093 (2.95) (3.13)

R2

0.0354

0.0364

0.0375

Obs

12,156

11.798

11.798

Cohort

Effects Yes

Yes

Yes

Patant Originality by Firm Ag« Listing D»cad« Cohort Effect*

Patent Generality (1980 -1999) Age, Listing Cohorts, & Year Effects Decomposition

Intercept

0.4972 (19.46) 0.4885 (18.28) 0.4744 (11.05)

Log (Age)

-0.0818 (0.69;

-0.1113 (0.87)

-0.1043 f0 .7 f l

Log (Age2)

0.0273 (0.48J 0.0417 (0.67J 0.0401 (o.6o;

Log (ME)

0.0020 (1.60) 0.0041 (0.74)

Log (BVME)

-0.0106 (3.78;

-0.0136 (1.17)

Log (Age)

Log (ME)

-0.0005 (0.37;

Log (Age)

Log (BVME)

0.0008 (0.25;

R2

0.2069

0.2083

0.2084

Obs

11.039

10,721

10,721

Cohor t

Effects Yes

Yes

Yes

Patent 0*n*ratrty by Firm Aga

I l l

Table 2.6: Firm Age and Process Efficiency

The table examines the relation of firm age with respect to three efficiency measures: a) Gross Margin; b) Net Operating Profitability After Tax; c) Invested Capital Turnover. For each measure we present two panels: The first panel presents the average from 1965 to 2006 of yearly mean, standard deviation, bottom 5lh and top 95th percentiles, and median estimates of the fundamental measure. In addition the panel presents the measure's excess to 48 industries (columns 7 and 8 of the panel). The second panel presents the results of the regression decomposing the measure's excess to the industry by firm age, listing cohorts, and year effects following Deaton 1997, and the Fama MacBeth average estimates for yearly cross-sectional regressions. Finally, for each measure we present graphs of the measure's decomposition into age, listing cohort, and year effect. As a robustness check in all our regressions we add the firms size (log(ME)) and book-to-market ratio (log(BVME)) and interact those variables with the age (log(Age)) variable. T statistics are reported under the coefficient estimates in parenthesis.

Firm Age

4<= (5-7) (8-12)

(13-18) (19-25) (26-35) (36-55) (56-75)

(76-100) +100

Mean

0.153 0.138 0.209 0.229 0.319 0.321 0.302 0.307 0.313 0.327

SD

0.841 0.951 0.837 0.772 0.448 0.306 0.302 0.161 0.190 0.170

Gross Marg Bottom

5% -1.493 -2.168 -0.978 -0.830 0.062 0.085 0.090 0.097 0.103 0.107

Med

0.333 0.350 0.366 0.356 0.344 0.315 0.294 0.289 0.297 0.310

in

Top 95%

0.716 0.757 0.754 0.722 0.692 0.649 0.623 0.584 0.589 0.620

Mean Excess -0.127 -0.106 -0.032 -0.010 0.056 0.043 0.017 0.017 0.005 0.027

Med Excess 0.021 0.040 0.057 0.050 0.041 0.019 -0.001 -0.006 -0.005 0.001

Avg # of Firms

78 174 374 421 362 345 460 357 312 272

-OM

::

Intercept

-0.0120 (0.31)

-0.0326 (0.70)

-0.3480 {6.40)

Intercept

0.0794 (5.60; 0.0056 (0.31)

-0.0508 (2.11)

-0.3536 (5.31)

Log (Age)

-0.8481

(9.72; -1.1209 (12.83) -0.4118 (4.33)

Log (Age)

-0.8653 (B-13)

-0.6032 (5.65;

-0.9044 (5.97;

-0.28O0 (2.70)

Gross Margin A g e , L i s t i n g C o h o r t s , & Y e a r E f f e c t s

Log (Age2) Log (ME)

0.4265 (10.33; 0.5401 0.0436 (f3.07J (27.57; 0.2315 0.1128 (5.26; (20.02)

L-<BVME> E E 2

0.0434 ((3.25J 0.2977 -0.0212 (26.40; (13.48;

D e c o m p o s i t i o n

Log (Age) Loa IBVMEI

-0.0820 (23.58)


Log (Age2) Log (ME|

0.4280 (6.03; 0.3024 (5.68; 0.4354 0.0320 (5.91) (7.73) 0.1725 0.1041 (3.23; (6.74;

Gross Margin in Exc«ss to Industry by Firm Ag«

r f

* • " « •

„ ,„. ,., ::

Log (BVME) £ £ »

0.0188 (2.20) 0.2172 -0.0216 (6.29; (6.39)


-0.0635 (7.49)

Listing D*cad« Cohort Effvcts

,-.,-,-..»,».«..-.,--,--..-.

R2

0.0070

0.0128

0.0172

R2

0.0108

0.0140

0.0454

0.0557

ooso

owo

oo»

4020

oa»

Obs

132.413

128.385

128.385

l!{

3150

3058

3058

Cohort

Effects Yes

Yes

Yes

Cohort

Effects No

Yes

Yes

Yes

Y M T Effects

, n ,., ,« - '- " ""

Net Operating Profit After Tax Margin (NOPAT)

112

Firm Age Mean SD Bottom 5%

Med Top 95% Mean Excess

Med Excess

Avg # of Firms

4<= (5-7) (8-12) (13-18) (19-25) (26-35) (36-55) (56-75) (76-100)

+100

0.110 0.086 0.089 0.100 0.110 0.119 0.120 0.131 0.135 0.139

0.177 0.189 0.184 0.165 0.136 0.125 0.118 0.124 0.107 0.097

-0.069 -0.119 -0.098 -0.039 0.003 0.022 0.029 0.034 0.041 0.048

0.101 0.092 0.094 0.095 0.095 0.095 0.096 0.102 0.110 0.118

0.312 0.276 0.268 0.260 0.242 0.250 0.257 0.295 0.285 0.263

-0.010 -0.026 -0.019 -0.005 0.005 0.009 0.006 0.005 0.004 0.005

-0.011 -0.010 -0.007 -0.003 -0.002 0.000 -0.001 0.000 0.001 0.002

31 100 268 340 316 313 426 339 301 263

Intercept -0.0035 (0.46)

-0.0367 (4.21)

-0.1522 (14.20)

00136 (1.75) 0.0147 (2.47)

-0.0347 (5.18)

-0.1578 (9.06)

Net Operating Profit After Tax Margin (NOPAT) Age, Listing Cohorts, & Year Effects Decomposition

Log (Age)

-0.2567 (10.74) -0.4443 (19.23) -0.2626 (10.69)

Log (Age*) Log (ME) Log (BVME)

01290 (11.29) 0.2136 (19.34) 0.1386 (12.01)

Log (Age) Log (Age) Log (MEI Loo (BVME) Obs

00158

Cohort Effects

113.163 Yes

0.0208 0.0022 (70.78J (3.45; 0.0432 0.0482 (36.80) «9 .72)

-0.0063 (19.91)

-0.0138 (19.38)

109.977

109.977

Fama MacBeth Average Estimates Intercept Log (Age) Log (Age2)

-0.2322

(2.92;

-0.1932

(3.15) -0.3737

(5.52;

-0.1837

P-07;

0.1157

(2.95;

0.0958

(3.14) 0.1783

(5.35;

0.1014

(3.33)

Log(ME) Log,BVME, £ * £ > L o g ^ E ,

0.0173 -O.0008 ()5.02; (0.43) 0.0423 0.0348

(12.05) (4.13) -0.0070 (9.95)

-0.0106

0.0078

0 0112

0.0766

0.0845

Avg # of Firms 2691

2691

2620

2620

Yes

Cohort Effects

No

Yes

Yes

Yes

NOPAT in Excess to Industry by Firm Ag« Listing Dseads Cohort Effects

113

Firm Age

4<= (5-7)

(8-12) (13-18) (19-25) (26-35) (36-55) (56-75) (76-100)

+100

Mean

1.731 1.939 1.952 2.098 2.086 2.086 2.035 1.803 1.653 1.451

SD

1.416 1.781 1.756 1.819 1.755 1.681 1.563 1.467 1.404 1.233

Invested Capital Bottom

5% 0.498 0.330 0.337 0.407 0.485 0.515 0.477 0.299 0.295 0.197

Med

1.439 1.536 1.531 1.663 1.677 1.720 1.720 1.578 1.470 1.327

Turnover

Top 95%

3.303 3.991 4.000 4.090 3.995 3.804 3.795 3.279 2.879 2.498

Mean Excess -0.161 -0.051 -0.017 0.117 0.081 0.085 0.061 -0.041 -0.097 -0.164

Med Excess -0.299 -0.283 -0.291 -0.177 -0.175 -0.177 -0.147 -0.193 -0.194 -0.208

Avg # of Firms

31 100 268 340 316 313 426 339 301 263

Invested Capital Turnover Age, Listing Cohorts, & Year Effects Decomposition

Intercept

0.8199 (11.16) 0.7640 (9.09) 1.6166 (15.63)

Log (Age)

-3.4758 (15.22) -1.8305 (8.22)

-3.0740 (12.96)

Log (Age2)

1.6422 (15.04) 0.9197 (8.64) 1.4245 (12.79)

Log (ME)

-0.1875 (66.10) -0 3475 (30.70)

Log (BVME)

-0.4110 (66.59) -0.6537 (27.70)

Log (Age) Log IMEI

0.0448 (14.69)

Log (Age) Lou (BVME)

0.0723 (10.55)

R'

0.0039

0.0557

0.0577

Obs

113.163

109.977

109.977

Cohor t

Effects Yes

Yes

Yes


Intercept

0.9966 (17.70) 0.8451 (13.95) 0.9528 (13.08) 2.0461 (17.07)

Log (Age)

-4.3666 (10.39) -4.1351 (9.56)

-2.1418 (5.09;

-3.7196 (7.70)

Log (Age1)

2.0679 (10.06) 1.9657 (9.31) 1.0642 (5.(7) 1.7003 (7.32)

Log (ME)

-0.1663 (25.38) -0.3852 (22.65)

Log (BVME)

-0.3640 (18.91) -0.5072 (9.35)

Log (Age) Log (ME)

0.0588 03.57)


0.0409 (3.42)

0.0063

0.0090

0.0552

00593

A v g # of Firms 2691

2691

2620

2620

Cohort Effects

No

Yes

Yes

Yes

Inv. Capital Turnover in Excess to Industry by Firm Age

L i s t i ng Decade Cohor t Effects

Graph 9. Invested Capital Turnover (1965 -2006)

(5-7) (6-12) (13-18) (19-25) (26-35) (36-55) (56-75) (?6-100> +100

F i rm Age

—•—A\erage —w— Median

Graph 9. Excess to Industry Invested Capital Turnover

4<= (5-7) (8-12) (13-18) (19-25) (26-35) (36-55) (56-75) [76-100) +100

F i rm Age

—•— Mean Excess to the Industry

Figures 2.9 and 2.10, Firm Age Raw and Excess to Industry Invested Capital Turnover

114

Table 2.7: Liquidity and Cash Flow Risk

The table examines the relation of firm age with respect to: a) Amihud's Illiquidity measure, b) Percentage of non-traded days over the year, c) Equity Duration as defined by Dechow, Sloan, and Soliman. For each measure we present two panels: The first panel presents the average from 1965 to 2006 of yearly mean, standard deviation, bottom 5th and top 95th percentiles, and median estimates of the fundamental measure. In addition the panel presents the measure's excess to 48 industries (columns 7 and 8 of the panel). The second panel presents the results of the regression decomposing the measure's excess to the industry by firm age, listing cohorts, and year effects following Deaton 1997, and the Fama MacBeth average estimates for yearly cross-sectional regressions. Finally, for each measure we present graphs of the measure's decomposition into age, listing cohort, and year effect. As a robustness check in all our regressions we add the firms size (log(ME)) and book-to-market ratio (log(BVME)) and interact those variables with the age (log(Age)) variable. T statistics are reported under the coefficient estimates in parenthesis. With our illiquidity measure we also include a panel that presents the equal weighted portfolio returns and excess returns to Fama & French portfolios for firms sorted into three Liquidity categories (low, medium, and high) and Firm Age. Additionally, this panel computes the average market value of the stocks in the portfolio and the average number of firms.

Illiquidity (Amihudl2)

Firm Age Mean SD Bottom 5% Med Top 95% Mean Excess

Med Excess

Avg # of Firms

4<=

(5-7)

(8-12)

(13-18)

(19-25)

(26-35)

(36-55)

(56-75)

(76-100)

+100

0.995

1.009

0.980

1.018

0.969

0.927

0.739

0.552

0.448

0.341

0.856

1.049

1.085

1.255

1.204

1.186

0.997

0.799

0.669

0.576

0.274

0.194

0.150

0.139

0.134

0.117

0.091

0.074

0.056

0.053

0.786

0.696

0.641

0.588

0.553

0.503

0.378

0.280

0.219

0.146

1.938

2.127

2.211

2.430

2.307

2.234

1.835

1.346

1.079

0.817

0.235

0.228

0.184

0.214

0.181

0.151

-0.001

-0.158

-0.222

-0.308

0.064

-0.033

-0.098

-0.167

-0.172

-0.187

-0.264

-0.324

-0.341

-0.426

94

173

359

410

355

334

454

357

320

282

Illiquidity (Amihudl2) Age, Listing Cohorts, & Year Effects Decomposition

Intercept

0.2152 (7.10) 1.1439 (33.08) 2.1244 (54.63)

Log (Age)

-0.0980 (26.52; 0.0475 (14.68) -0.2492 (38.25)

Log (ME)

-0.3001 (226.61) -0.5111 (111.78)

Log (BVME)

0.0318 (11.58) 0.1105 (12.19)

Log (Age) Log (ME)

0.0595 (47.34)


-0.0256 (9.19)

R2

0.0418

0.3753

0.3899

Obs

131,350

120.701

120,701

Cohort Effects

Yes

Yes

Yes


Intercept

1.2446 (10.84) 0.0823 (1.96) 1.4242 (14.16) 2 7835 (12.36)

Log (Age)

-2.9375 (9.26)

-0.1052 (7.58;

0.0467 (9.33)

-0.3605 (8.18)

Log (ME)

-0.2994 (1.27)

-0.6386 (4.57;

Log (BVME)

0.0128 0.00

0.1117 (8.27)

Log (Age) Log (ME)

0.0905 (4.80)


-0.0295 (63.93;

R2

0.0426

0.0749

0.4474

0.4722

Avg # of

Firms 3124

3124

2876

2876

Cohort

Effects No

Yes

Yes

Yes

Listing Decade Cohort Effects

Equ

al W

eigh

ted

Por

tfol

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Sor

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Liqu

idity

and

Age

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s

4<=

-0.3

0%

-0.5

6 -0

.099

-1

.20%

-3

.07

1296

20

0.33

%

0.66

0.

114

-0.6

1%

-2.1

1 14

4 44

1.68

%

3.66

0.

596

0.37

%

1.24

34

45

(5-7

)

0.30

%

0.63

0.

106

-0.5

4%

-1.9

4 89

4 39

0.65

%

15

0 0.

232

-0.4

9%

-2.1

3 13

5 68

1.86

%

4.18

0.

657

0.27

%

1.14

28

78

(8-1

2)

0.85

%

2.25

0.

350

-0.0

8%

-0.4

4 91

0 72

1.09

%

3.00

0.

466

-0.0

9%

-0.7

1 12

5 13

0

1.83

%

4.75

0.

737

0.28

%

2.15

27

14

5

(13-

18)

0.99

%

2.93

0.

454

-0.0

3%

-0.2

1 12

00

88

1.31

%

3.84

0.

595

0.06

%

0.61

13

4 14

3

2.08

%

6.04

0.

938

0.51

%

5.72

28

15

7

(19-

25)

1.03

%

3.34

0.

519

-0.0

1%

-0.0

7 20

77

81

1.47

%

4.82

0.

748

0.25

%

3.05

14

9 11

4

1.94

%

5.99

0.

929

0.36

%

4.29

28

13

5

(26-

35)

1.21

%

4.44

0.

689

0.21

%

2.19

21

24

89

1.41

%

5.05

0.

783

0.14

%

18

7 16

7 10

3

1.94

%

6.40

0.

994

0.28

%

3.38

28

11

9

(36-

55)

1.11

%

4.65

0.

721

0.06

%

1.10

20

32

136

1.33

%

5.35

0.

831

0.04

%

0.55

19

2 13

8

1.77

%

6.59

1.

022

0.08

%

1.01

34

13

8

(56-

75)

1.07

%

5.19

0.

806

0.02

%

0.36

31

86

142

1.18

%

5.12

0.

795

-0.1

7%

-2.1

5 20

1 10

7

1.57

%

6.37

0.

989

-0.1

6%

-1.5

0 43

75

(76-

100)

1.05

%

5.31

0.

825

0.01

%

0.17

36

76

153

1.23

%

5.53

0.

859

-0.1

5%

-1.6

9 22

5 83

1.53

%

6.31

0.

979

-0.2

0%

-1.7

0 43

58

+100

1.14

%

5.74

0.

890

0.09

%

14

4 62

74

169

1.22

%

5.59

0.

867

-0.1

7%

-17

3 21

7 56

1.40

%

5.00

0.

776

-0.3

7%

-2.2

9 42

33

Mat

ure

-Y

ou

ng

1.36

%

3.44

0.

612

1.25

%

3.23

19

41

119

0.81

%

2.80

0.

484

0.79

%

2.81

15

0 17

4

0.10

%

0.33

0.

053

-0.0

3%

-0.0

9 31

18

6

Mat

ure

-O

ld

-0.1

2%

-0.6

7 -0

.105

-0

.10%

-0

.70

4176

25

0

0.25

%

1.51

0.

234

0.43

%

3.32

18

3 17

1

0.54

%

2.74

0.

425

0.73

%

4.02

35

16

8

All

Fir

ms

1.02

%

4.26

0.

661

0.00

%

-0.0

2 29

78

977

1.19

%

4.30

0.

667

-0.0

7%

-19

2 16

8 97

7

1.84

%

6.f

2 0.

950

0.23

%

5.26

32

97

7

u>

116

Graph 11. Illiquidity and Firm Age

4<= (5-7) (8-12) (13-18) (19-25) (26-35) (36-55) (56-75) (76-100) +100

—•— High Liquidity -"— Med Liquidity —*— Low Liquidity

Figure 2.10 Illiquidity and Firm Age

Percentage of Non Traded Days

Firm Age Mean SD Bottom 5%

Med Top 95% Mean Excess

Med Excess

Avg # of Firms

4<= (5-7)

(8-12) (13-18) (19-25) (26-35) (36-55) (56-75)

(76-100) +100

0.197 0.168 0.153 0.137 0.123 0.129 0.117 0.099 0.070 0.055

0.144 0.167 0.165 0.168 0.167 0.172 0.181 0.177 0.147 0.136

0.076 0.019 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.152 0.125 0.111 0.087 0.059 0.063 0.026 0.007 0.000 0.000

0.396 0.388 0.357 0.358 0.354 0.375 0.375 0.351 0.268 0.211

0.059 0.039 0.026 0.017 0.007 0.016 0.006 -0.011 -0.036 -0.055

0.031 0.007 -0.006 -0.023 -0.042 -0.037 -0.058 -0.064 -0.073 -0.087

96 194 403 445 383 364 490 384 333 290

Percentage of Non Traded Days

Intercept

-0.0077

(1.19) 0.0780 110.36) 0.1937 (22.13)

Log (Age)

0.0973 (6.78) 0.2591 (18.64) 0.1417 (9.36)

Age, Listing Cohorts, & Year Effects

Log (Age2)

-0.0498 (7.35)

-0.1162 (17.69; -0.0745 ((0.64)

Log (ME)

-0.0426 ((66.82) -0.0641 (70.86)

Log (BVME)

-0.0011 (2.12; 0.0054 (3.03)

Log (Age) Loo (ME)

0.0061 (24.31)

Decomposition Log (Age)

Log IBVMEI

-0.0021 (3.73)

R2

0.0868

0.2718

0.2763

Obs

141,895

129.661

129,661

Cohort Effects

Yes

Yes

Yes


Intercept

0.1304 (5.99)

-0.0507 (5.31) 0.0953

( 7 5 9 ; 0.1799 (6.61)

Log (Age)

-01769 (6.30)

-0 0822 (1.96) 0.1295 (1.03)

-0.0126 (0.24)

Log (Age2)

0.0708 (5.79; 0.0388 (1.94)

-0.0520 (3.40)

0.0070 (0.30)

Log (ME)

-0,0422 ()3.oo; -0.0599 (780)

Log (BVME)

0.0046 (2.87) 0.0110 (4.25)

Log (Age) Log (ME)

0.0049 (3.57)


-0.0019 (2.39)

R2

0.0247

0.0939

0.2953

0.3018

Avg # of

Firms 3375

3375

3088

3088

Cohort Effects

No

Yes

Yes

Yes

Percentage of Non Traded Days in Excess to Industry by Firm Ag« i x

S"k

«

-7%

Y e a t E f f o c t s

• '•" XT76 <»< ,« 1991 > ae 2001 *»

117

Equity Duration

Firm Age

4<= (5-7)

(8-12) (13-18) (19-25) (26-35) (36-55) (56-75)

(76-100) +100

Mean

16.86 17.13 16.50 16.00 15.54 15.20 14.81 14.55 14.52 14.62

SD

3.907 3.461 3.231 3.036 2.912 2.809 2.778 2.686 2.601 2.402

Bottom 5%

11.41 11.67 11.27 11.00 10.64 10.43 9.92 9.80 9.88 10.40

Med

16.83 17.10 16.57 16.11 15.63 15.37 15.07 14.75 14.70 14.83

Top 95%

22.79 23.17 21.61 20.51 19.92 19.22 18.66 18.23 18.05 17.77

Mean Excess

1.11 1.33 0.76 0.37 0.04 -0.12 -0.30 -0.42 -0.41 -0.20

Med Excess

0.94 1.19 0.73 0.42 0.09 0.01 -0.11 -0.23 -0.22 -0.05

Avg # of Firms

38 116 287 347 312 300 406 322 291 255

Equity Duration

Intercept

0.1579 (0.84)

-1.3552 (7-64) 1.9378 (9.08)

Log (Age)

8.5754 (19.61) 8.8870 (21.53) 6.0089 (13.48)

Age, Listing Cohorts, & Year Effects

Log (Age2)

-4.3385 (20.79) -4.3603 (22.15) -3.3771 (16.21)

Log (ME)

-0 0640 (11.31) -0.5974 (27.48)

Log (BVME)

-1.3600 (114.96) -0.5491 (12.81)

Log (Age)

Log (ME)

0.1438 (24.24)

Decomposition Log (Age)

Log (BVME)

-0.2520 (19.53)

R2

0.0300

0.1427

0.1545

Obs

112.197

112,176

112,176

Cohort

Effects Yes

Yes

Yes


Intercept

0.4357 (2.01) 0.5303 (3.83)

-0.8043 (4.83) 1.5816 (5.92)

Log (Age)

6.6475 (5.12) 4.8550 (4.42) 5.2392 (4.75) 3.6486 (3.73)

Log (Age3)

-3.4256 (5.43)

-2.5267 (4.72)

-2.5776 (4.79)

-2.1379 (4.39)

Log (ME)

-0.0304 (1.58)

-0.4654 (8.95)

Log (BVME)

-1.6307 (13.36; -0.9692 (7.76)

Log (Age) Log (ME)

0.1176 (9.98)


-0.1945 17.31)

R2

0.0350

0.0417

0.2582

0.2675

Avg # of Firms 2668

2668

2672

2672

Cohort Effects

No

Yes

Yes

Yes

Equity Duration In E X C M S to Industry by Firm Ag«

* * * ^ ^ M ^ ^

Listing Docad* Cohort Effect*

.-

,„ *,*,

BM 1971 197*

Yoar Effects

19(1 t9M 1991 .<» » 0 1 20QS

118

Table 2.8: Default and Debt Structure

The table examines the relation of firm age with respect to: a) Ohlson's Default Probabiltiy, b) Actual Default and, c) Debt Structure. We analyze Ohlson's Default in two panels: The first panel presents the average from 1965 to 2006 of yearly mean, standard deviation, bottom 5th and top 95th

percentiles, and median estimates of the fundamental measure. In addition the panel presents the measure's excess to 48 industries (columns 7 and 8 of the panel). The second panel presents the results of the regression decomposing the Ohlson's Default Probability in excess to the industry by firm age, listing cohorts, and year effects following Deaton 1997, and the Fama MacBeth average estimates for yearly cross-sectional regressions. For this measure we present graphs of the measure's decomposition into age, listing cohort, and year effect. T statistics are reported under the coefficient estimates in parenthesis. The third panel presents actual annualized delisting and merging probabilities tracing them as the firms in an initial age group mature and move to older groups. The fourth panel presents in the same form as the firms panel average, median, standard deviation, etc. of the percentage of long term debt to total debt

Ohlson's Default Probability

Firm Age Mean SD Bottom

5% Med Top 95%

Mean Med Avg # of Excess Excess Firms

4<= (5-7) (8-12) (13-18) (19-25) (26-35) (36-55) (56-75)

(76-100) +100

2.801 2.465 2.187 1.925 1.737 1.548 1.422 1.345 1.302 1.320

3.834 2.721 2.308 1.891 1.529 1.229 0.975 0.998 0.806 0.736

0.849 0.798 0.802 0.794 0.773 0.763 0.737 0.750 0.756 0.784

1.785 1.658 1.477 1.371 1.275 1.197 1.153 1.101 1.087 1.096

7.714 6.944 5.732 4.761 4.190 3.434 2.999 2.702 2.564 2.700

0.922 0.561 0.299 0.067 -0.046 -0.151 -0.161 -0.164 -0.192 -0.145

0.131 -0.037 -0.156 -0.215 -0.246 -0.271 -0.250 -0.213 -0.204 -0.155

81 178 381 425 363 343 452 351 310 270

Ohlson's Default Measure Age, Listing Cohorts, & Year Effects Decomposition

Intercept

0.9710 (15.23) 1.6585 (22.53) 3.2722 (40.3d)

Log (Age)

-0.2568 (35.88; 0.0517 (7.82;

-0.4686 (36.28)

Log (ME)

-0.3815 (137.98) -0.8525 (91.46)

Log (BVME)

-0.4540 (79.79) -1.1612 (67.96;

Log (Age) Log (ME)

0.1380 (53.63;


0.2289 (39.67;

R2

0.0221

0.1509

0.1726

Obs

133,809

129,747

129,747

Cohort Effects

Yes

Yes

Yes


Intercept

1.2278 (19.02) 0.7539 (12.79) 1.6745 (16.93) 3.0935 (11.74)

Log (Age)

-1.5020 (3.42)

-0.2367 (15.49) 0.0225 (1.39)

-0.4985 (7.54J

Log (ME)

-0.3305 (10.99) -0.7688 (7.97)

Log (BVME)

-0.3666 0.00

-0.8720 (9.19)

Log (Age) Log (ME)

0.1270 (6.59)


0.1652 (37.37;

R2

0.0313

0.0398

0.1818

0.2037

Avg # of Firms 3183

3183

3091

3091

Cohort Effects

No

Yes

Yes

Yes

Default Prob in Excass to Industry by Firm Ag«

Listing Decad* Cohort Effects

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121

Table 2.9: Firm Age and Profitability

The table examines the relation of firm age with respect to three growth measures: a) 1 year Asset Growth, b) 1 year Sales Growth, c) Tobin's Q proxy (market value of Assets / Book Value of Assets). For each measure we present two panels: The first panel presents the average from 1965 to 2006 of yearly mean, standard deviation, bottom 5th and top 95th percentiles, and median estimates of the fundamental measure. In addition the panel presents the measure's excess to industry which is computed in the following form: 1) compute the industry average following Fama and French 2000, 48 industry classification; 2) subtract the industry average at the firm level from the raw measure; 3) group the firms by age and compute the average and median of the of the excess to the industry measure (columns 7 and 8 of the panel). The second panel presents the results of the regression decomposing the measure's excess to the industry by firm age, listing cohorts, and year effects following Deaton 1997, and the Fama MacBeth average estimates for yearly cross-sectional regressions. Finally, for each measure we present graphs of the measure's decomposition into age, listing cohort, and year effect. As a robustness check in all our regressions we add the firms size (log(ME)) and book-to-market ratio (log(BVME)) and the interact those variables with the age (log(Age)) variable. T statistics are reported under the coefficient estimates in parenthesis.

Return on Assets

Firm Age

4<= (5-7)

(8-12) (13-18) (19-25) (26-35) (36-55) (56-75)

(76-100) +100

Mean

-0.009 -0.019 0.011 0.041 0.061 0.074 0.077 0.079 0.079 0.081

SD

0.200 0.209 0.184 0.157 0.125 0.099 0.081 0.068 0.061 0.061

Bottom 5%

-0.410 -0.480 -0.388 -0.273 -0.165 -0.085 -0.045 -0.022 -0.011 -0.001

Med

0.039 0.032 0.055 0.073 0.079 0.081 0.081 0.081 0.080 0.082

Top 95%

0.227 0.246 0.243 0.234 0.227 0.208 0.188 0.177 0.165 0.170

Mean Excess -0.059 -0.060 -0.030 -0.003 0.013 0.019 0.016 0.013 0.010 0.013

Med Excess -0.020 -0.018 0.004 0.018 0.022 0.021 0.016 0.011 0.007 0.007

Avg # of Firms

38 124 297 343 306 306 417 320 276 225

Return on Assets Age, Listing Cohorts, & Year Effects Decomposition

Intercept

0.0060 (0.77)

-0.0033 (0.38)

-0.30O0 (29.74)

Log (Age)

-0.5675 (24.53) -0.7274 (33.4S) -0.0615 (2<*>

Log (Age )

0.2841 (25.79; 0,3504 (33.84) 0.0592 (5.42)

Log.ME, Log.BVME, £ £ g £ • * £ R2

0.0385

0.0165 (28.53)

0.0800 0.1536 (77.92) (75.06)

0.0252 (90.18)

-0.0163 (57.16)

-0.0434 (69.81)

Obs C o h o r t

Effects 111.283 Yes

107.560 Yes

Fama MacBeth Average Estimates Intercept Log (Age) Log (Age2)

0.0365

(1.75) 0.0087

(1.01) -0.0212

P-B7) -0.2694

(10.07)

-0.5011

(7.34) -0.4116

(6.81) -0.5861

-0.0485

(0S1)

0.2489

(7.32;

0.2061

16.87) 0.2821

(7.61) 0.0517

<1-89)

Log«ME, Log.BVME, £ £ « £ £ &

0.0197 0.0028 (11.35) (0.70) 0.0696 0.1001 (10.46) (6.33;

-00147 f9.7?J

-0.0311

0.0317

0.0365

0.1753

0.2184

Avg # of Cohort Firms Effects 2647 No

2647

2562

2562

Yes

Yes

Yes

Rsturn on Asssts in Excess to Industry by Firm Listing Dscods Cohort Effects

Dividend Yield (Dividends / MV Equity)

Firm Age

4<= (5-7) (8-12) (13-18) (19-25) (26-35) (36-55) (56-75) (76-100)

+100

Mean

0.007 0.007 0.007 0.009 0.011 0.015 0.021 0.028 0.032 0.033

SD

0.015 0.014 0.015 0.016 0.017 0.019 0.021 0.023 0.023 0.021

Bottom 5%

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.004 0.006

Med

0.002 0.002 0.000 0.001 0.004 0.008 0.016 0.025 0.030 0.031

Top 95%

0.022 0.019 0.022 0.029 0.034 0.039 0.050 0.060 0.064 0.063

Mean Excess -0.008 -0.008 -0.007 -0.006 -0.004 -0.003 0.001 0.005 0.007 0.007

Med Excess -0.009 -0.009 -0.008 -0.008 -0.007 -0.005 -0.001 0.004 0.006 0.007

Avg # of Firms

81 178 380 425 364 346 461 358 312 272

Dividend Yield Age, Listing Cohorts, & Year Effects Decomposition

Intercept

-0.0111 (18.97) -0.0119 (15.45) -0.0154 (18.03)

Log (Age)

0.0038 (58.32; 0.0025 (36.42; 0.0033 (24.64;

Log (ME)

0.0014 (48.63; 0.0012 (1196;

Log (BVME)

0.0033 (56.07) -0.0050 (25.60)

Log (Age) Loo (ME)

0.0001 (4.03;


0.0027 (44.57)

R2

0.0902

0.1193

0.1335

Obs

133,452

129,397

129,397

Cohort Effects

Yes

Yes

Yes


Intercept

-0.0320 (18.04) -0.0093 (6.76;

-0.0124 (7.34;

-0.0149 (6.15)

Log (Age)

0.0533 (13.38) 0.0043 (12.80) 0.0026 (10.79) 0.0028 (5.71)

Log (ME)

0.0019 (8.52; 0.0016 (3.80;

Log (BVME)

0.0053 0.00

-0.0019 (1.34)

Log (Age) Log (ME)

0.0001 (14.32)


0.0022 (18.28)

R2

0.0914

0.1066

0.1592

0.1689

Avg # of Firms 3174

3174

3083

3083

Cohort Effects

No

Yes

Yes

Yes

Dividend Yield in Excess to Industry by Firm Age

Listing Decade Cohort Effect*

3-S49 WO-1959 1960-069 «70- WT9 W60-19B9 B90- B99

1971 1976 SS I <90£ 1991 1996 2001 2006

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2

Tab

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ure

-Y

ou

na

0.66

%

3.72

0.

672

0.61

%

2.86

33

3 28

2 -2

5.90

%

0.65

%

3.44

0.

742

0.57

%

3.73

45

7 19

4 0.

91%

-0.0

1%

-0.0

7 -0

.016

-0

.03%

-0

.79

814

235

20.0

8%

Mat

ure

-O

ld

0.32

%

0.93

0.

200

0.31

%

7.35

22

96

271

-17.

86%

0.37

%

7.44

0.

311

0.30

%

7.85

33

76

271

0.95

%

0.40

%

7.53

0.

329

0.40

%

2.16

60

32

289

17.7

7%

All

Fir

ms

1.44

%

3.64

0.

785

0.03

%

0.47

98

2 10

83

-20.

09%

1.33

%

4.49

0.

969

-0.0

6%

-0.9

9 18

19

1152

1.

07%

1.46

%

4.66

1.

006

0.22

%

2.92

27

40

1199

17

.58%

Table 2.13: New Duration Estimates

The table examines the relation of firm age with respect to the new re-estimated equity duration measure. Our new re-estimated equity duration follow Dechow, Sloan, and Soliman however instead of assuming mean reversion in sales growth to compute expected sales during the next 10 years, we utilize sales growth estimates based on last year growth and current firm age combined with industry sales growth regression coefficients. We present two panels: The first panel presents the average from 1965 to 2006 of yearly mean, standard deviation, bottom 5th and top 95th

percentiles, and median estimates of the fundamental measure. In addition the panel presents the measure's excess to 48-industry (columns 7 and 8 of the panel). The second panel presents the results of the regression decomposing the measure's excess to the industry by firm age, listing cohorts, and year effects following Deaton 1997, and the Fama MacBeth average estimates for yearly cross-sectional regressions. Finally, for each measure we present graphs of the measure's decomposition into age, listing cohort, and year effect. As a robustness check in all our regressions we add the firms size (log(ME)) and book-to-market ratio (log(BVME)) and the interact those variables with the age (log(Age)) variable. T statistics are reported under the coefficient estimates in parenthesis.

Firm Age

4<= (5-7)

(8-12) (13-18) (19-25) (26-35) (36-55) (56-75) (76-100)

+100

Mean

35.85 28.89 23.36 22.23 19.78 17.26 15.64 14.60 14.04 13.87

SD

19.714 13.843 9.947 12.720 8.909 4.646 3.039 2.820 2.717 2.605

New Bottom

5% 16.51 11.02 8.97 14.17 14.10 12.86 11.63 10.38 9.39 9.33

Equity Duration

Med

31.23 25.29 21.52 18.88 17.57 16.48 15.43 14.60 14.20 14.15

Top 95%

68.94 57.77 41.14 52.00 34.12 24.74 20.47 18.61 17.43 16.83

Mean Excess 15.71 8.51 3.20 2.57 0.75 -0.86 -1.75 -2.17 -2.41 -2.51

Med Excess 11.98 6.23 2.82 1.01 0.03 -0.65 -1.34 -1.70 -1.82 -1.87

Avg # of Firms

38 116 287 347 312 300 406 323 291 255

New Equity Duration Age, Listing Cohorts, & Year Effects Decomposition

Intercept

-2.0804 (2-96) 0.9304 (1.34) 62430 (7.54)

Log (Age)

82.5687 (50.54; 83.5523 (51.62; 90.5326 (52.37)

Log (Age2)

-41.3000 (52.97)

-41.9234 (54 .30

-46.1532 (57.(2;

Log (ME)

-0.1295 (5.83;

-0.7119 (8.45)

Log (BVME)

2.1860 (47.I2J 11.4687 (68.99)

Log l«gei

Loo (ME)

0.1089 (4.74)

i-og («ge| Log (BVME)

-2.8991 (57.95)

R2

0.0912

0.1147

0.1468

Obs

112,250

112,223

112,223

Effects Yes

Yes

Yes


Intercept

-1.3708 (1.26)

-1.6264 (1.49) 0.6577 (0.84) 6.1581 (7.23)

Log (Age)

66.3318 (9.00)

69.0212 (9.26;

68.6816 (9.90)

739382 (11.30)

Log (Age2)

-33.3675 (9.29;

-34 6981 (9.54;

-34.6212 (10.19;

-38.0222 (11.81)

Log (ME)

-0.0739 (2.25;

-0.8514 (8.54;

Log (BVME)

1.3767 (5.85) 9.4568 (12.44}

Log (Age) Log (ME)

0.1718 (7.35)


-24462 (13.11)

R2

0.2462

0.2491

0.2759

0.3297

Avg * of Firms 2670

2670

2673

2673

Cohort Effects

No

Yes

Yes

Yes

Ass«t Growth in Excess to Industry by Firm Age Listing D*cad« Cohort Effect*

. 1«B 2O0O-JO08

128

2.8 References

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2. Agarwal, Rajshree, and Michael Gort, 2002, "Firm and Product Life Cycles and Firm Survival," The American Economic Review, 92, 2, 184-190.

3. Amihud, Yakov, 2002, "Illiquidity and Stock Returns: Cross-section and Time-series Effects," Journal of Financial Markets, 5, 31-56.

4. Altaian, Edward I., "Default Recovery Rates and LGD in Credit Risk Modeling and Practice: An Updated Review of the Literature and Empirical Evidence," Working Paper 2006.

5. Carhart, Mark M., 1997, "On Persistence in Mutual Fund Performance," Journal of Finance, 52, 57-82.

6. Daniel, Kent, Hirshleifer D., and Subrahmanyam A., 1998, "Investor Psychology and Security Market Under- and Overreactions", Journal of Finance, 53, 6, 1839-1885.

7. Daniel, Kent, Hirshleifer D., and Subrahmanyam A., 2001, "Overconfidence, Arbitrage, and Equilibrium Asset Pricing", Journal of Finance, 56, 3, 921- 965.

8. Daniel, Kent, and Sheridan Titman, 1997, "Evidence on the Characteristics of Cross Sectional Variation in Stock Returns," Journal of Finance, 52, 1, 1-33.

9. Daniel, Kent, and Sheridan Titman, "Testing Factor-Model Explanations of Market Anomalies," 2005 Working Paper.

10. Daniel, Kent, Grinblatt M, Titman S., and Wermers R., 1997, "Measuring Mutual Fund Performance with Characteristic-Based Benchmarks," Journal of Finance, 52, 3, 1035-1058.

11. Davis, James L., Eugene F. Fama and Kenneth R. French, 2000, "Characteristics, Covariances and Average Returns: 1929-1997," Journal of Finance, 55, 389-406.

12. Dealers' Digest Publishing Company, 1961, "Corporate Financing, 1950-1960" (Dealers' Digest Publishing Company, New York).

13. Dean, Arthur H., William Piel Jr., and Row H. Steyer, 1951, "Issuer Summaries: Securities Issues in the United States - July 26, 1933 to December31, 1949" (privately printed, New York).

129 14. Dechow, Patricia, Sloan R., and Soliman M., 2004, "Implied Equity Duration: A New Measure of

Equity Risk," Review of Accounting Studies, 9, 197-228.

15. Evans, David S. , 1987, "The relationship between firm growth, size, and age: Estimates for 100 Manufacturing Industries," Journal of Industrial Economics, 35, 4, 567-581.

16. Fama, E. and French K., 1992, "The Cross-Section of Expected Stock Returns," Journal of Finance, 47, 2, 427-466.

17. Fama, E. and French K., 1993, "Common Risk Factors in the Returns on Stock and Bonds," Journal of Financial Economics, 33, 1, 3-56.

18. Fama, E. and French K., 1996, "Multifactor Explanations of Asset Pricing Anomalies," Journal of Finance, 51, 1,55-84.

19. Fama, E. and French K., 1997, "Industry Cost of Equity," Journal of Financial Economics, 43, 153-193

20. Fama, E. and French K., 2004, "New Lists: Fundamentals and Survival Rates," Journal of Financial Economics, 73, 229-269.

21. Fama, E. and French K., 2006, "Profitability, investment and average returns," Journal of Financial Economics, 82, 491-518.

22. Fama, E.F., MacBeth, J., 1973, "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, 81, 607-636.

23. Fink, Jason, Fink, K. E., Grullon, G. and Weston, J., working paper 2005, "IPO Vintage and the Rise of Idiosyncratic Risk."

24. Frankel, Richard, and Charles M.C. Lee, 1998, "Accounting Valuation, Market Expectation, and Cross-Sectional Stock Return," Journal of Accounting and Economics, 25, 283-319.

25. Gupta, S. and Newberry, K. 1997, "Determinants of the variability in corporate effective tax rates: evidence from longitudinal data," Journal of Accounting and Public Policy, 16, 1 -34.

26. Hall, B. H., A. B. Jaffe, and M. Tratjenberg, 2001, "The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools." NBER Working Paper 8498.

27. Hillstrom, Roger, and Robert King, 1970, "/* Decade of Corporate and International Finance: 1960-1969" (Investment Dealers Digest, New York).

130 28. Hou, Kewei, and David Robinson, 2006, "Industry Concentration and Average Stock Returns," Journal

of Finance, 61, 4, 1927-1956.

29. "International Directory of Company Histories," St. James Press, Vols. 1 to 82.

30. Jovanovic, Boyan, 1982, "Selection and the Evolution of Industry," Econometrica, 50, 3, 649-670.

31. Jovanovic, Boyan and Rousseau, P. L., 2001, "Why Wait? A Century of Life Before IPO," AEA Papers and Proceedigs, 91, 2, 336-341.

32. Kaplan, Steven N., Sensoy B., and Stromberg P., 2005, "What are Firms? Evolution from Birth to Public Companies," Working Paper.

33. Kelley, M. Etna, 1954, "The Business Founding Date Directory", Morgan & Morgan Publishers, New York.

34. Klepper, Steven, 1996, "Entry, Exit, Growth, and Innovation over the Product Life Cycle," The American Economic Review, 86,3,562-583.

35. Lyon, J. D., Barber, B. M. and Tsai, C-L, 1999, "Improved Methods for Tests of Long-Run Abnormal Stock Returns," Journal of Finance, 54, 1,165-201.

36. Moody's Industrial Manuals, various dates (Moody's Investor Services, New York).

37. Mueller Dennis C , 1972, "A Life Cycle Theory of the Firm," The Journal of Industrial Economics, 20, 3, 199-219.

38. Ohlson, J.A., 1980, "Financial Ratios and the Probabilistic Prediction of Bankruptcy," Journal of Accounting Research 18, 109-131.

39. Pastor, Lobus, and Pietro Veronesi, 2003, "Stock Valuation and Learning about Profitability," Journal of Finance, 58, 5, 1749-1789.

40. Plesko, Geoge A., 2003, "An evaluation of alternative measures of corporate tax rates", Journal of Accounting and Economics, 35, 201 - 226.

41. Shumway, T., 1997, "The Delisting Bias in CRSP's Data," Journal of Finance, 52, 1, 327-340.

42. Shumway, T., and Warther, V. A., 1999, "The Delisting Bias in CRSP's Nasdaq and its Implications for the Size Effect," Journal of Finance, 54, 6, 2361-2379.

131

Appendix 2.A Variable Definitions and Construction

1) Firm Growth

• Asset Growth22: dAt I At_x = (At - At_x) / At_x, assets is given by Compustat's data6, and

restricts for assets to be > 0.

• Sales Growth: dS, / <Sf_j = (S, — iS",,, ) /£ ,_ , , sales is given by Compustat's datal2, and

restricts for sales to be > 0.

• Tobin's Q approximation (Market Value of Assets / Book Value of Assets(data6)), where

Market Value of Assets = Market Equity (data24 * data25) + Assets (data6) + Book Value

Equity.23

2) Innovation Edge

• R&D (data46) / Assets (data6): research and development expense as a percentage of total

assets, where assets are greater to zero.

• Patent Originality and Generality, based on citations made and received these measures proxy

the relative level of originality and generality of a patent. They are defined in detail on Hall,

Jaffe, and Tratjenberg 2001.

3) Process Efficiency

• Gross Margin: 1 - COGS(data41) / Sales(datal2).

• Net Operating Profit After Tax Margin (NOPAT): (EBITAD, - Taxes) / Sales,, where

EBITAD is defined as Pretax Income (data 170) - Special Items (data 17) + Interest Expense

(datal5) + Depreciation (datal4). Taxes are computed as Pretax Income times Effective Tax

Rate24

We also computed book value growth, however that limited the sample to firms with positive book values, while asset growth captures more generally firms growth even if at some instances the firms have negative book values.

Book Value of Equity is defined as in Fama & French 92, Shareholders Equity (data60) + Balance Sheet Deferred Taxes (data 74) + Investment Tax Credit (data51).

Effective Tax Rate is defined following Gupta and Newberry, Current Income tax Expense (data 16 -data50) over Book Income before interest and taxes (data 170 - data55 - data 17 + data 15)

132

• Invested Capital Turnover: Average of beginning and ending fiscal year of Sales, / (Assets, -

Current Liabilities, (data5) + Short Term Debt, (data 34) - Long Term Investments, (data31 +

data32) - Excess Cash, which is defined as cash in excess of 3% of Sales.

4) Liquidity and Cash Flow risk

• Illiquidity = [1000 * (ABS(ret) / (ABS(prc) * Vol))"2] , constructed using CRSP daily data,

following Amihud's illiquidity measures.

• Percentage of Non-Traded days, we consider a day as non-traded if volume is zero and if

volume is less than 100 and return is zero.

• Equity Duration, computed following Dechow, Sloan, and Soliman 2004. This measure can

be seen as an approximation of when equity is paid back, a longer (shorter) duration implies

longer (shorter) investment horizon until payback and therefore higher (lower) probability that

such cash flows will not be received.

5) Debt Structure and Default

• Ohlson's Default Probability, is the probability of default on debt, estimated at the end of

fiscal year t, from the logit regression model of Ohlson 1980.

• We used CRSP delisting code to separate delisting, merging, and other firms.

• Long Term debt (data 9) as percentage of total debt (data 9 + data 34)

6) Profitability

• Return on Assets = Earnings,25 / Assets,.i

• Dividends (data21) / Assets, dividend percentage normalized to assets.

Earnings = Earnings before ex-items + interest expense + deferred taxes, data 18 + data 15 + data50.

Appendix 2.B Credit Rating Numerical Conversion

We code the four rating agencies (FISD Rating Type) in the following form: FR (Fitch) = 1;

(Standard & Poors) = 3; and DPR = 4 (which we ignore);

If Rating Type is either Fitch or Standar Poors then:

'AAA+' = 10.33; 'AAA' = 10; 'AAA-' = 9.67;

•AA+' =9.33;'AA' = 9; 'AA-' = 8.67;

*A+' = 8.33; 'A' = 8; 'A-' = 7.67;

'BBB+' = 7.33; 'BBB' = 7; 'BBB-' = 6.67;

'BB+' = 6.33; 'BB* = 6; 'BB-' = 5.67;

'B+' = 5.33;'B' = 5;'B-'=4.67;

'CCC+' = 4.33; *CCC = 4; 'CCC-' = 3.67;

'CC+' = 3.33; 'CC = 3; 'CC-' = 2.67;

'C+' = 2.33;'C' = 2;'C-'=1.67;

'DDD' = 0.67; 'DD' = 0.33; 'D' = 0;

If Rating Type is MR then:

'Aaa' = 10; 'Aal' = 9.33; 'Aa2' = 9; 'Aa3' = 8.67;

'Al' = 8.33; 'A2' = 8; 'A3' = 7.67; 'A' = 8;

'Baal' = 7.33; 'Baa2' = 7; 'Baa' = 7; 'Baa3' = 6.67;

'Bal' = 6.33; 'Ba2' = 6; 'Ba' = 6; 'Ba3' = 5.67;

'Bl' = 5.33; 'B2' = 5; 'B' = 5; 'B3' = 4.67;

'Caal' = 4.33; 'Caa2' = 4; 'Caa' = 4; 'Caa3' = 3.67;

'Ca' = 3; 'C = 2; 'SUSP' = 0;

134

Chapter 3: The Rational Part of Momentum*

3.1 Introduction

The data in this paper suggest that the momentum effect (Jagadeesh and Titman,

1993, 2002) is closely related to changes in a measure of the fundamental value of a

company's equity. We show that the return of each stock return decile tracks the rate of

change in the decile's fundamental value, which we measure as a function of the change

in analysts' earnings estimates. This close co-movement of price and fundamental value

is one aspect of rationality: price should reflect fundamental value.

However, we also find another more subtle aspect of rationality. When we

redefine the momentum deciles to reduce errors in the change in fundamental value, the

stock return deciles appear to predict future changes in fundamental value. This contrasts

with the more frequently discussed result that analysts' estimates tend to predict future

changes in stock price . While we find some evidence of that, the more pronounced

effect works in the opposite direction; stock returns appear to predict future changes in

analysts' estimates.

While there are several possible explanations for this predictability and for the

momentum effect, we believe the most plausible is simple and rational. We conjecture

that informed investors predict changes in fundamental value and move stock prices

before less well informed investors obtain, and react to, the same information. Initially

the informed investors obtain new information and move stock price. Subsequently,

This chapter is from a co-written paper with James H. Scott

26 Ball and Brown, (1968). A growing literature suggests the momentum and PEAD anomalies are linked, e.g., Chan, Jagadeesh and Lakonishok (1999); Van Dijk and Huibers, 2002; Hong, Lee and Swaminathan (2003); Chordia and Shivakumar (JFE, 2006).

135

when the uninformed gain the same information their trades cause a further movement of

stock price in the same direction.

This explanation is inconsistent with Market Efficiency. In its strong form, the

Efficient Markets Hypothesis asserts that "at any time prices fully reflect all available

information" (Fama, 1970, p. 383). Clearly the momentum effect violates this since it

implies that past returns predict future returns. So too does our explanation, since we

believe that uninformed investors affect price, causing it to differ from what informed

investors believe it should be. As we discuss below, our view is not original, but is

consistent with many rational models of capital market equilibrium, and so provides a

rational explanation of the momentum effect.

For example, a heterogeneous expectations model (e.g., Fama and French, 2007,

Rubinstein, 1974, Lintner, 1969) can explain how past returns can predict both future

returns and future fundamental values. In particular, consider a model in which some

investors become "informed" in the sense that they deduce the correct mean of next

year's level of a stock's price. Other investors remain "uninformed" and have incorrect

estimates of the mean. As Fama and French argue the informed investors are likely to

have positive alpha's and the uninformed, negative alpha's (p. 673).

For example, if initially all investors assume that next period the expected price of

a stock is 11 and the appropriate discount rate is 10%, the current market price will be 10.

Next, to add heterogeneous expectations, assume that a subset of investors suddenly

discovers new information that leads them to believe the stock will have higher future

cash flows so that its price next period will have a mean of 14. If the market reopens,

136

then as long as some investors remain uninformed, the current stock price will not rise as

much as the informed investors believe is warranted.

How much the price increases will depend on the relative wealth, risk aversion

and different price expectations of both types of investors as well as their other portfolio

holdings. Suppose that the current price rises to 11.5, thereby creating a positive,

abnormal return. If next period's price equals 14, which is the mean of the informed

investors' distribution, a second abnormally positive return will follow the first.

In sum, if some investors become informed before others, one abnormal return

will be followed by another. That is, there will be a momentum effect. Further, since

prices are determined by discounting future cash flows, the cash flows expected by the

uninformed investors in one period will be the cash flows expected by the informed

investors in the previous period.

An appealing version of this type of equilibrium appears in "On the Impossibility

of Informationally Efficient Markets," (Grossman and Stiglitz, hereafter GS, 1980),

which we believe best captures the essence of what drives the momentum effect. They

begin by showing that, if information is costly, there is no market mechanism to ensure

that prices will fully reflect all available information. Stated differently, if the Efficient

Markets Hypothesis held and prices did reflect all information, there would no incentive

to collect costly information. Hence, prices will not reflect the information. Fama (1991,

p. 1575) acknowledged the point in his second review of the efficient markets literature.

"Since there are surely positive information and trading costs, the extreme version of the

market efficiency hypothesis is surely false."

137

In the noisy rational expectations equilibrium described by GS (see also Lucas,

1972), a subset of investors collects costly information because they expect to profit by

doing so. "Informed investors" in the GS model must be able to buy and sell at prices

that do not reflect the information they generate. When they uncover particularly good

news about a company they must be able to buy at a price that is, on average, less than

the price that fully reflects available information. Similarly, they must be able to sell a

bad-news stock before the bad news is fully incorporated in price. As a result, the prices

behave in a fashion similar to that described in the above example.

Since price partially reflects the information collected by the informed investors,

prices convey useful information. In the GS model, uninformed investors know this and

try to take advantage of it. However, they are hampered by the normal randomness of

prices (which GS model as supply shocks) and their own risk aversion. Although they

know that a stock whose price has just gone up is more likely to have a higher expected

return than a stock that has gone down, because price is an imperfect signal and because

many stocks have gone up, they respond with only a small purchase.

Notice that an uninformed investor in the GS model behaves like a momentum

investor to the extent that he is more likely to favor a stock that has recently gone up than

one that has gone down.

Hong and Stein (1999) present a theoretical model that, like GS, has two types of

investors. While their model is one with boundedly rational agents, rather than a rational

expectations one, Hong and Stein's "news watchers" are like the informed investors in

GS, while their momentum investors are similar to the uninformed investors in GS.

138

In practice, we believe that a highly competent subset of professional investors fill

the role of the informed investors in the above models. Most professional investors

predict future earnings, and many use present value techniques to predict future stock

prices. To the extent they are successful; they buy or sell stocks before changes in

fundamental value are fully incorporated in stock prices. Their trades move prices, but

not so much that prices fully reflect the information these investors have gathered and

interpreted.

Security analysts employed by brokerage firms provide the information to the

uninformed investors. Like professional investors, security analysts carefully research

companies and estimate earnings and future cash flows; but they trade very little on the

basis of the information they generate. Instead, they work to disseminate it widely and

the information they generate is subsequently impounded in prices. As we show below,

over the six month intervals we investigate, changes in our measure of analysts'

expectations are highly correlated with changes in stock price27.

To investigate the earnings/momentum link, we use a modified present value

framework to estimate changes in a stock's fundamental value. Our measure of

fundamental value is similar to but different from earlier present value models of equity

valuation (e.g., Edwards and Bell, 1961, and Miller and Modigliani, 1966), but, like

them, allows the use of security analysts' expectations.

We present the time path of the actual returns of different momentum deciles and

compare them to our estimates of the concurrent percent change in fundamental value for

each momentum decile. The similarity between actual returns and changes in

We show below that analysts' expectations also anticipate future price changes, but that effect is smaller than the correlation between changes in analysts' expectations and concurrent price change.

139

fundamental value is striking and supports our notion that the primary force underlying

the momentum effect is changing company fundamentals as reflected in analysts'

expectations.

This linkage casts doubt on the primacy of several behavioral and/or technical

explanations of momentum. In particular, prospect theory and the disposition effect

suggest investors sell winners too early and hold losers too long so that prices slowly

react to fundamentals and thus, significantly diverge from fundamentals (e.g., Kahneman

and Tversky, 1979; Shefrin and Stattman, 1985; Grinblatt and Han, 2005). In contrast,

our findings suggest that prices mirror current changes in fundamentals, and anticipate

future changes in fundamentals.

Other theories posit that momentum occurs because technical traders react to

unusual price movements by seeking to ride a trend and over-extrapolate underlying

fundamentals (e.g., Delong et al, 1990). Daniel et al (1998) suggest that the behavior of

over-confident investors causes over-extrapolation. Again, our work suggests that, by

and large, prices track and anticipate underlying fundamentals. While these

behavioral/technical explanations may account for some portion of the momentum effect,

they do not seem to be the driving force.

This paper shares similarities with recent research on futures prices by Boudoukh

et al (2007) who contest the view of Delong et al (1990). Delong et al argued that

fundamentals had little effect on the frozen concentrated orange juice futures market.

They based their argument on the fact that regressions relating futures prices to

temperature that appeared in Roll (1984) have low R2's.

140

Later research by Boudoukh et al show that when temperatures remain above 36°,

Delong et al are correct; temperature change has little effect on futures prices. However,

movements in temperature near and below the freezing point have a significant impact on

supply because they can destroy orange crops. These freezing temperatures also have

dramatic effects on price, just as economic theory would predict. In concert with our

findings, changing fundamentals drive prices.

In a similar way, separating stock returns into momentum deciles and focusing on

the extreme deciles forces a researcher to study stocks at a time when something highly

significant affected their prices. Neoclassical finance suggests the significant event

should relate directly to the valuation of future corporate cash flows. Behavioral finance

might allow for some impact by fundamentals but would hold that there should be

additional and significant behavioral effects that would be considered irrational in a

neoclassical context. We find that fundamentals appear to be the predominant force.

The findings of Hong, Lee and Swaminathan (2003) (HLS) can be interpreted as

added evidence supporting a noisy rational expectations equilibrium. In particular, if

information is costless and the price system highly informative, there is unlikely to be a

noisy rational equilibrium or a momentum effect. An important instance of this can

occur when corporate insiders, who do have costless information about their own

corporations, can trade freely on that information. HLS studied 11 countries. In those

countries where investor protection was low and corruption was high (and, presumably,

insider trading largely unimpeded), there was neither post-earnings-announcement-drift

nor a momentum effect, as the GS analysis would suggest.

141

Section 3.2 describes our estimate of the change in fundamental value. Section

3.3 discusses the data we use. Section 3.4 provides initial evidence suggesting the

potential of our measure of fundamental value to explain the momentum effect. Section

3.5 investigates the momentum effect directly by showing how prices and fundamental

values of different momentum deciles change together over time from a year before the

formation of the momentum deciles until a year and a half after. Section 3.6 presents a

similar story for deciles ranked on change in value. Section 3.7 provides a check to see

whether our results could be explained by assuming that analysts' expectations simply

track past rates of return. Section 3.8 tests momentum hypotheses based on behavioral

arguments and estimates how far into the future professional investors appear to predict

fundamental value. Section 3.9 contains concluding comments and final remarks on the

literature.

3.2 Representing Changes in Fundamental Value with Analysts'

Estimates

Since our tests concern rates of return, we do not need to measure fundamental

value directly. We only need to measure the rate of change in fundamental value. We

then relate that to the rate of change in price. However, in order to motivate our rate of

change measure we will begin with a multi-period dividend discount formula. In

particular, we assume that V, the fundamental value per share of a firm's equity is given

by

v=Y-^— [l] ' £ ( l + r)' M

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Where Dit = annual dividends per share expected by the average or representative

investor, and r = risk-adjusted discount rate , or equivalently,

V, = - + 1±T + > " ", [21 ' 1 + r (1 + r)2 tl(l + r)1 LJ

Where Eit = annual earnings per share for the i,h firm in period t as expected by

the representative investor, and A.jt = dividend payout ratio as expected by the

representative investor, where we have assumed the first two X.'s are equal for simplicity.

We next assume that in many instances, news that effects fundamental value in a

cross-sectional analysis also effects expected earnings within in the next two years.

Implicit in this view is also the assumption that valuation-sensitive news that only affects

information beyond two years is less likely and often more difficult to assess, so its

impact on valuation is less.

Mechanically, denote the first two terms on the right hand side of [2] A and the

last term B. We assume B is proportional to A, i.e., B = yA, or

r,=(i + r) XEn + XEi2

1 + r (l + ry [3]

The discount rate used was fixed at 10%. Similar results were obtained using a fixed industry discount rate suggested by Fama & French, 1997, and with a time varying discount rate equaling 6% plus the 10 year Treasury bond rate.

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We define Ritv as the rate of change in fundamental value, or smoothed earnings

estimates. In a cross-sectional of stock returns, macro factors, such as the level of the

overall stock market, will be common for all stocks. As a result we assume that, cross-

sectionally, Rjtv will be proportional to the change in a firm's fundamental value .

l+Rltv~(Vlt/VlM). [4]

Notice that since X and (1+y) appear multiplicatively in both numerator and

dominator of [4] they are not required in the definition of Rtv. In sum, we approximate

the cross-sectional change in fundamental value by changes in expectations about firm-

specific, near-term events.

Analysts commonly estimate earnings for the current fiscal year, the following

fiscal year, and sometimes more. They typically estimate a long-run growth estimate as

well. The time interval we use in estimating Rjtv is six months, i.e., in [4], t is six months

after t-1. Our measure requires estimates for earnings one year hence and two years

hence. In each month, for each firm in our sample we estimate expected earnings one

year hence and two years hence using the following formulas.

Eit+1 = w-FYl + (1 - w)-FY2 and [5]

29 For a practitioner-oriented view of Rjt

v consider the following: let Pmt represent the average prices of the stocks in the market at period t. Let V^ represent the average of Vj, from [2], for all the stocks in the market. Then Pmt/Vmt is a type of market P/E ratio. Assume that the corresponding P/E ratio for each stock moves proportionally with the market's P/E ratio. Equation [2] then implies

1+Ritv = (Vi/Vu.0[(Pm/Vmt)/(PmtyVmt.I)]. [4']

Since we use [4'] cross-sectionally, the market term is the [4'] is the same for every firm. This implies that in cross-sectional analysis l+R," is proportional to the firm specific term, (VJ/VJM), or equation [4] above.

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Eit+2 - wFY2 + (l-w)-FY2-(l + LTG) [6]

Where Ejt is company i's expected earnings for period t constructed each month

with the weighted average of FY1 and FY2, the analyst average estimates30 for earnings

for fiscal year 1 and 2 respectively. LTG is the consensus long term growth31, and w is

the fraction of months remaining in the current fiscal year. Using [5] and [6], we create

monthly proxies for earnings one and two years ahead.

Defining Rjtv in this manner has several advantages. A six month interval allows

us to use a time interval used in many previous studies. A six month interval is also long

enough to allow meaningful changes in fundamental value.

In addition, smoothing earnings over two years allows us to update changing

expectations on a regular basis and track the relation between prices and expectations as

stocks in different momentum deciles evolve over time. The smoothing process also

makes it more likely that our measure will capture changes in relating to the firm's longer

run profitability. Thus, it helps mitigate issues relating to transitory earnings (Kothari,

2001).

However, because our measure of expectations looks out only two years, it may

miss the full impact of some important changes. For example, the discovery of an oil

field or a new drug may impact long-run fundamentals but may not affect near-term

30 We also computed very similar results using the median instead of the mean of FY1 and FY2.

31 When LTG is not available we assign the industry (three digit SIC) average long term growth rate, similar results obtained when assigning 0% to the missing LTG estimates.

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earnings estimates. In Section V we use an averaging process to reduce error in our

measure.

3.3 Data and Variable Construction

Our sample is composed of firms listed in the Center for Research in Securities

Prices (CRSP) tapes for the period of 1985 to 2006 for which there are also security

analyst estimates of future earnings in the IBES database. To be consistent with previous

literature, we focus on common equity and exclude REIT's, ADR's, limited partnerships,

and closed-end funds. Since the tests in this paper are based on holding periods of six

months,32 we include in our sample only firms that have analyst estimates for both fiscal

year 1 and 2 six months before the formation date of our momentum deciles and six

months after formation.

Table 3.1 contains the characteristics of our sample. In an average every month

we cover 1,977 firms. Before 1990, the average number of firms was 1,036. It rose to

2,674 in the second half of the 90's and then decreased to 2,318 in the following decade.

In terms of size and book-to-market quintiles, the sample appears slightly skewed toward

smaller low book-to-market stocks. However, the apparent skew is largely attributable to

the fact that the Fama French breakpoints are based on NYSE stocks, while our sample

includes Nasdaq and AMEX stocks as well. Panel F shows that the sample covers a wide

range of industries. Over the sample period the percent of manufacturing and non

durable industries decreased slightly, while business equipment increased.

Test periods of 3, and 9 months were also conducted with similar results.

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3.4 An Initial Look at the Evidence

In this section we show that, as expected, the momentum effect occurs in our data.

We also show the power of our rate of change in fundamental value to explain

contemporaneous returns.

First, both the momentum literature and the noisy rational expectations model

suggest that past returns should predict future returns. That is, bi in the following

regression should be positive (the momentum effect).

R i t=a, +b,Ri t., +e t [7]

Table 3.2 presents average monthly cross-sectional regression estimates, using six

month returns, so that Rjt represents one six month return and Rit.i represents the six

month return immediately preceding it with a one month lag between the two periods.

Given that the holding periods are 6 months long and the regressions are estimated every

month, the observations overlap. Therefore reported ^-statistics are computed with

Newey-West (1987) standard errors, using a lag length of one less than the holding-

period horizon. This statistic is designed to correct for moving average errors induced by

the overlapping observations. Equation [7] appears as the first regression in Panel A of

Table 2. The R2 is low 0.014, but the t Statistic is significant (5.19).

The remaining regressions in Panel A show that Rjt.2 is insignificant and Rit_3 has

a significant, but negative sign. The negative sign is consistent with the familiar reversal

effect in the momentum literature (e.g., Jegadeesh and Titman, 1993). We shall return to

it later in the discussion of momentum.

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The second regression of Panel B shows that when returns are regressed against

last period's change in fundamental value, Ri.tV, the R2 is even lower (.0046), though the

t Statistic is significant at 2.52. However, when last period's return is included in the

regression, its coefficient remains significant while the coefficient on Rj,t-iv falls to a third

of its former value and becomes insignificant (t = .97). This suggests that much of the

information in Ri,t-iv is subsumed in past return. Finally, Panel C includes regressions

where return is regressed on concurrent R / .

Ri t=a2 + b2Ri tv + e2t. [8]

R2 equals .11 and the t Statistic on R;/ equals a highly significant 16.9. We

believe this suggests that investors, whose activities are reflects in return, and analysts,

whose expectations are reflected in R;/, are reacting to the same news about future

corporate profitability. Further, when past return is added to the regression, the

coefficient on past return becomes insignificant, suggesting that the information in past

return is largely subsumed by the current change in fundamental value.

A noisy rational expectations interpretation of these results is that in period t-1,

informed investors correctly anticipated much of the information contained in next

period's R / . The trading activity of these informed investors causes Rt.i to partially

reflect next period Rv. Then in period t, analysts publish their expectations, and

uninformed investors trade based on the now public information in Rjtv. This explains the

momentum effect.

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In sum, Table 3.2 provides initial evidence consistent with the momentum effect,

the noisy rational expectations hypothesis and the power of our measure of fundamental

value to explain concurrent stock returns. The next section presents a more dramatic view

of these issues by tracing the time paths of both fundamental value and the returns of

stocks sorted into momentum deciles.

3.5 The Time Path of Momentum Deciles

3.5.A Returns

We create overlapping momentum deciles by ranking stocks each month

according to their trailing 6-month returns. We will call each 6-month ranking period,

Period 0, e.g., for the six month period beginning January, 2000 and ending June, 2000,

we rank the stocks as of their six month return at the end of June. We then calculate 6

month returns for the stocks in each Period 0 decile for five additional periods. The 6-

month period immediately proceeding the ranking period is Period -1 (in the example,

July, 1999 through December, 1999); the 6-month period before that is Period -2. We

skip one month between period 0 and the next 6-month period (Period 1), as is common

in the momentum literature, to avoid bid-ask bounce problems (as well as lags in

analysts' earnings changes). Immediately following Period 1 are the two final 6-month

periods, Periods 2 and 3. Counting the month we skipped after the ranking period, our

data covers 37 months for each momentum decile. Each month begins another

(overlapping) ranking period, In our example, the next Period 0 would start at the

beginning of February, 2000. To adjust for overlapping observations, we use Newey-

West standard errors to calculate our t-statistics.

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If the data met the test of strong form market efficiency, and assuming each decile

is well-diversified, a plot of the momentum deciles would look like Figure 3.1. In Period

0, the stocks are ranked into deciles by returns. Therefore in the ranking period, the

deciles differ by construction. In an efficient markets' interpretation, the return

differences in Period 0 reflect the idiosyncratic returns of the underlying stocks,

presumably the result of a news event. The market should fully reflect the news event in

Period 0, and that news should not affect returns in other periods.

In periods other than the ranking month, each decile simply represents a more or

less random sample of 10% of the stocks in the market. In these non-ranking periods,

each decile should earn the market rate of return. Only in the ranking period should the

deciles, by construction, differ from the market return.

Before and after Period 0, the returns of all deciles should equal the average

return in the market. Table 3.3 presents the actual results using our data. Figure 3.2,

which is based on the first panel in Table 3.3, presents a graphical summary of the actual

returns of the momentum deciles. It is similar to Figure 3.1, and, visually, suggests rough

correspondence with market efficiency. But Figure 3.2 differs from Figure 3.1 in a

number of important respects. We will start with Period 1, the ranking period, and move

forward in time before considering the earlier periods.

Period 0 is the ranking period. As expected, deciling stocks on 6-month returns

results in huge return differentials. Return differentials this large are most likely the

result of investors reacting to significant new information. The heterogeneity of the

returns suggests that the news is company-specific, or at most industry-specific.

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Period 1 shows the familiar momentum effect. The deciles in Period 1 line up

just as they did in Period 0. Stocks in decile 10 had higher returns than stocks in decile 9

and so on, monotonically down to decile 1. The difference in returns between deciles 10

and 1 is large, 8.1% per six month period and the t statistic of the difference is a

statistically significant 5.4.

A noisy rational expectations interpretation is that some investors became

informed and traded stocks in Period 0. Those stocks then subsequently out- or under-

performed in Period 1. We investigate this interpretation more fully below when we

present the changes in fundamental value over these periods.

By Period 2 the t statistic of top decile average return minus the bottom decile

return is negative and insignificant, and the returns within the deciles are roughly similar.

Deciles returns in Period 1 do not predict decile returns in Period 2. By the end of this

period, 13 months after the ranking period, the information that moved prices in Periods 0

and 1 seem to be fully imbedded in stock prices.

In Period 3, the familiar reversal effect is evident (as it was in the regressions of

Table 2). The decile returns are again monotonic, but in the opposite direction. Stocks

that 19 months earlier had the highest returns, now have the lowest and underperformed

the lowest momentum decile by a statistically significant -4.5% (t statistic = -3.7).

In part the reversal effect is probably due to survivorship. To be in the Period 3

analysis, a firm must survive until Period 3. The Period 3 returns in Table 3.3 and Figure

3.2, are not realizable returns, rather they are conditional returns, conditional on the

survival of the firm until Period 3. It is not surprising that bottom decile firms, who may

have flirted with bankruptcy, but survived, have high returns. However, survivorship

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does not seem to explain the swan dive taken by the former high flyers in the top deciles.

In the next subsection, we show the close relation of the reversal effect to fundamental

value and resume discussion of survivorship issues.

Next, consider the returns in Period -1, the period before the ranking period. The

decile returns in Period -1 or any period before Period 0 are not realizable as an

investment strategy since they are based on a sort that occurs in Period 0. Nevertheless,

as we argued above, under the hypothesis of Market Efficiency as depicted in Figure 1,

the decile returns in Period -1 should be independent of those in Period 0. However they

are not, and the actual relationship between Period 0 and Period -1 might be called the

reverse momentum effect. The top decile resulting from the ranking in Period 0

outperforms the bottom decile in Period -1 by 4.7% (t statistic = 3.12). Though

somewhat peculiar, this reverse momentum effect would seem as great a challenge to

market efficiency as the more familiar momentum effect. On the other hand, a noisy

rational expectations interpretation of this reverse momentum effect is that, in Period -1 ,

informed investors successfully traded stocks that subsequently out- or under-performed

in Period 0.

Although the payoffs to these informed Period -1 investors seem large, they

reflect the mechanical construction of the momentum deciles constructed in Period 0.

Many stocks had returns as high (or low) as the extreme decile portfolios did in Period -1.

On average these stocks did not earn extreme returns in Period 0. We are simply looking

at the stocks that subsequently did earn high returns. Nonetheless, even for these stocks,

Market Efficiency implies there should be no reverse momentum effect.

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Period -2 is odder still. A year before the ranking period, the extreme deciles all

had high returns while the middle deciles had below average returns, the difference

between top and bottom deciles is -2.8% but it is statistically insignificant. To cast

further light on the time path followed by the momentum deciles, the next subsection

shows the relation between momentum and R / , our measure of the change in

fundamental value.

The third panel of Table 3.3 shows that adjusting for risk has little effect on the

results. Excess returns relative to Fama French size and book-to-value portfolios show

the same results discussed above, although in most cases the t statistics, though

significant, are less so. For example, in Period 1 the excess return from top minus bottom

momentum deciles is 6.34% with a t-statistic of 4.01; while in Period 2 the alphas are

statistically insignificant, but in Period 3 they show a reversal effect of -3.52% with a

significant t-statistic of 3.04.

3.5.B Fundamental Value by Momentum Deciles

For each of the above momentum deciles, Figure 3.3 and the second panel of

Table 3.3 present each decile's average Rv, our estimate of the change in fundamental

value. They trace the path of the Rv's for each momentum decile from a year and a half

before the formation of each momentum decile (period -2) until a year and seven months

after formation (period 3).

If there were no predictability in the time series of the Rv's, Figure 3.3 would look

like Figure 3.1. However, though Figure 3.3 resembles Figure 3.2, it does not look like

Figure 3.1. The monotonicity of the Rv's in Periods 0 and 1 suggest predictability in the

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time series of analysts earnings revisions and thus in our Rv's. Further, there seems to be

greater predictability in the high momentum deciles, which had above-average Rv's for

Periods -1,0, 1 and 2, or a little over two years.

The individual periods tell an interesting story. As before, we will begin with

Period 0 and work forward before considering the pre-ranking periods. In Period 0 the

Rv's for the different momentum deciles line up in exactly the same order as did their

stock returns. The highest momentum decile had the highest increase in fundamental

value exceeding that of the lowest decile by 56.2%. The difference is statistically

significant (t statistic = 28.3)33. This suggests that the primary driver of the large

differences in the Period 0 returns is captured by the estimated change in fundamental

value that also occurred in Period 0.

As mentioned in the previous section, there is modest evidence that analysts'

estimates, and thus our Rv's, may be affected by stock price changes. While some trend

following may account for a little of our measure of changing fundamental value, it is

unlikely to explain much. The increases (decreases) in fundamental value in the extreme

deciles in Period 0 appear too large for an analyst to justify if there is no fundamental

evidence supporting such dramatic changes.

Further, the monotonicity of the Rv's would require that most, if not virtually all

analysts, ignore fundamentals and change their earnings estimates to correspond to price

moves. Finally, if price movements were driving changes in analysts' expectations, we

would expect to find long discussions of momentum in analysts' reports. Instead, they

Compared with the actual returns for the difference in momentum deciles, this return is relatively low. This phenomenon is similar to the low values of "earnings response coefficients" in the accounting literature (Kothari, 2001). It also reflects that the extreme decile stocks are largely growth stocks as we show below.

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focus on company prospects for future earnings, cash flow and the relation of price to

fundamentals.

Similarly, the changes in fundamental value in Period 1 mirror the returns in

period 1. However, the Period 1 fundamental returns are larger than the price returns in

Period 1, and are monotonically in line with the momentum deciles of Period 0. A noisy

rational expectations interpretation is that the price changes in Period 0 reflect not only

the change in fundamental value occurring in Period 0 but also the expectations by

informed investors that fundamental value will continue to change in Period 1. As the

information underlying those expectations becomes more widely apparent, analysts, and

thus Rv, reflect it. The informed investors benefit from their foresight and trading activity

as the returns in Period 1 validate their Period 0 trades (the momentum effect).

There may be a second, more mechanical reason, for the wider spread in the Rv

deciles in Period 1 that is related to the way we construct Rv. As equation [3] shows, our

measure depends only on expected earnings over the next two years. It may be that some

of information expected by analysts in Period 0 effect earnings shortly beyond two years.

By Period 1 some of that information affects our measure of Rv and causes the wider

dispersion of the Period 1 deciles. This argument may also affect Rv in Period 2.

A similar phenomenon, "prices lead earnings," is familiar in the accounting

literature (Kothari, 2001). There it refers to reported earnings, which are reported later

than the earnings expectations we analyze. Figure 3.3 suggests that "prices lead analysts'

expectations of future earnings." A final interpretation is that analysts expectations

simply follow past price change, an issue to which we return in Section 3.7.

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In Period 2 the pattern of the Rv's, partially extends the pattern of Period 1, but

the extreme deciles have begun moving toward the mean. By Period 3, which ends over

a year and a half after the momentum deciles were established, a reversal in the Rv's is

evident, which corresponds with the reversal in their stock returns. The bottom four

deciles have an average Rv of 7.3%, while the top six have an average of 3.9%.

Fundamental value in Period 3 suggests that the reversal in price returns is not a

return to rational pricing after a period when prices overshoot fundamentals (as suggested

by e.g., Daniel et al, 1998). To the extent that the results are not due to survivorship

bias34, investors appear to react to changing fundamentals. The expectations of these

fundamentals may be over-extrapolations, but this data suggests the return reversal

mirrors changing fundamentals. Notice too that these changes in fundamental value

cannot be interpreted as the result of analysts changing their earnings expectations to

ratify past returns.

3.6 Ranking Stocks into Rv Deciles

In this section instead of ranking stocks by returns in Period 0, we rank them into

deciles based on Rv, the percent change in fundamental value. Then, just as we did in the

previous sections, we follow these deciles through time in terms of both Rv and R. If our

hypotheses are correct, a more powerful link between stock price and fundamental value

should be apparent when we sort the stocks into Rv deciles. This is because our measure

We were able to reduce the reversal effect to a degree by revising the initial way we did the study. Initially, to be included in any period, a firm had to be in the sample for the entire period. In our revision, we required only that a firm be in the sample during periods 0 and 1. We assumed any proceeds from the last price at which the firm traded were invested in the remaining stocks in its decile. Its revised Rv equaled its R. This reduced survivorship and reversal, but as is apparent in the Tables and Figures, the reversal remains.

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of fundamental value, Rv, is based on near term earnings estimates and will be most

powerful when information about fundamentals changes near-term earnings (the next two

or three years).

When we ranked on returns, the extreme deciles likely contained, not only stocks

with large changes in near-term earnings estimates, but also stocks whose valuation-

sensitive information focused only on longer term cash flows. That is, the extreme

deciles in the R ranking likely contained stocks that either had won or lost long-term

contracts, had drugs or other products approved or not, found extensive mineral deposits,

etc. Since our measure is unlikely to respond to that type of information, we would

expect that in momentum rankings the link between stock price and fundamentals will

appear weaker than it actually is.

However, when we rank stocks by Rv, it is likely that stocks whose returns have

only been affected by changing long run fundamentals will be randomly distributed

among the deciles. In an Rv ranking, Rv is more likely to be the primary driver of stock

returns, and if the noisy rational expectations hypothesis is correct we should expect that

prices should appear to be a better predictor of changes in fundamental value. Note: Jim I

think that you saying that Rv is the driver could be interpreted as predictability????

Table 3.4 and Figures 3.4 and 3.5 show the time path of Rv and R. Figure 3.4

shows the path of fundamental value. In Figure 3.4 the changes in fundamental value

seem less predictable than in the momentum ranking. Here it looks as if analysts were

surprised, particularly in the extreme deciles. If the Rv's were independent of each other,

Figure 3.4 would look like Figure 3.1. However, although it is similar, the highest

deciles in Period 0 are also the highest in Periods -2 through Period 1. The lowest two

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deciles in Period 0 were also the lowest in the two years beginning in Period -2.

However mirroring Figure 3.3, by Periods 1 and 2, the lowest decile in Period 0 is now

the highest in terms of changing fundamental value.

The reversal in the lowest decile appears sooner than was apparent in the

momentum sorts. In particular, the lowest decile bounces back in the period immediately

following the ranking period. Further, as Table 3.4 and Figure 3.5 show, the

corresponding stock return for that decile do not reverse until periods 2 and 3. This may

be related to excessive pessimism by analysts about bottom decile stocks in Period 0 or to

major earnings write-downs by managers, which analysts subsequently corrected in

Period 1. It is important to note that returns in Period 2 and 3 in excess of Fama French

portfolios are all statistically insignificant.

Figure 3.5 shows the returns corresponding to the value deciles and these returns

appear consistent with a noisy rational expectations market. Compared to Figure 3.2,

prices predict the forthcoming Period 0 changes sooner and to a larger extent.

Recall that the returns in Figure 3.2, where stocks were ranked on the returns

themselves, approximated market efficiency. On the other hand, Figure 3.5, which shows

the returns when stocks are ranked by the change in fundamental value, does not look like

market efficiency at all. High (low) ranking deciles in Period -2 continue to be high

(low) ranking deciles for the next three six month periods.

An interpretation of Figure 3.5 in the spirit of noisy rational expectations is that,

as early as Period -2, some informed investors predicted the large changes in fundamental

value that security analysts reported in Period 0. Then, in Period -1 , additional investors

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became informed (and/or the previously informed investors become more confident)

causing Period -1 returns to deviate even more from average.

Security analysts then disseminate the information in Period 0 and the price

moves dramatically as most of the uninformed investors react. Finally, a few of the

uninformed do not react until Period 1, causing a small momentum effect in Period 1.

Simple correlations between decile returns are consistent with a stronger link

between current returns and future changes in value when using an Rv ranking. Consider

the correlations of R in one period with Rv one or two periods later. For the two period

ahead prediction, this indicates how well returns in one six month period, for clarity, say

January to June, predict changes in fundamental value the following January to June. For

the momentum deciles (Figures 3.2 and 3.3), the correlation for one period predictions is

.63; for two periods, .17. For the fundamental deciles (Figures 3.4 and 3.5) the

correlations rise to .73 and .29.

3.7 Do Prices Predict Fundamentals or Do Analysts Chase Prices?

The results so far, and particularly Figure 3.5, suggest that prices lead analysts'

expectations and, thus our measure of Rv. We believe the data suggests a noisy rational

expectations equilibrium. That is informed, or professional, investors either obtain

information more quickly or analyze existing data better than the average analyst. These

professional investors make their decisions and move prices before analysts publish their

opinions.

Nevertheless, another interpretation is that analysts' expectations of earnings

simply follow prices. If say, a stock's return relative to the market is positive, analysts

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increase their published expectations. Under this interpretation, the high concurrent

correlation between returns and our measure of the change in fundamental value simply

reflects analysts publishing expectations that ratify recent price changes.

We will call this possibility the extrapolative hypothesis. That is, current changes

in analysts' expectations move in the same direction as lagged returns . It is not possible

to cleanly distinguish the extrapolative hypothesis from a noisy rational expectations

explanation because both hypotheses imply some degree of extrapolation. The

extrapolative hypothesis implies that all a security analyst does, On the other hand, if

rational expectations are noisy, analysts base forecasts on their assessment of company

fundamentals. However, since they realize that returns convey information about current

and future fundamentals, their expectations should also reflect lagged returns.

It is relatively easy to cast substantial doubt on an extreme version of the

extrapolative hypothesis, particularly over the six month intervals we have investigated.

In particular, suppose that over the past six months a stock's return has been relatively

high. If the extrapolative hypothesis is true, then analysts' should increase their

expectations of earnings, regardless of what is happening currently.

For example, suppose we look at the change in earnings expectations one month

after the end of the six month ranking period. If a company's lagged return was

relatively high, then regardless of what is happening currently, analysts' expectations

should rise and should be independent of the actual return over that one month interval.

35 There is some evidence prior stock movements partially explain changes in analyst's earnings expectations, but that fundamentals are more important. For example, Stickel (1990) shows that a stock's return over the interval between an analyst's prior estimates and her current estimate has statistically significant explanatory power. However, he also shows that its importance is small and weak (R2 increases from .37 to .38), and that the behavior of other analysts is far more dominant. Subsequent work, such as that by Keane and Runkel (1998) and others (e.g., Lim, 2001) suggest that analysts' earnings estimates are focused on predicting earnings and not driven primarily by stock prices.

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That is, if we look at high momentum stocks, the change in analysts' earnings estimates

should be reflect past return, but not current return.

On the other hand, if the noisy rational expectations view is correct, we should

expect the current change in analysts' expectations to reflect both past and current

returns. And as the observation period after the ranking period lengthens from one month

to three and six months, the more analysts' expectations should appear to track

concurrent rather than past returns.

To investigate this, we independently sort stocks each month into deciles based

their past six month return (momentum deciles) as well as their realized returns over the

next 1, 3 and 6 months. We then calculate the average realized Rv's (change in value, as

measured by the change in analysts' expectations) for each of the 100 bins for the

following 1, 3 and 6 month periods. Each bin has between 12 and 30 firms on average.

Table 3.5 shows how Rv, our measure of fundamental value changes over each of those

intervals.

Table 3.5 does not support an extreme version of the extrapolative hypothesis

over any subsequent horizon. Panel B summarizes the results of the first panel in Table

3.5. Instead of deciles, it simply divides the sample in half, the five top deciles and the

bottom five. The six numbers in the top left corner show what happens to the stocks that

had relatively high rates of return in the last six months, and also had relatively high

returns in the subsequent 1,3 or 6 month period. The top line shows that stocks that did

well in both the initial six month period and in the subsequent one month period a return

in that subsequent month of 7.7% above the equally weighted average of all stocks in our

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sample. Rv was also above average for those stocks, as we would expect either under

either hypothesis.

However, follow the top line across the table to the low return quadrant to the

high momentum stock that underperformed in the subsequent one month period. Already

in this first subsequent month the difference in analysts' expectations suggests a noisy

rational expectation explanation rather than the extrapolative hypothesis. The Rvt+i is

below average and significantly below the average for all high momentum stocks (t stat =

-12.6). If analysts were simply extrapolating past returns, the t statistic would have been

0 (and the change in value would be above average).

The remaining cells in the Table 3.5 also support the noisy rational expectations

explanation. For stocks where momentum works (the upper left six cells and the lower

right six cells) fundamental value tracks both past and present return. For the cells in the

off diagonals, fundamental value tracks concurrent price change more closely as the

observation interval increases. By the time six months have elapsed, which is the

window we used for our analysis, the differences in Rvt+i are striking.

Nevertheless, even at six months, Table 3.5 suggests that the stocks for which

momentum did not work still reflect some of the initial momentum. Perhaps some

analysts are still extrapolating the optimism or pessimism in past returns, or perhaps they

have not yet observed the evolution of fundamentals expected by professional investors.

3.8 The Prediction Horizon of Informed Investors

The theoretical arguments and empirical results suggest that, because of the

activity of professional investors, prices anticipate future changes in fundamental value.

162

If so, current stock price should reflect not only current fundamental value but future

fundamental value as well. Current price should also reflect the activity of noise traders

whose actions are independent of any estimates of fundamental value.

It is simplest to capture this in a multiplicative model, so we can then take

logarithms and derive a regression equation. Let Pt and Vt represent current price and

fundamental value respectively.

In a perfect market, with homogeneous expectations, where Vt accurately

measures those expectations, Pt = Vt and, over any period of time, Rt = Rvt . However,

suppose that all investors know the current Vt while a few can accurately predict Vt+i.

Then current price will be a function involving the beliefs of those who know only Vt

and those who know Vt and Vt+i (and thereby Rvt+i). The pricing function will also

reflect the number of investors in each group, their wealth levels, aversion to risk and any

possible constraints they face. In this case we might represent current price as

Pt = V t(l+Rvt+i)pl, [9]

Where and Pi reflect the relative effect of the two types of investors on price. If

all investors know Vt and no one knows Vt+i, then (3, = 0. If all investors know both Vt

and Vt+i, then p, - 1. If some know Vt+i and other do not, then p, will lie between 0 and

1. Notice that this is a different way of presenting our view of how the momentum effect

works. At any time, the current price reflects not only current fundamental value (as

perceived by the representative investor) but future value as well.

163

We can generalize equation [9] to allow some investors who may not know even

Vt, as well as some investors who can predict not only Vt+i but Vt+2 or Vt+3. Assuming

that a noisy rational expectations equilibrium is a reasonable approximation to reality, it

is interesting to ask how far into the future can investors and thus current prices anticipate

future fundamental value. Let p b (52 and P3, reflect the relative importance of future

changes in fundamental value in determining current price. Also let p0 and X be

parameters to reflect the relative importance of traders whose decisions do not depend on

any estimates of fundamental value ("noise traders"). In the following equation, it is

reasonable to expect Pi > P2 > P3-

Pt = 7LtVtP(,(l+Rv

t+i)p,(l+RV2)P2(l+Rvt+3)P3et [10]

Since 1+Rt = Pt /Pt_i, taking logarithms, differences, and using lower case letters

to denote a logarithmic variable, e.g., r = ln(l+R), yields

r, = a + p0rvt + pi Ovm -rvt) + p2(rvt+2-rvt+i) + P3(rvt+3- rVt+2) + u t , [11]

Where we reflect the influence of noise traders by a, a constant, and ut, a random

error term. Since ut = ln(et) - ln(et), we would expect it to display negative

autocorrelation if we were to estimate [11] in a time series. However, we estimate the

equation cross-sectionally.

164

The following two regressions show that Pi and (32 > 0, but p3 < 0. The positivity

of pi and P2 suggests that current prices impound (accurate) predictions of future changes

in expected fundamental value one year hence (two periods).

Months 250

244

R i t = a

Intercept 0.0704 4.89

0.0740 5.09

+ /?0Rv,t

Rv,t 0.3433 18.94

0.3456 16.90

+ /?1dRVit+1

dRvt+ i 0.1434 13.46

0.1291 9.79

,+ /?2dRv

dRv.f2 0.0383

6.27 0.0109

1.08

,t+2 + /?3dRv,t+3 + e i , t

dRvt+3 Avg # Obs 1900

-0.0183 1720 -2.45

R2

0.12339

0.1329

The negativity of P3 is consistent with the familiar reversal effect and is

inconsistent with rational expectations. However, as different regressions involve more

and more periods in the future, there is a survivorship issue - the sample size keeps

decreasing. Although we have used standard procedures to deal with this, it should be

borne in mind that a regression requiring rvt+3 is a conditional regression. That is, the

observations only involve firms that have survived more one year after the observation of

rt. If a number of firms have gone bankrupt, or were delisted, during this period,

although they were properly represented in prior regressions, they are not represented in

this one. This suggests that p3 may be biased downward and that the suggested reversal

may be more apparent than real.

3.9 Conclusion

This paper argues that stock returns are closely linked to current changes in

fundamental value and anticipate future changes in fundamental value. The behavior of

165

price and value is consistent with what one would expect in a rational well-functioning

stock market where information is costly, expectations are heterogeneous and some

investors are better informed than others. More narrowly, our data is consistent with a

heterogeneous expectations equilibrium, and more narrowly, a noisy rational expectations

equilibrium.

The noisy rational expectations model is attractive in terms of assumptions and

implications. In the first place, it requires the plausible assumption that the acquisition

and interpretation of information about equity pricing requires skill and resources. In the

second, it implies that the less well informed investors base their investment decisions, in

part, on past returns

While our work supports the notion that the evolution of prices is consistent with

the above models as well as with present value theory, it does not support the efficient

markets hypothesis. Prices do not fully reflect all available information. Instead, the data

suggests that prices partially reflect two types of information: (1) information that is

readily available (from analysts' expectations) and interpretable by a large group of

investors, and (2) the predictions of a group of "informed" investors, who rely on

information that is harder to obtain and/or more difficult to interpret.

The momentum effect in this interpretation is due to the fact that informed

investors move prices in anticipation of information that other investors will learn of in

subsequent periods.

This interpretation of price and value has implications beyond momentum, post

earnings announcement drift and the broader anomalies literature. It suggests that the

behavioral hypotheses that rely on investor reactions to past price movements may be less

166

important than hypotheses about how investors form expectations about stock

fundamentals.

It may also relate to the literature on the excess volatility of the equity market

(see, e.g., Shiller, 2003). Much of that work is based on a measure of fundamental value

equal to the present value of the future dividends of companies that survive. Stock prices

appear to move far too much relative to the slow and smooth movements of discounted

actual dividends. The results here suggest that, at the individual stock level, the volatility

of the fundamental value is far larger than suggested by discounted dividends. It may be

that, even in the aggregate, fundamental values are more volatile and more closely tied to

stock prices. If better estimates of fundamental value are highly volatile, perhaps the

stock market behaves appropriately, in that stock prices reflect estimates of fundamental

value. However, it may also be true that investors' estimates of fundamental value are

excessively volatile.

167

Table 3.1: Sample size and distribution mapped into Fama French quintiles and 12 industry classifications

The sample is composed of common equity firms that have both IBES earnings estimates for fiscal year 1 and year 2, and that can be matched to CRSP tapes. Every month firms are classified into their corresponding Fama French book to market and size quintile based on the information available on the last month of June. Panel A presents the average number of firms analyzed per month, the 5x5 size - book to market grid and the average for each quintile. Panels B to E show the sample distribution for different time periods. Panel F shows the sample distribution when firms are classified using Fama French 12 industry sectors.

Panel A: Sample Distribution from 1985 to 2006 Avg # Firms

1977 Low B/M High B/M Total Size Small

2 3 4

Big

6.1% 6.5% 5.7% 4.7% 5.4%

6.4% 5.6% 4.2% 3.6% 3.1%

6.1% 5.0% 3.7% 3.0% 2.3%

6.0% 3.9% 2.6% 2.2% 1.8%

5.9% 2.5% 1.5% 1.2% 1.1%

30.5% 23.5% 17.7% 14.6% 13.7%

Total B/M 28.5% 22.8% 20.2% 16.4% 12.0% 100.0%

Panel B: Sample Distribution from 1985 to 1989 Avg # Firms

1036 Low B/M 2 3 4 High B/M Total Size Small

2 3 4

Big

7.3% 8.0% 6.6% 4.1% 4.5%

7.4% 5.8% 3.9% 3.5% 3.3%

5.3% 4.7% 3.5% 3.3% 3.1%

3.8% 2.7% 2.5% 2.4% 2.7%

3.8% 2.3% 1.9% 1.5% 1.8%

27.7% 23.6% 18.5% 14.8% 15.5%

Total B/M 30.6% 24.0% 19.9% 14.2% 11.3% 100.0%

Panel C: Sample Distribution from 1990 to 1994 Avg # Firms


2 3 4

Big

6.6% 7.2% 5.7% 4.4% 4.6%

6.2% 6.5% 4.4% 3.2% 3.1%

4.7% 5.3% 3.8% 3.1% 2.6%

4.8% 3.9% 2.8% 2.4% 1.6%

6.3% 3.3% 1.4% 1.2% 0.7%

28.6% 26.2% 18.2% 14.4% 12.6%

Total B/M 28.6% 23.5% 19.6% 15.5% 12.8% 100.0%

Panel D: Sample Distribution from 1995 to 2000 Avg # Firms


2 3 4

Big

5.3% 6.0% 5.6% 5.1% 6.0%

5.8% 4.9% 4.2% 3.6% 3.0%

7.1% 4.9% 3.5% 2.8% 1.8%

7.5% 4.3% 2.6% 2.0% 1.2%

7.4% 2.4% 1.3% 1.1% 0.8%

33.1% 22.5% 17.1% 14.6% 12.7%

Total B/M 28.0% 21.4% 20.1% 17.6% 12.9% 100.0%

Panel E: Sample Distribution from 2001 to 2006 Avg # Firms


2 3 4

Big

5.6% 5.0% 5.0% 5.0% 6.3%

6.3% 5.2% 4.4% 3.9% 3.0%

7.3% 5.2% 3.9% 2.9% 1.9%

7.5% 4.7% 2.4% 2.0% 1.6%

5.8% 1.9% 1.3% 1.0% 1.1%

32.4% 22.0% 17.0% 14.7% 13.9%

Total B/M 26.9% 22.7% 21.2% 18.2% 11.0% 100.0%

Panel F: Sample Distribution Per Industry Sector Industry (1985-2006) (1985-1989) (1990-1994) (1995-2000) (2001 - 2006)

Non-Durables Durables Manufacturing Energy Chemicals BusinessEquip Telecom Utils Shops

Health Finance Other

8.45% 2.03% 10.26% 3.74% 2.51% 16.99% 1.52% 3.89% 12.49%

7.31% 18.83% 11.99%

10.68% 2.35% 11.68% 3.03% 3.48% 15.89% 1.16% 4.16% 12.18%

6.04% 18.24% 11.12%

9.49% 1.99% 10.33% 4.27% 2.75% 14.68% 1.36% 4.17% 13.43%

8.18% 18.18% 11.18%

7.29% 1.99% 10.07% 3.71% 2.01% 18.95% 1.55% 3.62% 12.63%

7.13% 18.42% 12.64%

6.70% 1.80% 9.06% 3.94% 1.95% 17.97% 1.98% 3.69% 11.74%

7.88% 20.45% 12.84%

169

Table 3.2: Returns and Change in Value, Cross-Sectional Evidence

The table presents time series average of monthly cross-sectional regression coefficients used to test the validity of the Grossman Stiglitz hypothesis. The test is conducted for return periods of six months as well as change in value over six months, therefore each subscript indicates a six month period i.e. R;,t-i is the return over the last six months for firm i and Rit.2 is the return from t - 12 months to t - 6 months. The means are computed with overlapping observations, therefore t-statistics are computed with Newey-West (1987) standard errors with a lag length of one less than the holding period horizon in months. Panel A presents mean estimates for the regression explaining current return by past returns (momentum). Panel B presents estimates for the regression explaining current return by past change in value, concurrent change in value, past returns and concurrent SIC industry return.

Panel A: Rj,t = a + KiRi,n + K2RU-2 + K3RU-3 +e\,t

Months Intercept RjiM R,^ Ri,t-3 Avg # Obs R

251

245

239

245

239

239

0.0653 4.43

0.0704

4.81 0.0744

5.04 0.0636

4.44 0.0738

5.09 0.0670

4.73

0.0688 5.19

0.0623 4.69

0.0584 3.95

0.0053

0.44

0.0039 0.34

0.0063 0.50

0.0047 0.39

-0.0368 -4.27

-0.0357 -4.34

-0.0340 -4.20

1956

1764

1615

1763

1605

1604

0.0140

0.0106

0.0085

0.0233

0.0185

0.0315

Panel B: RM = a + /3,Rv>t., + &Rv,t.2 + /33RV),.3 + YiRi,,-, + yiî,t-i + Y3RM-3 + f M

Months Intercept RM_, R,,t.2 R ^ RV,M R ^ j Rv?,_3 Avg # Obs R2

251 0.0653 0.0688 1956 0.0139

4.43 5.19 251 0.0717 0.0147 1956 0.0046

4.78 2.52 251 0.0653 0.0659 0.0046 1956 0.0171

4.45 5.12 0.97 245 0.0633 0.0596 0.0066 0.0037 -0.0115 1765 0.0286

4.48 4.39 0.55 0.77 -2.38 239 0.0668 0.0540 0.0038 -0.0317 0.0087 -0.0058 1616 0.0361

4.73 3.65 0.31 -4.13 1.59 -1.11 239 0.0670 0.0538 0.0032 -0.0332 0.0086 -0.0051 0.0027 1616 0.0389

170

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Figure 1 Hypothetical R e t i m s for Momentim Deciles Under the AssLrrption of Market Efficiency

100%

75%

50%

25%

0%

-25%

-50% Reriod-2 Fteriod-1 FteriodO Period 1 Period2 Fteriod3

Period 0 is the Ranking Period

-LowNbm- - 6 - - 8 9 -Hg f iNbm

Table 3.1: Hypothetical Returns for Momentum Deciles

Figure 2 Returns for Momentum Deciles

Period -2 Period -1 Period 0 Period 1 Period 2 Period 3


- Low Mom -•— 2 -8 — 9 - High Mom

Table 3.2: Returns for Momentum Deciles

Figure 3

Change in Value (Rv) for Momentum Deciles

Period -2 Period -1 Period 0 Period 1 Period 2


Period 3

- Low Mom -•— 2 -8 - Ugh Mom

Table 3.3: Change in Value for Momentum Deciles

172

Table 3.3: Returns and Change in Value for Momentum Portfolios

The table presents average returns and average change in value (Rv) for ten momentum portfolios. In period 0 stocks are sorted in deciles based on their six month returns. Periods -2 and -1 show the returns and change in value that the portfolios formed in Period 0 had prior to being formed. Period 1 is the six month holding period, which to avoid bid-ask problems and any lags in analysts' earnings changes starts one month after the formation period. Periods 2 and 3 correspond to six months post holding period.

Returns for Momentum Deciles (1985 -2006)

Low Mom 2 3

4

5

6

7 8 9

High Mom High - Low

Period -2

t-18 tot-12 14.57% 11.11% 9.54%

9.29%

9.33%

9.39%

9.68% 10.10% 11.38% 11.74% -2.82%

Period -1

t -12tot-6 10.08% 8.90% 8.56%

8.95%

8.43%

8.63%

8.88% 9.57% 10.85% 14.80% 4.73%

Period 0

t-6 to t -37.32% -18.19% -9.15%

-2.59%

3.07%

8.58%

14.61% 22.19% 33.96% 73.09% 110.40%

Period 1

t+1 to t+7 3.23% 5.09% 6.26%

7.03%

7.17%

7.45%

7.86% 8.36% 8.87% 11.33% 8.10%

Period 2

t+7 to t+13 7.24% 6.76% 6.97%

7.41%

7.32%

7.35%

7.55% 7.69% 7.03% 7.15% -0.09%

Period 3

t+13 to t+19 10.14% 8.28% 8.01%

7.72%

7.47%

7.63%

7.26%

6.98% 7.02% 5.63% -4 .51%

Low Mom 2 3

4

5

6 7 8 9

High Mom High - Low

Change in Period -2

t-18 tot-12 18.56% 11.78% 10.29%

8.97%

8.40%

8.31%

9.31% 9.27% 10.92% 12.02% -6.55%

Value (Rv) for Momentum Deciles (1985 - 2006) Period -1

t-12 to t-6 14.94% 10.91% 8.30%

8.33%

7.58%

8.38%

8.76% 9.66% 12.30% 17.52% 2.58%

Period 0

t-6 t o t -12.86% -1.17%

2.59%

5.59%

7.52%

10.18%

11.67% 14.80% 22.47% 43.36% 56.22%

Period 1

t+1 to t+7 -5.24% 0.71% 3.01%

4.32%

6.22%

6.71%

7.72% 9.75% 12.90% 20.21% 25.46%

Period 2

t+7 to t+13 0.95% 2.88% 3.50%

4.92%

3.54%

4.40%

6.05% 6.45% 7.24% 9.86% 8.91%

Period 3

t+13 to t+19 8.42% 7.54% 6.06%

7.02%

3.99%

4.27%

3.81% 3.77% 3.59% 3.93% -4.49%

Excess Returns to Fama French Size & Value Porfolios (1985 -2006)

Low Mom 2 3 4

5

6

7 8 9

High Mom High - Low

Period -2

t-18 tot-12 3.29% 0.48% -0.42% -0.54%

-0 .61%

-0.55%

-0 .21% -0.22% 0.62% 0.00% -3.29%

Period -1

t-12 to t-6 1.08%

-0.54% -0.45% -0.13%

-0.50%

-0.42%

-0.35% 0.26% 1.20% 4.09% 3.02%

Period 0

t-6 t o t -42.80% -24.18% -15.87%

-9.80%

-4.70%

0.21%

5.70% 12.53% 23.26% 60.25% 103.05%

Period 1

t+1 to t+7 -2.29% -1.36% -0 .61% -0.02%

0.05%

0.30%

0.49% 0.99% 1.58% 4.05% 6.34%

Period 2

t+7 to t+13 0.39% -0.22% -0.15% -0.19%

-0.08%

-0.02%

0.16% 0.26% -0.24% 0.02% -0.37%

Period 3

t+13 tot+19 2.76% 0.50% 0.49% 0.16%

-0.02%

0.02%

-0.12% -0.15% -0.18% -0.76% -3.52%

173

Table 3.4: Returns and Change in Value for RV,M> Value Portfolios

The table presents average returns and average change in value (Rv) for ten value portfolios. In period 0 stocks are sorted in deciles based on their change in value over six months. Periods -2 and -1 show the returns and change in value that the portfolios formed in Period 0 had prior to being formed. Period 1 is the six month holding period, which to avoid bid-ask problems and any lags in analysts' earnings changes starts one month after the formation period. Periods 2 and 3 correspond to six months post holding periods.

Low R ^

2 3 4 5 6 7 8 9

High Rv,w

High - Low

Returns for Rv

Period -2 t-18 tot-12

3.22%

5.68% 7.03% 8.00% 9.26% 11.21% 13.56% 16.09% 18.43% 13.96% 10.74%

Period -1 t -12tot-6

-8.16% -1.10% 2.93% 5.82% 8.23% 10.71% 13.59% 18.21% 23.70% 26.50% 34.66%

^ Value Decile (1985 - 2006) Period 0 t - 6 t o t

-15.07%

-5 .91% -0 .01% 3.97% 6.85% 9.73% 13.05% 17.05% 23.31% 35.15%

50.22%

Period 1 t+1 to t+7

5.42% 5.94% 6.34% 6.79% 7.04% 7.72% 7.63% 7.66% 8.88% 9.21%

3.79%

Period 2 t+7 to t+13

8.79% 7.49% 7.32% 6.99% 7.20% 7.20% 6.99% 7.04% 7.09% 6.35% -2.45%


9.49% 8.05% 7.32% 7.35% 7.28% 7.35% 7.21% 7.28% 7.14% 7.56% -1.92%

Low RViM

2 3 4 5 6 7 8 9

High Rv,,.6 High - Low

Change in Period -2

t-18 tot-12 10.21%

6.79% 5.69% 6.00% 7.10% 8.44% 10.59% 13.08% 18.82% 24.22%

14.01%

Value (Rv) for Rv>6 Value Decile (1985 - 2006) Period -1 t -12tot -6

10.17%

6.93% 6.63% 6.46% 7.13% 8.49% 10.09% 13.16% 18.04% 21.35% 11.18%

Period 0 t - 6 t o t

-43.65% -15.30% -5.29% 0.32% 4.15% 7.48% 11.20% 16.48% 26.64%

102.32% 145.96%

Period 1 t+1 to t+7

19.19% -0.29% 0.25% 1.86% 2.83% 4.48% 5.87% 7.29% 10.02%

14.73% -4.46%


13.45% 4 .11% 1.36% 1.97% 2.46% 3.23% 4.23% 4.53% 5.67%

10.73% -2.72%


16.78%

6.75% 3.69% 2.98% 4.45% 3.51% 3.82% 3.83% 3.87%

4.08% -12.70%

Excess Returns to Fama French Size & Value Porfoli

Low R v M

2 3 4 5 6 7 8 9

High R v W

High - Low

Period -2 t-18 tot-12

-7.12% -4.33% -2.80% -1.63% -0.53% 1.21% 3.07% 5.36% 6.35% 1.81% 8.93%

Period -1 t -12tot -6 -16.74%

-9.68% -5.73% -3.14% -0.75% 1.57% 3.99% 7.99% 12.96% 14.96% 31.70%

Period 0 t - 6 t o t

-22.61% -13.47% -8 .01% ^ . 0 7 % -1.12% 1.43% 4.64% 8.22% 13.93% 25.84%

48.45%

Period 1 t+1 to t+7

-1.19%

-0.82% -0.78% -0.38% -0 .01% 0.45% 0.74% 0.90% 1.73% 2.53% 3.72%

ios (1985 -2006) Period 2

t+7 to t+13 1.12%

0.08% -0.07% -0.43% -0.11% 0.01% 0.04% -0.07% -0.24% -0.29% -1.42%


1.67% 0.43% -0.09% -0.27% -0.07% 0.12% -0.25% 0.16% 0.22% 0.51% -1.16%

Figure 4 Change in Value (Rv) for Rv,t-6 Value Deciles


Period 0 is the Formation Period

• Low Rv,t-6 -6 • 8 9

Period 3

High Rv,t-6

Table 3.4: Change in Value for Past Value Deciles

Figure 5

Returns for Rv,t-6 Value Deciles

(A

E 3

•4-*

o a. •a a N O. -


Period 0 is the Formation Period

• Low Rv,t-6 • 8

Period 3

High Rv,t-6

Table 3.5: Returns for Past Value Deciles

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Low Mom (R,.,) 2 3 4 5 6 7 8 9

High Mom (R,^)

Low R,+, 31 22 16 14 12 13 13 14 17 25

2 19 18 18 17 17 17 18 18 18 19

Average Number of Firms fort 3 15 18 18 19 19 19 20 19 18 16

4

13 17 19 20 20 20 20 19 18 14

5 12 16 19 20 21 21 20 19 17 13

+1 Months 6 12 17 19 20 21 21 20 19 17 13

7

13 17 19 20 20 21 20 19 17 14

8

15 17 19 19 19 19 19 19 18 17

9

19 19 18 17 17 17 17 18 19 20

High R^,

31 19 15 13 13 13 13 15 19 28

Average Number of Firms for t + 3 Months

Low Mom (R,,*) 2 3 4 5 6 7 8 9

High Mom (R,4)

Low Rt+3

31 21 16 14 13 13 13 15 18 25

2 20 19 17 17 16 17 18 18 18 19

3 16 17 18 19 19 19 19 19 18 16

4 13 17 19 20 20 20 20 19 17 14

5 13 16 19 20 21 21 20 19 17 13

6 13 17 19 20 21 20 20 19 17 13

7

13 17 19 20 20 20 20 19 17 13

8 15 18 19 19 19 19 19 19 18 15

9 18 18 18 17 17 17 17 18 19 20

High R,tJ

27 18 15 14 13 13 14 16 20 30

Low Mom (R,^) 2 3 4 5 6 7 8 9

High Mom (Rw )

Low R M

32 22 16 14 12 12 13 14 17 26

2

22 20 18 16 16 16 17 17 18 19

Average Number of Firms for t 3

17 18 18 18 19 19 18 18 18 15

4 14 17 19 20 21 20 19 19 17 13

5 12 17 19 21 21 21 21 19 16 12

+ 6 Months 6 12 17 19 21 21 20 20 19 17 13

7

13 17 20 20 20 20 19 19 17 13

8 14 17 18 19 19 19 19 19 19 15

9 17 17 17 17 17 17 18 19 20 20

H ighR,« 24 17 15 14 13 14 14 17 21 31

Panel B: Summary of the Evolution of Fundamental Value High Rt+1 Low Rt+1

V+1 Rv t+1 M+1 t+1

Average Rt+1 Average Rvt+1 Average Rt+1 Average R' t+i

of .c if

5 o

1 month

3 months

6 months

1 month

3 months

6 months

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8.22%

1.36%

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10.34%

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-1.37%

-4.88%

-9.74%

3.10 References

177

1. Ball, Ray and Philip Brown, 1968, An Empirical Evaluation of Accounting Income Numbers, Journal of Accounting Research, 6, 159-178.

2. Boudoukh, Jacob, Matthew Richardson, YuQing (Jeff) Shen, Robert F. Whitelaw, 2007,Do Asset Prices Reflect Fundamentals? Freshly Squeezed Evidence from the OJMarket Journal of Financial Economics, 83, 397-412.

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5. Daniel, Kent, David Hirshleifer and Avanidhar Subrahmanyam, 1998, Investor Psychology and Security Market Under- and Overreactions, The Journal of Finance, 53, 1939-1885.

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7. Edwards, E. O. and P. W. Bell, 1961, The Theory and Measurement of Business Income, University of California Press.

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11. Fama, Eugene F. and Kenneth R. French, 1997, Industry Costs of Equity, The Journal of Financial Economics, 43, 153-193.

12. Fama, Eugene F. and Kenneth R. French, 2007, Disagreement, Tastes, and Asset Prices, The Journal of Financial Economics, 83, 667-689.

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14. Grossman, Sanford J., and Stiglitz, Joseph E., 1980, On the Impossibility of Informationally Efficient Markets, The American Economic Review, 70, 393-408.

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16. Jagadeesh, Narasimhan and Sheridan Titman, 1993, Returns to Buying Winners and Selling Loser: Implications for Stock Market Efficiency, The Journal of Finance, 48, 65-91.

17. Jagadeesh, Narasimhan and Sheridan Titman, 2002, Cross-Sectional Determinants of Momentum Returns, The Review of Financial Studies, 15, 143-156.

18. Kahneman, Daniel and Amos Tversky, 1979, Prospect Theory: An Analysis of Decision under Risk, Econometrica, 47, 263-292.

19. Keane, Michael P., and David E. Runkle, 1998, Are Financial Analysts' Forecasts of Corporate Profits Rational? Journal of Political Economy, 106, 768-805

20. Kothari, S.P., 2001, Capital Markets Research in Accounting, Journal of Accounting & Economics, 31, 105-231

21. Kyle, Albert S., 1985, Continuous auctions and insider trading, Econometrica 53, 1315— 1335.

22. Lim, Terence, 2001, Rationality and Analysts' Forecast Bias, Journal of Finance, 56, 369-385.

23. Lintner, John, 1969, The Aggregation of Investors' Diverse Judgments and the Selection of Risky Investments in Stock Portfolios and Capital Budgets, Journal of Financial and Quantitative Analysis, 4, 347-400.

24. Lucas, Robert E., Jr., 1972, Expectations and the Neutrality of Money, Journal of Economic Theory, A, 103-124.

179

25. Miller, Merton H. and Franco Modigliani, 1961, Dividend Policy and the Valuation of Shares, The Journal of Business, 34,411-433.

26. Newey, Whitney and Kenneth West, 1987, A Simple Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix, Econometrica, 55, 3, 703-708.

27. Roll, Richard, 1988, Orange Juice and Weather, American Economic Review, 74, 861-880.

28. Rubinstein, Mark, 1974, An Aggregation Theorem for Securities Markets, Journal of Financial Economics, 1, 225-244.

29. Shefrin, Hersh and Meir Statman, 1985, The Disposition to Sell Winners Too Early and Ride Losers Too Long," The Journal of Finance, 40, 777-790.

30. Shiller, Robert J., 2003, From Efficient Markets Theory to Behavioral Finance, Journal of Economic Perspectives, 17, 83-104.

31. Stickel, Scott E., 1990, Predicting Individual Analyst Earnings Forecasts, Journal of Accounting Research, 28, 409-417.

32. van Dijk, Ronald and Fred Huibers, 2002, European Price Momentum and Analyst Behavior, Financial Analysts Journal, 58, 96-105.

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