Two Essays in Finance: Has Momentum Lost its Momentum, and Venture

Capital Liquidity Pressure and Exit Choice

Debarati Bhattacharya

Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and

State University in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

in

Business, Finance

Raman Kumar, Co-Chair

Ozgur S. Ince, Co-Chair

Dilip K. Shome

Arthur J. Keown

March 05, 2014

Blacksburg, Virginia

Keywords: Momentum, Venture Capital, IPO

Copyright 2014, Debarati Bhattacharya

Two Essays in Finance: Has Momentum Lost its Momentum, and Venture Capital

Liquidity Pressure and Exit Choice

Debarati Bhattacharya

ABSTRACT

My dissertation consists of two papers, one in the area of investment and the second in the

area of corporate finance. The first paper examines robustness of momentum returns in the

US stock market over the period 1965 to 2012. We find that momentum profits have become

insignificant since the late 1990s partially driven by pronounced increase in the volatility of

momentum profits in the last 14 years. Investigations of momentum profits in high and low

volatility months address the concerns about unprecedented levels of market volatility in this

period rendering momentum strategy unprofitable. Past returns, can no longer explain the

cross-sectional variation in stock returns, even following up markets. We suggest three

possible explanations for the declining momentum profits that involve uncovering of the

anomaly by investors, decline in the risk premium on a macroeconomic factor, growth rate in

industrial production in particular and relative improvement in market efficiency.

We study the impact of venture capital funds’ (VC) liquidity concerns on the timing and

outcome of their portfolio firms’ exit events. We find that VC funds approaching the end of

their lifespan are more likely to exit during cold exit market conditions. Such late exits are

also less likely to be via initial public offerings (IPO). A one standard deviation

increase in the age of a VC fund at the time of the exit event is associated with a 5

percentage points decline in the probability of an IPO vs. a trade sale from an

unconditional probability of roughly 30%. Several tests indicate that the decline in IPOs with

VC fund age is not caused by lower portfolio firm quality. Focusing on the aftermath

of IPOs, VC-backed firms experience significantly larger trading volume and lower stock

returns around lock-up expirations if they are backed by older funds, and this lock-up effect

is amplified if there are multiple VC firms approaching the end of their lifespan.

Altogether, our results suggest that the exit process is strongly influenced by VCs’

liquidity considerations.

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Acknowledgements

I didn’t realize how difficult it would be to write this part of my dissertation. Now

don’t get me wrong, it is not for a lack of ability to articulate my emotions and I do have a

long list of people without whose support I couldn’t do this. It’s just that the last five years

with the finance department at Tech have been more than writing a dissertation, it has been

quite the journey. Leaving a career and family thousands of miles away, coming back to

school after a good seven years hiatus wasn’t easy. But I loved every second of the challenge

despite some days that were more glum than others. Dilip K. Shome admitted me to the

program and I am grateful to him for giving me this opportunity. My sincerest appreciation

goes out to the co-chairs of my dissertation committee, Raman Kumar and Ozgur ‘Ozzie’

Ince. I cannot thank them enough for everything that I have learnt from them. Raman’s

support has gone beyond the scope of an advisor on various projects to that of a

compassionate mentor taking care of me through some very tough personal times. Ozzie has

always had his doors open for me to walk in and brainstorm ideas.

Dilip provided a safe place for me to discuss almost anything, topics ranging from

how to get my committee together to decide on the future course of action towards

completion of my thesis to the latest movies, albums and books. Arthur J. Keown always had

an encouraging thing or two to say about my research and teaching and went out of his way

to help me during my job search process. In addition, I am grateful to Vijay Singal for his

support. I have often asked him for advice and he always took a sincere interest in helping

me. I also appreciate the advice that I got from John Easterwood when I first started teaching

at Tech.

I also want to thank Gokhan Sonaer who is truly a great friend. We have worked on

several co-authored papers and will continue to do so in the future. We have learned a lot

together and the contribution of Gokhan in my learning process is invaluable. Terry Goodson,

the soft and sincere woman I met five years ago has become one of my closest friends. I do

not have words to express my love and gratitude for her friendship. Wei-Hsein Lee, Hong

Yang, Jitendra Tayal, Mete Tepe, Nan Qin and Jaideep Chowdhury have also been great

friends and support over several years during the program.

My deepest appreciation goes out to my family. My parents, Amalendu and Supriya,

have given me so much and have expected nothing in return other than my happiness. They

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are my biggest cheerleaders and they have cheered me on no matter what till I reached my

goal and for that I will forever be grateful. Debopriyo, my young brother, a man of few words

have told the whole world but me how proud he is of his sister. My friend Atish who is

almost family has egged me on at times when I went through some of my existential phases

(who doesn’t get some of those while getting a PhD?) and wanted to throw in the towel.

Through the past years I have come to realize that how lucky I am to have this kind of family

support. It has made all the difference.

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Attribution

I am grateful to my co-authors, Raman Kumar and Gokhan Sonaer in the first paper of my dissertation

for their invaluable comments and advice.

I am also indebted to my co-author, Ozgur Ince in the second paper of my dissertation. He has been

involved in the paper right from the start. He has helped me develop the idea and has been a constant

support.

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Table of Contents

Paper I Has Momentum Lost its Momentum? ………………………………………………..1

1.1 Introduction……………………………………………………………………………1

1.2 Disappearance of momentum profits since 1999……………………………………...5

1.2.1 Holding period returns: Evidence from subperiods…………………………...6

1.2.2 Seasonality and holding period returns………………………………………..8

1.2.3 Extreme volatility and holding period returns since 1999…………………….9

1.2.4 Holding period return in a 14-year rolling window analysis: Evidence from

1965-1999……………………………………………………………………10

1.2.5 Market cycles and holding period returns……………………………………11

1.2.6 Holding period returns for small firms, large firms, low liquidity, and high

liquidity firms………………………………………………………………...13

1.2.7 Cross sectional variation in returns explained by past returns……………….14

1.2.8 Cross sectional variation in returns explained by past returns in the

intermediate horizon…………………………………………………………16

1.3 Possible explanations for the disappearance of momentum profits since 1999……..17

1.3.1 Uncovering of anomaly by investors…………………………………….......18

1.3.2 Reduced risk premium on macroeconomic variable……………………........20

1.3.3 Relative market efficiency Pre and Post 1999 Periods……………………....21

1.4 Conclusion……………………………………………………………………………23

References 1…………………………………………………………………………………………..24

Paper II Venture Capital Liquidity Pressure and Exit Choice………………………………..42

2.1 Introduction…………………………………………………………………………..42

2.2 “VC liquidity pressure” hypothesis…………………………………………………..48

2.3 Data and summary statistics………………………………………………………….50

2.3.1 Sample selection……………………………………………………………...50

2.3.2 Variable definitions and summary statistics………………………………….51

2.4 Timing of VC exits…………………………………………………………………...53

2.5 Exit choice……………………………………………………………………………58

2.5.1 Baseline results in exit choice………………………………………………..59

2.5.2 Identification…………………………………………………………………61

2.5.2.1 Instrumental variable approach………………………………………62

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2.5.2.2 Matched sample approach……………………………………………63

2.5.2.3 VC age and portfolio firm quality……………………………………65

2.6 Which funds succumb to liquidity pressure..………………………………………...67

2.7 Liquidity pressure at IPO lock-up expirations……………………………………….69

2.8 Conclusion…………………………………………………………………………...72

References 2………………………………………………………………………………………….74

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List of Tables

Table 1.1 Momentum portfolios’ raw returns for 6-month/6-month strategy…………..31

Table 1.2 Fama-French three-factor alphas of momentum portfolios for 6-month/6-

month strategy………………………………………………………………..32

Table 1.3 Momentum portfolios’ returns in times of extreme volatility for the period

1999-2012…………………………………………………………………….33

Table 1.4 Momentum portfolios’ returns following periods of low and high

volatility……………………………………………………………………....35

Table 1.5 Momentum profits over 14-year rolling window for the period

1965 to 1999………………………………………………………………….36

Table 1.6 Momentum portfolios’ raw returns following Up and Down markets……….38

Table 1.7 Momentum portfolios’ raw returns for 6-month/6-month strategy –size and

liquidity……………………………………………………………………....39

Table 1.8 Fama-MacBeth regressions of stock returns on past 11 months cumulative

returns, β, size, and BE/ME………………………………………………….40

Table 1.9 Measures of delay for the three sub-periods…………………………………41

Table 2.1 Summary statistics …………………………………………………………..79

Table 2.2 Number of months between investment and exit,

by fund Age at investment……………………………………………………81

Table 2.3 OLS analysis of time between VC investment and exit……………………...82

Table 2.4 Probit analysis of exit market conditions…………………………………….83

Table 2.5 Exit choice - Probit analysis………………………………………………….84

Table 2.6 Exit choice - 2SLS analysis…………………………………………………..85

Table 2.7 Exit choice - Propensity score matching……………………………………..86

Table 2.8 Liquidity Pressure and Fund Incentives……………………………………..87

Table 2.9 Liquidity pressure at IPO lockup expiration…………………………………88

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List of Figures

Figure 1.1 Average Winner-Loser Portfolio Returns by Year…………………………...26

Figure 1.2 Comparison of Distribution of Momentum Portfolios’ Returns following Up

Markets…………………………………………………………………….....27

Figure 1.3 Buy and Hold Abnormal Returns of New Entrants to Winner and

Loser Portfolios-Event Study………………………………………………...30

Figure 2.1 Histogram of exits by VC age categorized by exit method………………….76

Figure 2.2 Predicted probability of IPO based on observable quality proxies…………..77

Figure 2.3 Acquired firm characteristics by VC age at exit……………………………..78

Figure A.1 VC investment by Washington State Investment

Board between December 2002 and December 2012………………………..89

1

Paper I

Has Momentum Lost Its Momentum?

(Co-authored with Raman Kumar and Gokhan Sonaer)

1.1 Introduction

Momentum in stock prices has been shown to be a persistent market anomaly in the past.

Jegadeesh and Titman (1993) were the first to document that a trading strategy that longs winner

stocks and shorts loser stocks generates significant profits over a holding period of 3-12 months,

later labeled in the literature as momentum. Some advocates of market efficiency, however,

suspected these observed regularities in returns arise because of data snooping. In a follow up

study, Jegadeesh and Titman (2001) respond to such skepticisms by showing that momentum

strategy continues to generate abnormal returns in the 1990s. Momentum has grown in its

popularity ever since in the finance community that includes both the academics and

practitioners. Some of the recent works in the area of market anomalies, such as McLean and

Pontiff (2013) asks an interesting question of whether or not academic research could potentially

destroy return predictability.1 In this paper, we investigate whether momentum profits have been

driven away or at the very least its pattern altered in the wake of growing knowledge about

momentum strategy and competition amongst arbitrageurs who trade on it, if we were to believe

momentum profits were caused in the first place due to delayed price reactions to firm-specific

information as suggested by Jegadeesh and Titman (1993, 2001).

What if momentum is no longer profitable? The answer to this question makes this paper

important. It is needless to say that the disappearance of momentum profits, if proven to be true

1 Hwang and Rubesam (2008) build an inter-temporal model that explains momentum returns allowing for

structural breaks over an extended sample period 1927-2006. They document that momentum profits have slowly started declining in the last two decades of their sample period, a process that began in the early 90’s but delayed by the occurrence of high-technology stock bubble.

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would have an impact over a number of interest groups in the capital market, such as the traders

in forming strategies, the investors on how to evaluate their money managers’ performance, and

academics on how they perceive and explain the disappearance of this flagrant affront to the idea

of rational, efficient markets. This paper could potentially trigger an entirely different debate on

why has momentum disappeared in the context of the rich literature that exists on its persistence

and rationale, both behavioral and rational.

Our analyses span over the period between 1965 and 2012. We divide the entire time period

into three subperiods. The first subperiod corresponds to the Jegadeesh and Titman (1993)

sample period, 1965 to 1989, the second subperiod covers the Jegadeesh and Titman (2001) “out

of sample period”, 1990 to 1998, the third subperiod corresponds to the period 1999 to 2012. In

our study, we choose to examine the persistence of momentum profits while avoiding concerns

of data dredging by conducting tests in our out-of-sample period that starts at the beginning of

1999 immediately after Jegadeesh and Titman’s (2001) “out of sample period” ends. Using the

data over the 1999 to 2012 sample period, we find that Jegadeesh and Titman (1993) momentum

strategies fail to yield profits in the more recent times. This period is particularly interesting as it

witnessed the dot-com bust after catching the boom by its tail and also the financial crisis

followed by the greatest stock market meltdown since the great depression. One of our concerns

in dealing with this unique period is what if the recent turbulence in the economy with a series of

high-loss episodes in the US stock market and unprecedented levels of market volatility has

rendered momentum strategy unprofitable?

We employ alternate methodologies to scrutinize whether the rapid decline of momentum

profits to insignificant levels in this 14 year period is indeed an outcome of the marked rise in

market volatility. For instance, we use controls for the periods of unusual volatilities in the

3

capital market, 2007 to 2009 in particular and yet fail to reject the hypothesis that momentum

profits have not declined to insignificant levels. Excluding the last financial crisis, 2007 to 2009

serves the additional purpose of excluding spring of 2009 that witnessed the biggest momentum

crash in the history of stock market since the summer of 1932 as alluded to by Daniel and

Moskowitz (2012). Next, we employ the daily median volatility index, VXO for the period 1986

to 1998 to classify months in the latest subperiod into high and low expected volatility months.2

If momentum profits have declined because of increased volatility of the market, momentum

strategy should be profitable at least in months when the implied volatility is as low as in low

volatility months in the period 1986 to1998, a period when momentum is profitable. However,

what we document is that while momentum strategy is profitable in the period 1986 to 1998 no

matter the implied volatility, it fails to generate profit for the period 1999 to 2012 even in the 60

months classified as low volatility months primarily clustered between November 2003 and July

2007.

We also investigate whether momentum profits resurface in this period following up markets

as documented by Cooper, Gutierezz and Hameed (2004). Not only are these momentum profits

insignificant on average following up markets, their distribution also reveal visible and statistical

difference from those in the periods 1965 to 1989 and 1990 to 1998, indicating a deeper and

more fundamental change in the underlying process of generation of momentum profits, beyond

huge market crashes. The distribution of up market momentum profits in this period is extremely

volatile interspersed with huge negative returns that suggest that momentum as a strategy has

become riskier in the latest subperiod compared to the two earlier subperiods. Further analysis

indicates that the idiosyncratic volatility of momentum portfolio returns has increased compared

2 We use VXO instead of VIX since the former that is computed using a different methodology and eventually

revised by CBOE provides us with an additional 4 years’ worth of data.

4

to the previous periods. We also examine whether cumulative past returns can explain the cross-

sectional variation in stock returns. In the presence of return continuation, we expect past stock

returns to be positively related to current stock returns, especially following up markets since

momentum profits are essentially up market phenomena. As expected in the periods 1965 to

1989 and 1990 to 1998, current stocks returns are positively related to past returns exclusively

following up markets. However, in the current subperiod, with decline in momentum profits past

returns fail to explain current returns following up markets and show a reliably negative relation

following down market.

We suggest three possible explanations for the declining momentum profits that involve

uncovering of the anomaly by investors, decline in the risk premium on a macroeconomic factor,

growth rate in industrial production in particular, and relative improvement in market efficiency.

The first explanation proposes that momentum profits decline post 1998 because investors

become increasingly aware about the profitability of implementing a relatively simple

momentum trading strategy, wherein they identify winner (loser) stocks and buy (sell) them. The

growing awareness and competition amongst these investors would lead to an increasingly

earlier identification and trading of momentum stocks. This explanation predicts intensified

reaction to both winner and loser stocks in the identification period itself, which would result in

either exhaustion or, at the least, a substantial reduction in return continuation in the holding

period.3 We find evidence consistent with this prediction.

The second explanation is based on the findings of Liu and Zhang (2008) who document that

growth rate of industrial production, in various specifications, explains over half of the

3 Reducing underreaction or mispricing may also result in similar patterns of returns from loser and winner

portfolios, if we were to believe momentum profits were caused in the first place due to delayed price reactions to firm-specific information as suggested by Jegadeesh and Titman (1993, 2001). The distinction between uncovering of anomaly by investors and reducing undereaction is beyond the scope of this paper.

5

momentum profits. We find that in the latest subperiod although the momentum portfolio’s

returns continue to load on this industrial production factor, this particular risk factor is no longer

priced. The third explanation explores the possibility of relative improvement in market

efficiency. Following Griffin, Kelley, and Nardari (2010), we compute their DELAY measure,

that reflects the degree of response of stock returns to past market returns, and we record a fairly

significant reduction in delay in all size portfolios but for the largest one.

1.2 Disappearance of momentum profits since 1999

Our sample is constructed from all common stocks traded on New York Stock Exchange

(NYSE), American Stock Exchange (AMEX), and Nasdaq. We obtain the data related to the

stock market from the Center for Research in Security Prices (CRSP) database, and accounting

data from Standard and Poor’s (S&P’s) Compustat. We exclude all stocks priced below $5 at the

beginning of the holding period and all stocks with market capitalizations smaller than that of the

lowest NYSE size decile following Jegadeesh and Titman (2001).

Our analyses span over the period between 1965 and 2012. We divide the entire time period

into three subperiods. The first subperiod corresponds to the Jegadeesh and Titman (1993)

sample period, 1965 to 1989, the second subperiod covers the Jegadeesh and Titman (2001) “out

of sample period”, 1990 to 1998, and the third subperiod corresponds to the period 1999 to

2012.We choose our third sample subperiod adhering to standard model validation practice and

testing the hypothesis of persistence of momentum profits in our out-of-sample period that starts

at the beginning of 1999 immediately after Jegadeesh and Titman’s (2001) “out of sample

period” ends.

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1.2.1 Holding period returns: Evidence from subperiods

In this section we examine whether momentum strategies continue to be profitable since the

late 1990s. Jegadeesh and Titman (2001) document that their out of-sample tests designed to

assess persistence of momentum profits in the 1990s performed at least as well as the ones

conducted with the original sample in their earlier study in 1993. It has been a while since money

managers and traders at large have acceded to the claims that momentum strategies generate

substantial profits, and we have concurrently seen a phenomenal growth in the size of funds in

their hands. Hedge funds managed about $1.64 trillion in 2011 up from $ 200 billion in 1998 and

equity mutual funds managed about $13 trillion at year-end 2012 up from $5.5 trillion in 1998.4

These developments raise a fairly obvious question. Has momentum survived this new era of the

capital markets?

Our tests reveal strong evidence of momentum profits in the first, less strong evidence in the

second consistent with the literature, and decline in momentum profits to insignificant levels in

the third subperiod.

Following Jegadeesh and Titman (1993), we examine the profitability of 16 strategies that

select stocks based on the their returns over the past 3, 6, 9, and, 12 (J) months and hold them for

either 3, 6, 9, or 12 (K) months in each of our three subperiods. At the end of each month (t), we

sort stocks into 10 equally weighted portfolios based on their cumulative returns earned in the

past J months (t – J + 1 to t). We hold these portfolios for K months (t + 1 to t + K). As a result

we have K overlapping portfolios each of which is assigned an equal weight in the portfolio. We

also construct a momentum strategy portfolio that buys the winner portfolio (top past return

4 Sourced from McKinsay’s Global Institute forecasts, HedgeFundFacts.com and ICIFACTBOOK.ORG.

7

decile) and sells the loser portfolio (bottom past return decile). Similar to Jegadeesh and Titman

(2001) we compute the portfolio returns using data from the CRSP monthly returns file.

Next, we compute the Fama-French three-factor alphas (Fama and French, 1993) earned by

the winner, loser and momentum (winner-loser) portfolios for all the 16 (J-month/K-month)

strategies.

Our investigation reveals that over the periods 1965 to 1989 and 1990 to 1998, the returns for

all the momentum strategies are positive and statistically significant confirming the other known

results as in Jegadeesh and Titman (1993) and Jegadeesh and Titman (2001). However, for the

1999 to 2012 period none of the 16 momentum strategies delivers any returns different from

zero. The risk adjusted profit analysis also confirms that for all the 16 (J-month/K-month)

strategies with a few exceptions the alphas of the loser portfolios are negative whereas the alphas

of the winner portfolios are positive for the periods 1965 to 1989 and 1990 to 1998. Momentum

portfolios for all strategies earn statistically significant alphas for these two subperiods. In the

period 1999 to 2012, none of the past return deciles earn alphas significantly different from zero

and the alpha of momentum portfolio also disappears.5

Following Jegadeesh and Titman (1993) we now examine the six month formation/ six

month holding strategy in more detail. Table 1.1 presents the average monthly raw returns for the

10 past return portfolios. At the end of each month (t), we sort stocks into 10 equally weighted

portfolios based on their cumulative returns earned in the past six months (t - 5 to t). We hold

these portfolios for the next six months (t + 1 to t + 6). This process presents us with six

overlapping portfolios each of which is assigned an equal weight in the portfolio. We also

5 These results are not reported for the sake of brevity, but they are available upon request.

8

construct a portfolio following momentum strategy that buys winner (top past return decile) and

sells loser (bottom past return decile).

Table 1.1 shows that the average returns increase as we go from the lowest to the highest

deciles for all the three subperiods. The momentum portfolio (P10-P1) on average earns a 1.10 %

per month in the period 1965 to 1989 that continues in the period 1990 to 1998. Consistent with

the findings of Jegadeesh and Titman (2001), the momentum portfolio in the second subperiod

earns 1.37% a month. However, as noted earlier in Table 1, the momentum returns decline to

insignificant levels in the period 1999 to 2012.

Table 1.2 presents the alphas for the 10 past return portfolios. Past losers P1 earn negative

alpha and past winners P10 earn positive alpha in the periods 1965 to 1989 and 1990 to 1998.

The momentum portfolio (P10-P1) on average earns an alpha of 1.27% per month in the period

1965 to 1989 and 1.35% per month in the period 1990 to 1998. However, neither the past loser,

or past winner or the momentum portfolios earn any alphas in the period 1999 to 2012 that are

significantly different from zero. 6

1.2.2 Seasonality and holding period returns

We examine whether the January effect on momentum profits reported by Jegadeesh and

Titman (1993, 2001) have become pronounced in the period 1999 to 2012 so much so that the

momentum profits in the non-January months are overshadowed. The momentum profits in

January for our sample are no different from zero over the period 1965-2012. The momentum

profits for the non-January months are, however, positive and significant for the periods 1965 to

6 George and Hwang (2004) find that proximity to the 52-week high predicts the future returns significantly better

than past returns. However we find that the 52-week high strategy does not work, exactly as the momentum strategy in the last subperiod.

9

1989 and 1990 to 1988 but significantly in the period 1999 to 2012. The evidence indicates that

there has not been any significant change in the absence of momentum profits.7

1.2.3 Extreme volatility and holding period returns since 1999

The post 1998 period, during which we document significant decline in momentum profits,

experienced stretches of extreme stock market volatility as it witnessed the dot -com bust after

catching the boom by its tail and also the financial crisis followed by the greatest stock market

meltdown since the great depressions. We acknowledge the importance of controlling for these

periods of unusual volatilities. Table 1.3 presents the monthly average returns for 10 portfolios

formed on the basis of the past 6 months’ cumulative returns and held for another 6 months,

earned in six separate time periods post 1998. The first two columns report the returns for the

periods 1999 to 2005, and 2006 to 2012, dividing the post 1998 period into two halves. The first

two columns of the table reveal that the momentum portfolios (P10-P1) earn no profit in the first

as well as the second half of our last subperiod. The third column reports the returns for the

period 1999 to 2012 excluding the last financial crisis, 2007 to 2009, a period that also includes

spring of 2009, the biggest momentum crash in the history of stock market since the summer of

1932 as alluded to by Daniel and Moskowitz (2012). The fourth column reports the returns for

the period 2004 to 2012, excluding the tech boom and bust, 1999 to 2003 as well as the last

financial crisis. These columns do not reveal any resurfacing of momentum profits, and it is

especially interesting to find no momentum in the period 2004 to 2012 (excluding 2007 to 2009)

since the market showed an upward trend in these years, a condition favorable for generating

momentum profit.

7 These results are not reported for the sake of brevity, but are available upon request.

10

We employ an alternate methodology to scrutinize whether the rapid decline of momentum

profits to insignificant levels in this 14 year period is indeed an outcome of the marked rise in

market volatility. We obtain daily levels of volatility index, VXO available for the period 1986

to 2012 from the website of Chicago Board of Options Exchange, CBOE. The daily median

implied volatility for the period 1999 to 2012 jumps to 21.72 from 18.35 in the period 1986 to

1998 consistent with the common knowledge that market volatility in the latest subperiod

reached higher levels compared to the previous two subperiods. We classify months in the latest

subperiod into high (low) volatility months if the monthly mean volatility, VXO is above

(below) the daily median VXO for the period 1986 to 1998. 60 months get classified as low

volatility months primarily clustered between November 2003 and July 2007 and 108 months get

classified as high volatility months. If momentum profits have declined because of increased

volatility, momentum strategy should be profitable at least in months when the implied volatility

is as low as in low volatility months in the period 1986 to1998, a period when momentum is

profitable. However, what we document in Table 1.4 is that while momentum strategy is

profitable in the period 1986 to 1998 no matter the implied volatility, it fails to generate profit for

the period 1999 to 2012 even in all of the 60 months classified as low volatility months. This

evidence suggests it is not the unprecedented levels of market volatility that has rendered

momentum strategy unprofitable in the last 14 years.

1.2.4 Holding period return in a 14-year rolling window analysis: Evidence from 1965-1999

Presented with all the initial evidence of disappearing momentum profits, a well-founded

question in the reader’s mind maybe: Has there been any other 14 year stretch in the past over

which the momentum strategy has not been profitable?

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We perform a 14-year rolling window analysis in which we compute the average raw and

risk-adjusted momentum returns for every 14 years starting at the beginning of each year from

1965-1999. In Table 1.5 we document that starting from 1965 for no other 14 year period until

1996, momentum strategy was ever unprofitable. The momentum profits are not significantly

different from zero only over the 14 year periods starting in 1996, 1997, 1998, and 1999.

Figure 1.1 plots the monthly average returns for each year to the momentum portfolio from

1965 to 2012. Post the tech bubble, other than 2002, 2005, and 2007 the momentum return is

either negative or close to zero.8 For those who would still like to ascribe the disappearance of

momentum profits to housing crisis of 2008-2009 we would like to point out that the period

1999-2012 was as good and as bad for momentum strategy, as is evident from the figure, if one

were to concentrate only on the highest and lowest return years, 2000 and 2009 respectively.

Moreover, as shown in Table 1.4 earlier excluding these years make no difference to our

inference that there is no more any momentum effect in stock prices.

1.2.5 Market cycles and holding period returns

Cooper, Gutierezz, and Hameed (2004) document that momentum profits are significant

following up market conditions. In this section we examine whether momentum profits reappear

once controlled for the up and down market cycles. Following Cooper, Gutierrez, and Hameed

(2004), we classify the months following a phase of 36 months of positive (negative) value

weighted CRSP index returns as up (down) markets. Table 1.6 presents the monthly average

returns for 10 portfolios formed on the basis of the past 6 months’ cumulative returns and held

for another 6 months earned following up and down market conditions. The results indicate that

8 We are aware that momentum returns peaked during 1999 and 2000 riding on the internet bubble. In spite of

that we include these years in our last subsample since Jegadeesh and Titman (2001)’s out-of-sample period ends in 1998, after which our out-of-sample period begins.

12

momentum portfolios (P10-P1) earn significant profits following up markets but they earn no

profits reliably different from zero following down markets in the periods 1965 to 1989 and 1990

to 1998 confirming earlier findings. The period 1990 to 1998 experienced no down market

conditions and this can partially explain, the larger momentum profit in this period recorded

above compared to the period 1965 to 1989. However, in the period 1999 to 2012, momentum

portfolios do not earn any profit significanlty different from zero, regardless of market

conditions. Not only are these momentum profits insignificant on average following up markets,

their distribution also turns out of to be visibly and statistically very different from those in the

first and the second subperiods indicating a deeper and more fundamental change in the

underlying process of generation of momentum profits, beyond huge market crashes.

Figure 1.2 plots and compares the distribution of monthly returns of momentum portfolios

(winners-losers), following up-markets. The solid line represents a fitted normal distribution and

the dashed line represents fitted kernel density, estimated with bandwidth parameter of 0.79.

Panel A plots the distributions of monthly returns of these momentum portfolios in the periods

1965 to 1989 and 1999 to 2012 and Panel B plots the same for the periods 1990 to 1998 and

1999 to 2012. Momentum profits in the last subperiod show larger dispersion as compared to the

two previous subperiods that may explain the lack of statistical significance of the average

momentum returns following up markets in this subperiod. Momentum as a strategy seems to

have become riskier in the most recent subperiod. Kuiper two sample tests that are used to assess

the uniformity of a set of distributions show that these distributions are significantly different

from each other. Panel C plots the distributions of monthly returns of momentum portfolios

following up markets in the periods 1965-1989 and 1990-1998. The distributions look similar

indicating comparable riskiness of the momentum strategy in the first two subperiods. The

13

Kuiper tests confirm that these two distributions are not significantly different from one another.

The idiosyncratic volatility of the momentum portfolio has increased in the latest subperiod

compared to the previous two subperiods combined which may be contributing towards the

overall rise in volatility of momentum returns. We calculate the variance of the residuals from

Fama-French 3-Factor model regression of momentum returns for each sub-period and conduct

an F-test to compare the statistical significance of the difference.

1.2.6 Holding period returns for small firms, large firms, low liquidity, and high liquidity firms

It is quite possible that momentum strategy continues to be profitable among smaller and

lower liquidity stocks for the simple reason that they are more expensive to trade. To address this

possibility, in this subsection we separately examine the momentum returns generated by small

and large stocks, and also by high and low liquidity stocks. Following Jegadeesh and Titman

(2001), the Small Cap group (Large Cap) comprises of stocks that are smaller (larger) than the

median NYSE stock by market capitalization at the beginning of the holding period.9 Illiquidity

is estimated as ratio of absolute one day return to dollar volume in that particular day, a measure

proposed by Amihud (2002). Low (High) Liquidity stocks have higher (lower) average

illiquidity than the median illiquidity stock in the month preceding the identification period (t -

6). We use the liquidity measure as of the sixth month before the holding period to make

liquidity sorting process independent from the past return sorting process.

The results in Table 1.7 indicate that the momentum effect that was prevalent in all size and

liquidity categories till 1998, decline uniformly across all these groups of stocks in the period

1999 to 2012.

9 We repeat our analysis with size subsamples formed on the basis of the market capitalization at the beginning of

the identification period to make the size sorting process more independent from the past return sorting process and this has no effect on inferences.

14

1.2.7 Cross sectional variation in returns explained by past returns

In the subsections above, we have provided evidence that momentum strategies no longer

earn significant returns since 1999, and that these results are robust to various controls for

seasonality, extreme volatilities and cycles in the capital market. However, financial market

anomalies are patterns in security returns not only in time series but also in the cross-section that

are not predicted by the central theory of asset pricing. We suspect that with declining return

continuation to relative strength portfolios, the past returns can no longer explain cross-sectional

variation in stock returns.

To investigate whether past returns explain stock returns in the cross section, we adopt the

methodology employed by Fama and French (1992). We carry out Fama-MacBeth regressions of

monthly returns of individual stocks on its past cumulative returns (t - 12 to t - 2) controlling for

post ranking beta, size, and book-to-market equity. The only accounting ratio used in the

regressions is the natural logarithm of book-to-market equity, ln(BE/ME). BE is the book value

of common equity plus balance-sheet deferred taxes, and ME is the market equity. BE is obtained

for each firm's latest fiscal year ending in calendar year t – 1 and BE/ME is computed using

market equity (ME) in December of year t - 1. However, firm size, the natural logarithm of

market equity ln(ME) is measured in June of year t. The explanatory variables for individual

stocks are matched with CRSP returns for the months from July of year t to June of year t + 1.

The gap between the accounting data and the returns ensures that the accounting data are

available prior to the return. Following Fama and French (1996), the cumulative past returns for

each stock, each month are computed by cumulating their returns from t - 12 to t - 2 months.

Individual stocks are assigned post-ranking β of the size-β portfolio that they are in at the end of

June of year t. We compute the post-ranking βs as in Fama and French (1992). Each June all

15

NYSE stocks are sorted based on market equity to determine NYSE size decile cut -off points.

Then, all NYSE, AMEX and NASDAQ stocks that have data both on CRSP and COMPUSTAT

are assigned to these size deciles based on NYSE cut -off points. We sort stocks in each size

decile, based on their pre-ranking βs. The pre-ranking βs are estimated using t - 24 to t - 60

monthly stock returns. The equal weighted average monthly returns of the 100 size-β portfolios

are computed over 12 months following June of each year and the post-ranking βs for these 100

size-β portfolios are estimated for the full period. We use Fowler and Rorke (1983) correction in

estimating the βs.

Table 1.8 presents the results of these Fama-MacBeth regressions. These results clearly

demonstrate that a positive relation between current and past stocks returns exists for the periods

1965 to 1989 and 1990 to 1998, but is no longer significant in the period 1990 to 2012.10

This

confirms our postulate that as momentum returns decline to insignificant levels, past returns can

no more explain cross-sectional variation in stock returns. The regressions also show that market

β does not help explain average stock returns for the entire sample period confirming the results

of Fama and French (1992). The small firm effect prevails through the first two subperiods,

though relatively weaker in the post 1989 period. However, it is subsumed by the book-to-

market. The value stocks on the other hand continue to outperform growth stocks over the entire

sample period. The results are consistent with the existing literature on widely known stock

market anomalies.

Momentum profits have been linked to market states in the literature. We earlier presented

evidence that momentum profits are insignificant on average following 3-year up markets in the

10

We also include natural logarithm of asset-to-market and asset-to-book ratios as explanatory variables instead of natural logarithm of book-to-market in the regressions and this does not have bearing on our inferences.

16

1999 to 2012 period, in contrast to the two previous subperiods. We also examine whether past

returns explain stock returns in the cross-section after controlling for market states. We carry out

Fama-MacBeth regressions of monthly returns of individual stocks as in Table 8, splitting the

subperiods into up and down market states this time. The results confirm all our previous

findings. In the periods 1965 to 1989 and 1990 to 1998, past stocks return is positively related to

current stocks returns exclusively following up markets. However, in the current subperiod, past

returns fail to explain current returns following up markets and show a reliably negative relation

following down market. So with decline in momentum profits, past returns do not show the

expected positive relation with current stock returns.11

1.2.8 Cross sectional variation in returns explained by past returns in the intermediate horizon

Novy-Marx (2012) concludes that the recent past performance does not matter as much as the

past performance within the intermediate horizon, in particular the cumulative returns 12 to 7

months prior to formation (t - 12, t - 7). We carry out Fama-MacBeth regressions of monthly

returns of individual stocks as in Table 8, only this time using the cumulative returns of stock

over the intermediate horizon. In the periods 1965 to 1998, intermediate past stocks return is

positively related to current stocks returns. However, in the 1999 to 2012 period, past

intermediate returns fail to explain current returns. Hence, with decline in momentum profits,

past returns, no matter whether measured over the recent past or the intermediate horizon do not

show the expected positive relation with current stock returns.12

11

These Results not presented for the sake of brevity, but they are available upon request. 12

These results are not tabulated for the sake of brevity, but they are available upon request.

17

1.3 Possible explanations for the disappearance of momentum profits since 1999

We suggest three possible explanations for the declining momentum profits that involve

uncovering of anomaly by investors, disappearance of the risk premium on industrial production

factor, and improvement in relative market efficiency. The first explanation proposes that

momentum profits decline post 1998 because investors learn about the benefits of implementing

a naive strategy called momentum thereby correcting mispricing if any in the firms identified as

winners and losers within the identification or the formation period faster in the last subperiod

compared to the earlier subperiods. This explanation predicts intensified reaction to both winner

and loser stocks in the identification period itself, which would result in either exhaustion or, at

the least, a substantial reduction in return continuation in the holding period, and weakened

return reversal (under the scenario of possible overreaction in the holding period perpetrated by

behavioral biases) in the post holding period. We find evidence consistent with all these

predictions. However, a caveat is order here; reducing underreaction or mispricing may also

result in similar patterns of returns from loser and winner stocks, if we were to believe

momentum profits were caused in the first place due to delayed price reactions to firm-specific

information as suggested by Jegadeesh and Titman (1993, 2001). The distinction between the

two is beyond the scope of this paper.

The second explanation is based on the findings of Liu and Zhang (2008) who show that

macroeconomic factors such as growth rate of industrial production are priced and in various

specifications explains over a half of the momentum profits. We however, find that in the latest

subperiod the marginal productivity factor is no longer priced.

The third explanation explores the possibility of improvement in relative market efficiency.

Following Griffin, Kelley, and Nardari (2010), we use the delay in order to assess the

18

improvement in market efficiency that measures the degree of response of stock returns to past

market returns. We record a fairly significant reduction in delay in all size portfolios but for the

largest one that suggests improvement in relative market efficiency.

1.3.1 Uncovering of anomaly by investors

The first explanation proposes that investors simply recognize that momentum strategy is

profitable and trade in ways that arbitrage away such profits partially consistent with Schwert

(2003) that documents two primary reasons for the disappearance of an anomaly in the behavior

of asset prices, first, sample selection bias, and second, uncovering of anomaly by investors who

trade in the assets to arbitrage it away. Competition amongst arbitrageurs to buy the winners and

short the losers would induce them to try to identify the winners and losers earlier and earlier.

Earlier identification and execution of the momentum strategy in the latter part of the

identification period itself would reduce, and eventually eliminate the abnormal returns in the

holding period. Moreover, the incentive and the competition amongst the arbitrageurs to unwind

the long and short trades before any losses due to any possible over-reaction in the holding

period would eventually eliminate any systematic over-reaction and subsequent reversals. It is

also interesting to note that Brav and Heaton (2002) point out even if irrationality perpetrates

financial anomalies, their disappearance hinges on rational learning, an ability of rational

arbitrageurs to identify observed price patterns and wipe out any return potential in excess of risk

based expectations.

This explanation predicts intensified reaction to winner and loser stocks in the identification

period itself, exhaustion or, at the least, a substantial reduction in return continuation in the

holding period, and weakened return reversal (under possible overreaction in the holding period

perpetrated by behavioral biases) in the post holding period.

19

To test these implications of growing investor awareness, we compute the buy and hold

abnormal returns of new winner and loser stocks during the identification period and in the

following 24 months. New winners (losers) are the stocks that enter the winner (loser) portfolio

in month t. Abnormal return for each event month is the average of the mean abnormal returns of

all stocks with monthly return data for 30 months, t - 5 to t + 24, across all calendar months. Buy

and hold abnormal return is the difference between the cumulative raw return and cumulative

expected return for each stock for each event month. The expected returns are computed using

the loadings on Fama-French three factors over the five year period between t - 71 to t - 13.

Stocks with less than 24 monthly observations are excluded for the purpose of estimation of the

three factor loadings. Figure 1.3 presents the plots of the buy and hold abnormal returns.

The buy and hold returns for the winner stocks in the identification period, months t -5 to t

show that in the post 1998 period they reach substantially higher levels on average spiraling at a

much faster rate compared to the pre 1999 period and they eventually flatten out in the holding

and post holding periods, months t + 1 to t + 24. Even though the graph for the buy and hold

return of winner stocks in the post 1998 period may suggest return continuation for a few months

in the post holding periods, months t+3 to t+10 in particular, none of these returns are

statistically significant. Very similar pattern is exhibited by the returns of loser stocks. However,

front running the traditional momentum traders on the short end seems more difficult to

implement. This is not a surprising finding in light of the existing literature that associates higher

asymmetry of information, transaction costs and other short trade restrictions.13

13

We also analyze the risk-adjusted 24 month post holding period returns of the winner and loser portfolios that show substantial reversal consistent with overreaction and subsequent price correction hypothesis until 1998. Post 1998, there is no evidence for either return continuation or subsequent reversal.

20

1.3.2 Reduced Risk Premium on Macroeconomic Variable

As mentioned earlier Liu and Zhang (2008) show that macroeconomic factors such as

growth rate of industrial production are priced and in various specifications explains over a half

of the momentum profits. If however, in the last subperiod the marginal productivity factor is no

more a priced risk factor then that could provide an explanation to the disappearance of

momentum profits.

To that end, we first compute the loadings of loser, winner, and winner-loser portfolios’

returns on the growth rate of industrial production. We use monthly regressions of these portfolio

returns for estimating the loadings on the Fama-French three factors and the growth rate of

industrial production (MP). =log as defined in Liu and Zhang (2008), where

is the index of industry production in month t from the Federal Reserve Bank of St. Louis.

Momentum portfolio continue to load significantly positive on this factor in the 1999-2012

period as in the 1965-1998 period.

Next, we the estimate of the risk premium of MP from two-stage Fama-MacBeth (1973)

cross-sectional regressions. Following Liu and Zhang (2008) in the first stage, we estimate factor

loadings using sixty-month rolling-window regressions and extending-window regressions. For

the rolling window, the starting month for the estimation is t - 60 and the ending month is t. For

the extending window the starting month for the estimation is always January 1965 and the

ending month is t. In the first stage, we run regressions of monthly excess returns of 30 testing

portfolios on Fama-French three factors and the MP. 30 testing portfolios consist of ten size, ten

book-to-market, and ten six/six momentum portfolios.14

In the second stage, we perform cross-

sectional regressions of 30 testing portfolios t + 1 month excess returns on the factor loadings

estimated in the first stage using information up to month t. We start the second-stage regressions 14

The ten size and ten book-to-market portfolio data are from Kenneth French’s web site.

21

in January 1965. The risk premium of MP is computed by taking the average of the coefficients

on the MP loadings from the second-stage cross-sectional regressions. The MP risk premium is

positive and significant in the first two subperiods combined. However, neither for the rolling

window nor for the extending window analysis is the MP risk premium significantly different

from zero indicating that the industrial growth rate factor is no longer priced, a plausible cause

for disappearing momentum profits.15

1.3.3 Relative Market Efficiency Pre and Post 1999 Periods

Post 1998, neither of the winner or the loser portfolios earn returns that are reliably

different from zero in the post identification period. The lack of return continuation and

subsequent reversal in the post identification period can be interpreted as an evidence of

improvement of market efficiency in the period 1999 to 2012. The markets might have become

more efficient because information gets impounded into prices faster in this period. Following

Griffin, Kelley, and Nardari (2010), we examine improvement in relative market efficiency using

the DELAY measure that reflects the degree of response of stock returns to past market returns.

DELAY is computed by subtracting the adjusted R2 of unrestricted market model from the

adjusted R2 of the restricted market model (Delay =

). The

unrestricted model uses four lags of weekly market returns:

,where is the weekly

portfolio (individual stock) return at time t and is the market return. In the restricted model,

the coefficients on the lagged market returns are constrained to zero:

15

These results are not reported for the sake of brevity, but they are available upon request.

22

Table 1.9 presents the results for DELAY for the 5 size quintiles for our sample of stocks.

In Panel A, weekly returns of five size portfolio are the dependent variables in the market model.

Weekly returns are the equal weighted portfolio returns for the size quintiles. All stocks in our

sample are sorted into quintiles at the end of previous year. DELAY across all size quintiles

declines substantially except for the largest portfolio. The smallest size quintile experiences an

88% reduction in delay between the second and the last subperiod. The numbers for the other

quintiles are fairly large though they decrease monotonically from the smallest to the largest

quintile. The results are not surprising since the larger stocks suffer a lot less from problems of

information asymmetry, constitute a big part of the market itself, hence their prices respond to

market wide news a lot faster. In Panel B, weekly returns of individual stock are the dependent

variables in the market model. For each size quintile, we then compute the average DELAY. We

also report the difference between the average DELAY of each subperiods and the corresponding

p-values. We record a fairly significant reduction in DELAY in all size quintiles but between the

second and the third subperiod in particular other than the third largest and largest portfolios.16

16

As indicated by Griffin, Kelley and Narrdari (2010), delay measures may be subject to larger estimation error noise for individual firms but in order test the statistical significance of delay measures across the three subperiods we have to use delay measure at the stock level.

23

1.4 Conclusion

In this paper we ask the question “what if momentum which has been shown to be a

persistent market anomaly is no longer profitable?” The contribution of this paper lies in the

answer to this question. It cannot be stressed enough that the disappearance of momentum

profits, if proven to be true would have a significant impact over a number of interest groups in

the capital market, such as the traders in forming strategies, the investors on how to evaluate

their money managers’ performance, and academics on how they perceive and explain the

disappearance of such a persistent market anomaly. This paper evaluates the persistence of

momentum or lack thereof over the last half a century.

We document that trading strategies, which buy past winners and sell past losers, though

remarkably profitable up until 1998, fail to generate significant abnormal returns in the period

1999 to 2012. These results are robust across extreme size and liquidity subsamples of stocks,

periods of unusual volatilities in the capital market, seasonality, and up and down market

conditions. We also document that past returns either in the long run or within the intermediate

horizon can no longer explain cross-sectional variation in stock returns in the post 1998 period.

We suggest three possible explanations for the declining momentum profits that involve

uncovering of the anomaly by investors, decline in the risk premium on a macroeconomic factor,

growth rate in industrial production in particular, and relative improvement in market efficiency.

In support of these explanations, we conduct an event study, the results of which hinge on

investor learning. We document decline in risk premium of industrial growth to insignificant

levels, and we also conduct traditional relative market efficiency tests, the results from which

suggest that market information gets incorporated faster into stock prices.

24

References 1

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financial markets 5 (2002), 31-56.

Brav, A., and J. B. Heaton. "Competing Theories of Financial Anomalies." Review of Financial

Studies 15 (2002), 575-606.

Cooper, M. J., R. C. Gutierrez, and A. Hameed. "Market States and Momentum." The Journal of

Finance 59 (2004), 1345-1365.

Daniel, K., and T. J. Moskowitz. 2012. “Momentum crashes.” Working paper, SSRN eLibrary.

Fama, E. F., and K. R. French. "The Cross-Section of Expected Stock Returns." The Journal of

Finance 47 (1992), 427-465.

Fama, E. F., and K. R. French. "Common risk factors in the returns on stocks and bonds."

Journal of Financial Economics 33 (1993), 3-56.

Fama, E. F., and K. R. French. "Multifactor Explanations of Asset Pricing Anomalies." The

Journal of Finance 51 (1996), 55-84.

Fowler, D. J., and C. H. Rorke. "Risk measurement when shares are subject to infrequent trading

: Comment." Journal of Financial Economics 12 (1983), 279-283.

George, T. J., and C. Y. HWANG. "The 52‐Week High and Momentum Investing." The Journal

of Finance 59 (2004), 2145-2176.

Hwang, S., and A. Rubesam. "The Disappearance of Momentum." SSRN eLibrary (2008).

Jegadeesh, N., and S. Titman. "Returns to Buying Winners and Selling Losers: Implications for

Stock Market Efficiency." The Journal of Finance 48 (1993), 65-91.

Jegadeesh, N., and S. Titman. "Profitability of Momentum Strategies: An Evaluation of

Alternative Explanations." The Journal of Finance 56 (2001), 699-720.

25

Liu, L. X., and L. Zhang. "Momentum profits, factor pricing, and macroeconomic risk." Review

of Financial Studies 21 (2008), 2417-2448.

McLean, R. D., and J. Pontiff. "Does Academic Research Destroy Stock Return Predictability?"

In AFFI/EUROFIDAI, Paris December 2012 Finance Meetings Paper (2013).

Novy-Marx, R. "Is momentum really momentum?" Journal of Financial Economics 103 (2012),

429-453.

Schwert, G. W. "Chapter 15 Anomalies and market efficiency." In Handbook of the Economics

of Finance,Volume 1, Part B, M. H. G.M. Constantinides and R. M. Stulz, eds.: Elsevier (2003).

26

Figure 1.1 Average winner-loser portfolio returns by year

This figure plots the average monthly returns of winner - loser portfolios for each year during the 1965-2012.

Winner-loser portfolios are constructed using the methodology as described in Table 1.

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

Average Winner-Loser Portfolio Returns by Year

27

Figure 1.2 Comparison of distribution of momentum portfolios’ returns following Up markets

Panel A. 1965-1989 and 1999-2012

This figure plots the distribution of monthly returns of winner- loser portfolios, constructed as described in Table 1

following up-markets as defined in Table 6 for the first and the most recent subperiods. The solid line represents a

fitted normal distribution and the dashed line represents fitted kernel density, estimated with bandwidth parameter of

0.79.

19

65-1

98

9

1999

-2012

Distribution of Winners-Losers

Monthly Returns

28

Figure 1.2-Continued

Comparison of distribution of momentum portfolios’ returns following Up markets

Panel B. 1990-1998 and 1999-2012

This figure plots the distribution of monthly returns of winner - loser portfolios, constructed as described in Table 1

following up-markets as defined in Table 6 for the second and the most recent subperiods. The solid line represents

a fitted normal distribution and the dashed line represents fitted kernel density, estimated with bandwidth parameter

of 0.79.

19

90-1

99

8

1999

-2012

Distribution of Winners-Losers

Monthly Returns

29

Figure 1.2-Continued

Comparison of distribution of momentum portfolios’ returns following Up markets

Panel C. 1965-1989 and 1990-1998

This figure plots the distribution of monthly returns of winner - loser portfolios, constructed as described in Table 1

following up-markets as defined in Table 6 for the first and the second subperiods. The solid line represents a fitted

normal distribution and the dashed line represents fitted kernel density, estimated with bandwidth parameter of 0.79.

19

65-1

98

9

1990

-1998

Distribution of Winners-Losers

Monthly Returns

Fre

qu

ency

(%

) F

req

uen

cy (

%)

Distribution of Winners-Losers

30

Figure 1.3

Buy and hold abnormal returns of new entrants to winner and loser portfolios-Event study

This figure plots the abnormal buy and hold returns of new entrants to winner and loser portfolios (constructed as in Table 1) over t -5 to t + 24. Our initial

sample includes all NYSE, AMEX and NASDAQ stocks priced above $5 at the beginning of the holding period and with market capitalizations above the cut -

off level of lowest NYSE decile. New winners (losers) are the stocks that enter the winner (loser) portfolio in month t and are not included in the winner (loser)

portfolios in any of the months t -5 to t -1. Abnormal return for each event month is the average of the mean abnormal returns of all stocks with monthly return

data for 30 months, t -5 to t+24, across all calendar months. Buy and hold abnormal return is the difference between the cumulative raw return and cumulative

expected return for each stock for each event month. The expected returns are computed using the loadings on Fama-French three factors over the five year

period between t -71 to t - 13. Stocks with less than 24 monthly observations are excluded for the purpose of estimation of the loadings on the three factors.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

-5 0 5 10 15 20

Bu

y a

nd

Hold

Ab

norm

al

Ret

urn

s

Event Month

Winners

1965-1998 1999-2012

-0.5

-0.4

-0.3

-0.2

-0.1

0

-5 0 5 10 15 20

Bu

y an

d H

old

Ab

no

rmal

Re

turn

s

Event Month

Losers

1965-1998 1999-2012

31

Table 1.1

Momentum portfolios’ raw returns for 6-month/6-month strategy

This table presents the average monthly returns earned by momentum portfolios constructed with all NYSE, AMEX

and NASDAQ stocks after excluding stocks priced below $5 at the beginning of the holding period and stocks with

market capitalizations less than the cut -off level of lowest NYSE decile. At the end of each month (t) stocks are

sorted into 10 equally weighted portfolios based on their cumulative returns earned in the past six months (t-5 to t).

This table reports the mean of monthly average returns to these ten portfolios formed on the basis of the past 6

months’ cumulative returns and held for another 6 months for the three periods, 1965-1989, 1990-1998, and 1999-

2012. The bottom two rows of this table present the average returns and the corresponding p-values to the winner-

loser portfolios that buy winners (highest past return decile) and sells losers (lowest past return decile). All the

portfolios are equal weighted.

1965-1989 1990-1998 1999-2012

P1 (Past Losers) 0.0053 0.0044 0.0044

P2 0.0097 0.0087 0.0065

P3 0.0107 0.0112 0.0075

P4 0.0113 0.0119 0.0082

P5 0.0116 0.0120 0.0082

P6 0.0121 0.0125 0.0083

P7 0.0124 0.0124 0.0084

P8 0.0132 0.0132 0.0088

P9 0.0140 0.0143 0.0093

P10 (Past Winners) 0.0162 0.0181 0.0113

P10-P1 (Winners-Losers) 0.0110 0.0137 0.0069

p-value (0.00001) (0.00006) (0.28821)

32

Table 1.2

Fama-French three-factor alphas of momentum portfolios for 6-month/6-month strategy

This table presents the Fama-French three-factor alphas earned by momentum portfolios constructed with all NYSE,

AMEX and NASDAQ stocks after excluding stocks priced below $5 at the beginning of the holding period and

stocks with market capitalizations less than the cut -off level of lowest NYSE decile. This table reports the alphas

earned by the ten portfolios formed on the basis of the past 6 months returns and held for another 6 months in a

Fama-French three-factor OLS regression for the three periods, 1965-1989, 1990-1998, and 1999-2012. The bottom

two rows of this table present the alphas and the corresponding p-values to the winner-loser portfolios that buys

winners (highest past return decile) and sells losers (lowest past return decile). All the portfolios are equal weighted.

P-values are in parentheses.

1965-1989 (SP1) 1990-1998 (SP2) 1999-2012 (SP3)

P1 (Past Losers) -0.0076 -0.0086 -0.0038

(0.00000) (0.00026) (0.3726)

P2 -0.0026 -0.0037 -0.0009

(0.01312) (0.00657) (0.71179)

P3 -0.0014 -0.0010 0.0002

(0.08885) (0.30714) (0.90889)

P4 -0.0007 -0.0003 0.0010

(0.28627) (0.67969) (0.43469)

P5 -0.0003 -0.0002 0.0011

(0.56747) (0.69823) (0.31824)

P6 0.0004 0.0002 0.0012

(0.33207) (0.75546) (0.23665)

P7 0.0009 -0.0003 0.0010

(0.05875) (0.65804) (0.20557)

P8 0.0017 0.0006 0.0012

(0.00442) (0.34575) (0.25047)

P9 0.0027 0.0013 0.0010

(0.00111) (0.18517) (0.52526)

P10 (Past Winners) 0.0050 0.0049 0.0022

(0.00020) (0.00641) (0.3628)

P10-P1 (Winners-Losers) 0.0127 0.0135 0.0060

(0.00000) (0.00014) (0.3315)

33

Table 1.3

Momentum portfolios’ returns in times of extreme volatility for the period 1999-2012

This table presents the average monthly returns earned by momentum portfolios constructed with all NYSE, AMEX

and NASDAQ stocks after excluding stocks priced below $5 at the beginning of the holding period and stocks with

market capitalizations less than the cut -off level of lowest NYSE decile. At the end of each month (t) stocks are

sorted into 10 equally weighted portfolios based on their cumulative returns earned in the past six months (t -5 to t).

Panel A reports the monthly average returns for these ten portfolios formed on the basis of the past 6 months’

cumulative returns and held for another 6 months for the following periods:1999-2005, 2006-2012, 1999-2012

excluding 2007-2009, 2004-2012, excluding 2007-2009. The bottom two rows of this table present the average

returns and the corresponding p-values to the winner-loser portfolios that buy winners (highest past return decile)

and sells losers (lowest past return decile). All the portfolios are equal weighted. Panel B reports the three factor

alphas and the corresponding p-values to these ten portfolios formed on the basis of the past 6 months’ cumulative

returns and held for another 6 months for the following periods:1999-2005, 2006-2012, 1999-2012 excluding 2007-

2009, 2004-2012, excluding 2007-2009.

Panel A. Raw Returns

1999-2005 2006-2012 1999-2012

(excluding

2007-2009)

2004-2012

(excluding

2007-2009)

P1 (Past Losers) 0.0031 0.0057 0.0055 0.0090

P2 0.0058 0.0072 0.0085 0.0116

P3 0.0079 0.0070 0.0098 0.0121

P4 0.0093 0.0072 0.0105 0.0121

P5 0.0094 0.0069 0.0104 0.0116

P6 0.0094 0.0071 0.0104 0.0116

P7 0.0107 0.0061 0.0110 0.0113

P8 0.0122 0.0054 0.0121 0.0122

P9 0.0139 0.0046 0.0133 0.0126

P10 (Past Winners) 0.0188 0.0038 0.0169 0.0132

P10-P1 (Winners-Losers) 0.0157 -0.0019 0.0113 0.0042

p-value (0.16534) (0.76922) (0.12445) (0.21272)

34

Table 1.3-Continued

Momentum portfolios’ returns in times of extreme volatility for the period 1999-2012

Panel B. Fama-French Three-Factor Alphas

1999-2005 2006-2012

1999-2012

(excluding

2007-2009)

2004-2012

(excluding

2007-2009)

P1 (Past Losers) -0.0039 -0.0022 -0.0045 -0.0036

(0.61402) (0.56938) (0.36944) (0.09561)

P2 -0.0026 0.0003 -0.0014 0.0006

(0.57177) (0.90782) (0.63879) (0.63478)

P3 -0.0012 0.0005 -0.0003 0.0015

(0.69376) (0.76846) (0.89804) (0.17249)

P4 -0.0004 0.0010 0.0004 0.0019

(0.84265) (0.44695) (0.79911) (0.03197)

P5 -0.0003 0.0010 0.0002 0.0014

(0.87033) (0.30702) (0.86322) (0.04254)

P6 -0.0006 0.0011 0.0001 0.0011

(0.6819) (0.11662) (0.9618) (0.05639)

P7 0.0004 0.0002 0.0005 0.0008

(0.72019) (0.81471) (0.5543) (0.21261)

P8 0.0016 -0.0007 0.0011 0.0013

(0.29765) (0.56347) (0.30468) (0.1489)

P9 0.0027 -0.0018 0.0018 0.0011

(0.29304) (0.25189) (0.28732) (0.37606)

P10 (Past Winners) 0.0074 -0.0033 0.0044 -0.0001

(0.07625) (0.20674) (0.1086) (0.97379)

P10-P1 (Winners-Losers) 0.0113 -0.0011 0.0088 0.0035

(0.30598) (0.85694) (0.2132) (0.29435)

35

Table 1.4

Momentum portfolios’ returns following periods of low and high volatility

Panel A of this table presents the average monthly returns earned by momentum portfolios constructed with all

NYSE, AMEX and NASDAQ stocks after excluding stocks priced below $5 at the beginning of the holding period

and stocks with market capitalizations less than the cut-off level of lowest NYSE decile. At the end of each month

(t) stocks are sorted into 10 equally weighted portfolios based on their cumulative returns earned in the past six

months (t -5 to t). Panel A of this table reports the mean of monthly average returns to the P1 (Losers), P10

(Winners), and P10-P1 (Winners-Losers) portfolios formed on the basis of the past 6 months’ cumulative returns

and held for another 6 months for the two time periods, 1986-1998, and 1999-2012. These sub-periods are further

segregated into high and low volatility periods based on the median daily VXO of the 1986-1998 period (18.35).

Panel B of this table presents the Fama-French three-factor alphas earned by the P1 (Losers), P10 (Winners), and

P10-P1 (Winners-Losers) portfolios over the low and high liquidity periods for the two time periods, 1986-1998,

and 1999-2012. All the portfolios are equal weighted. P-values are presented in parentheses.

Panel A. Raw Returns

Low Volatility High Volatility

1986-1998 1999-2012 1986-1998 1999-2012

P1 (Past Losers) 0.0023 0.0052 0.0061 0.0040

P10 (Past Winners) 0.0155 0.0104 0.0164 0.0118

P10-P1 (Winners-

Losers) 0.0132 0.0052 0.0102 0.0079

p-value (0.00011) (0.12727) (0.01419) (0.43083)

Panel B. Three-Factor Alphas

Low Volatility High Volatility

1986-1998 1999-2012 1986-1998 1999-2012

P1 (Past Losers) -0.0101 -0.0037 -0.0047 -0.0030

(0.00002) (0.07099) (0.12227) (0.64902)

P10 (Past Winners) 0.0020 0.0014 0.0040 0.0020

(0.22431) (0.46185) (0.05376) (0.59428)

P10-P1 (Winners-

Losers) 0.0122 0.0051 0.0088 0.0050

(0.00051) (0.13526) (0.03527) (0.60353)

36

Table 1.5

Momentum profits over 14-Year rolling window for the period 1965 to 1999

This table presents the results a 14-year rolling window analysis in which we compute the average raw and risk-

adjusted momentum returns for every 14 years starting at the beginning of each year from 1965-1999. Panel A

reports the raw returns and Panel B reports the Fama-French three-factor alphas. P-values are presented in

parentheses.

Panel A. Raw Returns

Starting

Year P1 P10 P10-P1

Starting

Year P1 P10 P10-P1

1965 0.0040 0.0138 0.0097

1983 0.0048 0.0156 0.0108

(0.50097) (0.01057) (0.00891) (0.29677) (0.00109) (0.00001)

1966 0.0048 0.0138 0.0090

1984 0.0040 0.0154 0.0115

(0.42999) (0.01098) (0.01459) (0.40991) (0.00139) (0.0000)

1967 0.0067 0.0170 0.0102

1985 0.0048 0.0173 0.0125

(0.28104) (0.00246) (0.00503) (0.34635) (0.00087) (0.0000)

1968 0.0038 0.0129 0.0091

1986 0.0053 0.0192 0.0139

(0.53288) (0.0199) (0.01023) (0.30777) (0.00045) (0.0000)

1969 0.0026 0.0131 0.0105

1987 0.0018 0.0195 0.0176

(0.67227) (0.01562) (0.00354) (0.74192) (0.0037) (0.00014)

1970 0.0063 0.0154 0.0091

1988 0.0041 0.0203 0.0162

(0.29939) (0.00416) (0.01085) (0.54303) (0.00164) (0.00425)

1971 0.0054 0.0161 0.0107

1989 0.0003 0.0178 0.0175

(0.34804) (0.00185) (0.001) (0.96781) (0.00681) (0.00344)

1972 0.0055 0.0168 0.0114

1990 0.0033 0.0192 0.0159

(0.33145) (0.00105) (0.00048) (0.65656) (0.00373) (0.008)

1973 0.0053 0.0169 0.0116

1991 0.0064 0.0206 0.0142

(0.35019) (0.0012) (0.00036) (0.38144) (0.00159) (0.01764)

1974 0.0086 0.0179 0.0093

1992 0.0042 0.0182 0.0140

(0.11252) (0.00129) (0.00154) (0.55932) (0.00468) (0.01905)

1975 0.0119 0.0208 0.0089

1993 0.0040 0.0184 0.0145

(0.02073) (0.00012) (0.00147) (0.57812) (0.00415) (0.01522)

1976 0.0084 0.0201 0.0117

1994 0.0026 0.0175 0.0149

(0.07709) (0.00016) (0.0000) (0.72011) (0.00644) (0.0123)

1977 0.0039 0.0174 0.0135

1995 -0.0001 0.0144 0.0145

(0.42485) (0.00131) (0.0000) (0.99184) (0.03133) (0.01825)

1978 0.0063 0.0196 0.0132

1996 0.0031 0.0126 0.0095

(0.21134) (0.00042) (0.0000) (0.70168) (0.0636) (0.14944)

1979 0.0065 0.0187 0.0122

1997 0.0044 0.0133 0.0089

(0.20054) (0.00041) (0.0000) (0.59441) (0.05278) (0.17594)

1980 0.0049 0.0174 0.0125

1998 0.0031 0.0118 0.0087

(0.31504) (0.00073) (0.0000) (0.70708) (0.0888) (0.18691)

1981 0.0029 0.0139 0.0110

1999 0.0044 0.0113 0.0069

(0.53803) (0.00385) (0.00001) (0.58208) (0.08742) (0.28821)

1982 0.0042 0.0166 0.0123

(0.36802) (0.0005) (0.0000)

37

Panel B. Three-Factor Alphas

Starting

Year P1 P10 P10-P1

Starting

Year P1 P10 P10-P1

1965 -0.0058 0.0056 0.0114

1983 -0.0081 0.0032 0.0113

(0.00413) (0.00953) (0.00258) (0.00000) (0.01393) (0.00001)

1966 -0.0056 0.0053 0.0110

1984 -0.0081 0.0034 0.0115

(0.00514) (0.01132) (0.00324) (0.00000) (0.01174) (0.00000)

1967 -0.0068 0.0060 0.0128

1985 -0.0079 0.0040 0.0118

(0.00064) (0.00268) (0.00041) (0.00001) (0.00441) (0.00001)

1968 -0.0059 0.0054 0.0113

1986 -0.0085 0.0052 0.0136

(0.00185) (0.00665) (0.0012) (0.00003) (0.00027) (0.00000)

1969 -0.0068 0.0055 0.0123

1987 -0.0124 0.0078 0.0202

(0.00077) (0.00342) (0.00046) (0.00002) (0.00005) (0.00000)

1970 -0.0069 0.0051 0.0121

1988 -0.0111 0.0078 0.0189

(0.00096) (0.00667) (0.00073) (0.00698) (0.00041) (0.00111)

1971 -0.0080 0.0056 0.0136

1989 -0.0121 0.0078 0.0199

(0.00003) (0.00217) (0.00004) (0.00445) (0.00054) (0.00089)

1972 -0.0083 0.0057 0.0140

1990 -0.0111 0.0072 0.0183

(0.00002) (0.00154) (0.00003) (0.00916) (0.00172) (0.00242)

1973 -0.0083 0.0057 0.0140

1991 -0.0107 0.0060 0.0167

(0.00003) (0.00166) (0.00004) (0.01419) (0.01104) (0.00691)

1974 -0.0072 0.0037 0.0109

1992 -0.0100 0.0064 0.0164

(0.00007) (0.02196) (0.00032) (0.01948) (0.0058) (0.00685)

1975 -0.0073 0.0034 0.0106

1993 -0.0100 0.0064 0.0165

(0.00004) (0.02276) (0.00026) (0.01827) (0.00544) (0.00631)

1976 -0.0084 0.0029 0.0113

1994 -0.0108 0.0068 0.0176

(0.00000) (0.02074) (0.00001) (0.0094) (0.00272) (0.00287)

1977 -0.0099 0.0036 0.0135

1995 -0.0098 0.0063 0.0161

(0.00000) (0.00404) (0.00000) (0.01908) (0.0083) (0.00753)

1978 -0.0095 0.0039 0.0134

1996 -0.0076 0.0039 0.0115

(0.00000) (0.00236) (0.00000) (0.08537) (0.11538) (0.07024)

1979 -0.0095 0.0039 0.0134

1997 -0.0066 0.0036 0.0102

(0.00000) (0.00252) (0.00000) (0.13718) (0.14604) (0.11026)

1980 -0.0094 0.0038 0.0132

1998 -0.0052 0.0036 0.0087

(0.00000) (0.00409) (0.00000) (0.23098) (0.14742) (0.16447)

1981 -0.0092 0.0030 0.0122

1999 -0.0038 0.0022 0.0060

(0.00000) (0.01866) (0.00000) (0.3726) (0.3628) (0.3315)

1982 -0.0097 0.0037 0.0134

(0.00000) (0.00471) (0.00000)

38

Table 1.6

Momentum portfolios’ raw returns following Up and Down markets

This table presents the average monthly returns earned by momentum portfolios constructed with all NYSE, AMEX

and NASDAQ stocks after excluding stocks priced below $5 at the beginning of the holding period and stocks with

market capitalizations less than the cut -off level of lowest NYSE decile. At the end of each month (t) stocks are

sorted into 10 equally weighted portfolios based on their cumulative returns earned in the past six months (t -5 to t).

Positive (negative) returns of the value weighted CRSP index over the past 36 months define UP (DOWN) markets

as in Cooper, Gutierrez, and Hameed (2004). Panel A and B report monthly average returns to these ten portfolios

formed on the basis of the past 6 months’ cumulative returns and held for another 6 months for the three periods,

1965-1989, 1990-1998, and 1999-2012 following UP and DOWN markets, respectively. The bottom two rows of

this table present the average returns and the corresponding p-values to the winner-loser portfolios that buy winners

(highest past return decile) and sells losers (lowest past return decile). All the portfolios are equal weighted.

Panel A. Up Markets

1965-1989

1990-1998

1999-2012

P1 (Past Losers) 0.0032 0.0044 -0.0036

P2 0.0083 0.0087 0.0010

P3 0.0098 0.0112 0.0028

P4 0.0105 0.0119 0.0038

P5 0.0109 0.0120 0.0043

P6 0.0115 0.0125 0.0045

P7 0.0121 0.0124 0.0050

P8 0.0129 0.0132 0.0059

P9 0.0139 0.0143 0.0073

P10 (Past Winners) 0.0162 0.0181 0.0104

P10-P1 (Winners-Losers) 0.0130 0.0137 0.0140

p-value (0.00000) (0.00006) (0.09439)

Panel B. Down Markets P1 (Past Losers) 0.0214 0.0208

P2 0.0205 0.0178

P3 0.0181 0.0171

P4 0.0173 0.0174

P5 0.0169 0.0161

P6 0.0164 0.0160

P7 0.0149 0.0154

P8 0.0150 0.0148

P9 0.0147 0.0134

P10 (Past Winners) 0.0168 0.0132

P10-P1 (Winners-Losers) -0.0046 -0.0076

p-value (0.66024) (0.45452)

39

Table 1.7

Momentum portfolios’ raw returns for 6-month/6-month strategy –size and liquidity

This table presents the average monthly returns earned by momentum portfolios for Small Cap, Large Cap, Low Liquidity and High Liquidity stocks. Sample

includes all NYSE, AMEX and NASDAQ stocks after excluding stocks priced below $5 at the beginning of the holding period and stocks with market

capitalizations less than the cut -off level of lowest NYSE decile. At the end of each month (t) stocks are sorted into 10 equally weighted portfolios based on

their cumulative returns earned in the past six months (t -5 to t). This table reports the mean of monthly average returns to these ten portfolios formed on the basis

of the past 6 months’ cumulative returns and held for another 6 months for the 1965-1998 and 1999-2012 periods. The bottom two rows of this table present the

average returns and the corresponding p-values to the winner-loser portfolios that buy winners (highest past return decile) and sells losers (lowest past return

decile). All the portfolios are equal weighted. Small Cap (Large Cap) comprises of stocks that have market cap smaller (larger) than median market cap NYSE

stock. Illiquidity is estimated as ratio of absolute one day return to dollar volume in that particular day. Low (High) Liquidity stocks have higher (lower) average

illiquidity than the median illiquidity stock in the month t-6.

1965-1998 1999-2012

Small Cap Large Cap Low

Liquidity

High

Liquidity Small Cap Large Cap

Low

Liquidity

High

Liquidity

P1 (Past Losers) 0.0045 0.0070 0.0060 0.0046 0.0059 0.0018 0.0050 0.0045

P2 0.0096 0.0098 0.0103 0.0092 0.0068 0.0049 0.0063 0.0061

P3 0.0114 0.0104 0.0120 0.0104 0.0082 0.0058 0.0080 0.0066

P4 0.0120 0.0110 0.0126 0.0111 0.0095 0.0069 0.0093 0.0075

P5 0.0123 0.0110 0.0129 0.0110 0.0094 0.0069 0.0092 0.0075

P6 0.0131 0.0112 0.0134 0.0114 0.0099 0.0067 0.0099 0.0073

P7 0.0134 0.0112 0.0140 0.0112 0.0101 0.0068 0.0102 0.0071

P8 0.0141 0.0120 0.0144 0.0121 0.0104 0.0075 0.0103 0.0080

P9 0.0150 0.0128 0.0155 0.0127 0.0111 0.0080 0.0104 0.0089

P10 (Past Winners) 0.0171 0.0159 0.0175 0.0159 0.0128 0.0108 0.0126 0.0114

P10-P1 (Winners-Losers) 0.0126 0.0088 0.0114 0.0114 0.0069 0.0090 0.0076 0.0069

p-value (0.00000) (0.00013) (0.00000) (0.00000) (0.34524) (0.28351) (0.25872) (0.39777)

40

Table 1.8

Fama-MacBeth regressions of stock returns on past 11 months cumulative returns, β, size, and BE/ME

This table presents the average slopes from month-by-month regressions of stock returns on cumulative past returns, beta, size, and book-to-market for each sub-

period. We consider all NYSE, AMEX and NASDAQ stocks that have data available both on CRSP and COMPUSTAT. Following Fama and French (1996), the

cumulative past returns for each stock, each month are computed by cumulating their returns from t - 12 to t - 2 months. Stocks are assigned post -ranking β of

the size-β portfolio they are in at the end of June of year t. BE is the book value of common equity plus balance-sheet deferred taxes. BE is obtained for each

firm's latest fiscal year ending in calendar year t - 1. The accounting ratio is computed using market equity ME in December of year t - 1. Firm size ln(ME) is

measured in June of year t. In the regressions, these values of the explanatory variables for individual stocks are matched with CRSP returns for the months from

July of year t to June of year t + 1. The gap between the accounting data and the returns ensures that the accounting data are available prior to the returns. LNBM

is natural logarithm of BE/ME. P-values are in parentheses. *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.

1965-1989 1990-1998 1999-2012

1 2 3 1 2 3 1 2 3

CUM_RETURN

0.00895***

0.00758***

0.00738***

0.00381**

0.00404** 0.00397**

-0.00483 -0.00483 -0.00479

(0.00060) (0.00043) (0.00070)

(0.03104) (0.02292) (0.02408)

(0.21966) (0.17721) (0.17314)

POST BETA - -0.00430 -0.00236

- 0.00007 0.00209

- 0.00198 0.00209

(0.11551) (0.35366)

(0.98706) (0.60016)

(0.72634) (0.68630)

LNME -

-

0.00197*** -0.00142***

-

-

0.00218** -0.00172*

-

-

0.00192***

-

0.00149*

(0.00023) (0.00594)

(0.01131) (0.05288)

(0.00844) (0.08282)

LNBM - - 0.00331***

- - 0.00264**

- - 0.00226*

(0.00001)

(0.01451)

(0.08015)

Number of

observations 300 300 300 108 108 108 168 168 168

41

Table 1.9

Measures of delay for the three sub-periods

This table presents the delay for the 5 size quintiles (individual stocks) for our sample of stocks. Delay is computed

by subtracting the adjusted R2 of unrestricted market model from the adjusted R

2 of the restricted market model

(Delay = R

). The unrestricted model uses four lags of weekly market returns:

,where is the weekly portfolio

(individual stock) return at time t and is the market return. In the restricted model, the coefficients on the lagged

market returns are constrained to zero:

In Panel A, weekly returns of five size portfolio are the dependent variables in the market model. Weekly returns are

the equal weighted portfolio returns for the size quintiles. All stocks in our sample are sorted into quintiles at the end

of previous year. In Panel B, weekly returns of individual stock are the dependent variables in the market model. For

each size quintile, we then compute the average delay. We also report the difference between the average delays of

each subperiods and the corresponding p-values.

Panel A. Portfolio

Small 2 3 4 Large

1965-1989 0.0403 0.0283 0.0218 0.0095 0.0003

1990-1998 0.0647 0.0380 0.0244 0.0080 0.0009

1999-2012 0.0074 0.0094 0.0049 0.0035 0.0004

Panel B. Individual Stocks

Small 2 3 4 Large

1965-1989 (SP1) 0.0119 0.0100 0.0102 0.0104 0.0049

1990-1998 (SP2) 0.0129 0.0114 0.0082 0.0105 0.0051

1999-2012 (SP3) 0.0082 0.0090 0.0092 0.0063 0.0056

Diff. SP1 and SP2 -0.0010 -0.0014 0.0020 -0.0001 -0.0002

p-value (0.49998) (0.37011) (0.23147) (0.93166) (0.82985)

Diff. SP1 and SP3 0.0037 0.0011 0.0010 0.0040 -0.0007

p-value (0.01229) (0.4901) (0.54442) (0.00247) (0.48949)

Diff. SP2 and SP3 0.0047 0.0024 -0.0010 0.0041 -0.0005

p-value (0.00027) (0.04416) (0.48961) (0.00053) (0.64346)

42

Paper II

Venture Capital Liquidity Pressure and Exit Choice

(Co-authored with Ozgur Ince)

2.1 Introduction

A lot of partnerships are 10 years, so many are looking for exits now. The IPO market

has calcified, so M&A is the only exit for many. It takes a long time to do these deals, so

they better get on it. Ideally, people would like to take their time, but the reality is they

can't afford to now. “Venture capital firms turn to M&A more for exits,” MarketWatch,

2006.

- Dick Kramlich, senior partner and co-founder of New Enterprise Associates

In this study we examine the impact of venture capital funds’ limited lifespan on the timing

and the outcome of their portfolio companies’ liquidity events. The majority of venture capital

funds (VCs) are structured as limited partnerships with a limited life span of 10 years.1 At the

end of the funds’ lifespan, the general partners are contractually obliged to dissolve the fund by

liquidating the remaining equity holdings in the portfolio and return the proceeds to their limited

partners. The limited lifespan of venture capital funds is a standard contractual feature designed

to protect the limited partners from general partners’ conflicts of interest (Sahlman, 1990).

While this mandatory liquidation requirement may protect limited partners from

expropriation, we posit that it can also affect various other aspects of the venture capital process

1 VC firms use their investors’ capital to acquire large minority stakes in young and high-risk private start-ups that

offer the potential of high returns. In independent limited partnerships with a limited life span, the venture capitalists

serve as general partners and the investors as limited partners. An independent limited partnership VC funds’

lifespan can be extended to 12 or 13 years in one- or two-year increments with the consent of the funds’ board of

advisors or at the discretion of the general partners (Sahlman, 1990). 72% of venture capital funds raised in number

and 76% in dollars between 1985 and 2012 were structured as independent limited partnerships. The rest were

mostly subsidiaries of industrial and financial corporations and university endowments.

43

in material ways. In particular, we investigate whether the obligation to dissolve the fund

imposes a constraint on venture capitalists and influences their investment and exit decisions.

The limited lifespan of independent limited partnership venture capital funds might act as a

binding constraint for two main reasons. First, venture capital investments are inherently illiquid

and venture capitalists rely on major liquidity events (e.g., initial public offerings (IPOs) and

trade sales) to generate the high returns expected by their limited partners. The start-up firms

financed by the VCs typically require multiple financing rounds over many years to reach the

maturity required for successful exits (usually 5-10 years; average of 7.9 years in our sample).

Second, IPO and M&A markets are inherently cyclical, frequently going through cold periods

with low deal volume and low valuations.2 This cyclicality can pose a significant challenge for

VCs since turning down a profitable exit opportunity today in favor of a potentially better but

uncertain exit in the future could be costly if the window closes. Consequently, the limited

lifespan of VC funds is likely to influence general partners’ decisions long before the funds

actually mature and are dissolved.

We empirically test this “VC liquidity pressure” hypothesis by conditioning on VC funds’

age and comparing the exit outcomes of VC funds that are under liquidity pressure with those

that are not.3 Our sample includes 6,966 successful exits via initial public offerings and trade

sales of companies backed by independent limited partnership VCs between 1985 and 2012. The

mean age of independent venture capital funds at the time of the exits is 6.96 years, with 42% of

exits occurring on or after VC funds’ eighth year and 30% occurring on or after their ninth year.

2 See, for instance, Lowry and Schwert (2002) for IPO market cycles, Harford (2005) for M&A waves, and Dittmar

and Dittmar (2008) for a comprehensive examination of corporate financing waves. 3 Note that it is the age of the VC fund rather than that of the VC firm that is relevant for the liquidity hypothesis.

VC firms do not have a limited life span and can manage multiple overlapping funds. In our analysis we use the age

of the VC firms as a proxy for VC reputation and skill following earlier studies (see, e.g., Gompers (1996)).

44

Focusing on independent VCs with limited lifespans, we find a significantly negative relation

between the age of the VC funds at the time of the investment and the time until exit. In

univariate results, we find that entrepreneurial firms backed by VC funds that are five years old

at the time of the first VC financing round have, on average, seven months less until exit

compared to firms that receive a first financing round from a VC fund that is in its first year after

inception. Controlling for portfolio firm and VC characteristics in a multivariate framework, we

find that older VC funds are associated with significantly quicker exits especially when they are

the lead VC and have greater influence on the portfolio firm, suggesting that the relation between

fund maturity and exit timing is primarily due to the funds’ influence on their portfolio firms (the

influence channel) rather than the funds’ strategic choice of portfolio firms (the sorting channel).

We also find that older funds are more likely to exit their portfolio companies during cold

markets, providing evidence that increasing liquidity pressure lowers VC funds’ ability to time

the market.

Next, we investigate the effect of VC funds’ liquidity pressure on the method of exit from

their portfolio companies. We hypothesize that the longer time commitment, illiquidity, and

uncertainty associated with IPO exits might lead older VC funds to prefer a sure gain from an

immediate trade sale to a potentially more lucrative but uncertain future IPO. We find that

entrepreneurial firms backed by VC funds that are older at the time of the exit are indeed

significantly more likely to be acquired than go public. Focusing on successful exits (i.e., IPOs

vs trade sales), a one standard deviation increase in the age of the VC fund at the time of the exit

from the mean of 7 to 9.65 is associated with a 5.0 percentage point decline in the likelihood of

an IPO from an unconditional probability of 30%. Furthermore, we find that the negative relation

45

between fund age and the likelihood of an IPO is stronger for lead VCs, suggesting that liquidity

pressure works through the influence channel.

A potential concern with the exit method analysis is that if VC fund age at exit is correlated

with portfolio firm quality, this might cause a spurious relation between fund age and exit choice

if firm quality is not adequately controlled for. More specifically, if higher quality firms are

exited earlier, the likelihood of an IPO might drop with VC fund age due to a decline in the

quality of the remaining portfolio of firms rather than an increase in the fund’s liquidity pressure.

We address this potential endogeneity concern in three ways. First, we repeat our exit method

analysis after excluding trade sales with low (or undisclosed) transaction values based on the

idea that these portfolio firms are likely to be of lower quality and unlikely to have a realistic

choice between an IPO and a trade sale. Second, we exploit time-varying capital market

conditions as a source of exogeneous variation in VC funds’ liquidity considerations and conduct

a two-stage analysis with past IPO market conditions as the instrument. And third, we use

propensity-score matching to estimate the impact of VC fund age on exit method in a subsample

of firms with similar characteristics along several dimensions. With all three methods, we find

that the relation between VC fund age at exit and the likelihood of an IPO is negative and both

statistically and economically significant.

Next, we turn our attention to VC-backed portfolio firms that go public and examine their

lock-up expiration. Several studies report an abnormally high trading volume and a permanent

stock price decline around lockup expirations, especially for firms with venture capital backing.4

For instance, Field and Hanka (2001) document that venture capital backed firms experience

4 Lockup agreements are voluntary but standard agreements between the issuing firms’ shareholders and the IPO

underwriters that restrict the insiders and pre-IPO shareholders from selling any of their shares for a pre-specified

period of time after the IPO. Lockups usually last for 180 days and cover most of the shares that are not sold at IPO.

For empirical analyses of IPO lockups, see for instance Bradley et al. (2001), Field and Hanka (2001), and Brav and

Gompers (2003).

46

abnormal returns that are almost three times larger and abnormal trading volume that is five

times larger compared to firms without VC backing during the three days surrounding the lockup

expiration, and interpret this as evidence of aggressive selling by venture capital funds.

Consistent with the VC liquidity pressure hypothesis, we find that portfolio firms backed by VC

funds that are closer to liquidation experience significantly lower stock returns and larger

abnormal trading volume around their lockup expirations. Moreover, both the trading volume

and stock return effects are more pronounced when there are multiple independent VCs under

liquidity pressure, whereas the number of independent VCs that are not under liquidity pressure

does not matter.

To briefly preview our results, we document that portfolio firms backed by VC funds that are

at the tail-end of their limited lifespan experience earlier exits, are more likely to be sold off than

taken public, are more likely to be exited during colder markets, and are more likely to

experience insider selling at the time of lock-up expiration following IPOs. Our results indicate

that the limited lifespan of independent VC funds has real consequences for the timing and the

outcome of their portfolio firms’ exit events.

Two recent studies also focus on the limited lifespan of venture capital funds. Theoretical

work by Kandel, Leshchinskii, and Yuklea (2011) shows that funds’ limited life horizon and

general partners’ informational advantage over the limited partners lead to inefficient decisions

during the investment cycle. However, they do not investigate the consequences of the funds’

limited lifespan on the exit cycle. Masulis and Nahata (2011) investigate the effects of VC

backing on the profitability of private firm acquisitions. They report that portfolio firms backed

by VC funds nearing maturity earn a lower acquisition premium over the book value of their

assets; however, they do not investigate the impact of fund maturity on the timing of exits and

47

the IPO process. Our study is also related to Gompers (1996) who shows that young venture

capital firms take their portfolio companies public earlier than the more established firms in order

to establish a track record quickly and raise capital for a new fund. Our results show that the

limited lifespan of VC funds cause a similar exit timing behavior regardless of the VC firm’s

reputation and future fundraising concerns.

This article also relates to the broader literature that examines the impact of VCs’ incentives

and the structure of VC contracts on exit outcomes. Cumming (2008) finds that the use of

convertible securities in VC investments is associated with a higher frequency of acquisitions

and fewer IPOs. There is also evidence that companies that share a common VC are more likely

to engage in strategic alliances (Lindsey, 2008) and successful acquisitions (Gompers and Xuan,

2008). Ince (2012) finds that IPO firms are more likely to grant the underwriting mandate to

investment banks with a strong relationship with the firms’ leading VCs, and such repeated

pairings between the investment banks and VC firms are associated with better IPO outcomes.5

Finally, several other articles also examine trading volume and stock returns around IPO

lockup expirations. Field and Hanka (2001) and Brav and Gompers (2003) attribute the

permanent price decline around the lockup expiration to a combination of downward sloping

demand curves, limited arbitrage in the form of restricted short-selling, and systematically biased

prior beliefs about the extent of insider selling. We contribute to this literature by documenting

that VC funds’ liquidity pressure is an important factor in lockup expirations. While our results

shed new light on this enduring market anomaly, they also add a new piece to the puzzle given

that VC funds’ time to maturity is observable and the predictable selling of shares by VC funds

should not raise any adverse selection concerns.

5 For a recent comprehensive survey of venture capital research, see Rin, Hellmann, and Puri (2011) and Metrick

and Yasuda (2011).

48

2.2 ‘VC liquidity pressure’ hypothesis

VC funds are typically organized as limited partnership and VC firms act as general partners

(GPs) for them. The limited partners (LPs) of VC funds are mostly institutional investors who

commit to provide a certain amount of capital during initial fundraising. Independent VC funds

typically have a finite life of 10 years, with an option to extend their life up to 12 or 13 years.

During the ten years of the fund’s typical lifetime, GPs select, monitor, mentor and provide a

variety of other services for their portfolio companies. At the end of this period the fund needs to

dissolve and distribute its profits to its LPs. In this paper we investigate whether the limited

lifespan of VCs acts as a constraint in general partners’ investment and exit decisions. In

particular, we examine whether VC-backing by funds nearing maturity influence their portfolio

firms’ (i) exit timing, (ii) exit method (IPO or trade sale), and (iii) the lock-up expiration

following IPOs. We label this as the ‘VC liquidity pressure’ hypothesis.

Venture capital funds usually hold large equity stakes and obtain significant control rights in

their portfolio firms. Most notably, venture capitalists hold multiple board seats, maintain veto

rights that grant them control over potential exit events, and retain the right to put their

investment back in the portfolio firm at original cost plus the cumulative dividends accrued.

These latter redemption rights provide the general partners with leverage over the entrepreneur

based on the credible threat of withdrawal in addition to allowing them to extract their original

investment from portfolio firms that are unlikely to succeed.6

There is growing empirical evidence that such control rights effectively grant VCs influence

over their portfolio firms’ exits. Cumming (2008) finds that the use of convertible securities in

6 See Sahlman (1990), Lerner (1994), and Smith (2005) for the properties of contracts between general partners and

limited partners of venture capital funds.

49

VC investments is associated with a higher likelihood of trade sales, consistent with the theories

of Bascha and Walz (2001) and Hellmann (2006). There is also evidence that companies that

share a common VC are more likely to engage in strategic alliances (Lindsey, 2008) and

successful acquisitions (Gompers and Xuan, 2008). Ince (2012) finds that VCs’ prior

relationships with investment banks influence their portfolio firms’ choice of IPO underwrites.

Gompers (1996) documents that young VC firms are associated with quicker IPOs at lower

valuations, and interprets this as evidence that venture capitalists lacking a strong track record

force their portfolio firms to early exits in order to facilitate future fundraising.

Our empirical tests focus on how VC funds’ time to maturity affects their portfolio firms’ (i)

exit timing, (ii) exit method, and (iii) abnormal trading volume and stock prices around the

expiration of the lock-up periods. We use the age of the VC fund at the time of exit as our

primary proxy for the liquidity pressure faced by venture capitalists, based on the notion that VC

funds which are closer to maturity are more likely to be under pressure to exit their portfolio

firms. In addition, in several tests we pay special attention to the liquidity pressure faced by the

lead VC firm, which typically has the most control rights and influence over the portfolio

company. Since VCs’ ownership stakes and control rights are not reported by most commonly

used commercial databases, we follow earlier studies (e.g., Masulis and Nahata, 2011 and Lee

and Wahal, 2004) and designate a VC as the lead on the basis of VC firms’ pre-exit financing

rounds as reported by VentureXpert. More specifically, we classify a VC fund as the lead VC for

a portfolio firm if it participates in the firm’s first VC financing round and its VC firm makes the

largest total investment in the firm across all pre-exit investment rounds. The lead VC

designation allows us to investigate whether the liquidity pressure of the VCs with larger

50

influence over the portfolio firm has a larger impact on the portfolio firm’s exit timing and

method.

2.3 Data and summary statistics

2.3.1. Sample selection

Our primary sample includes all VC investments made by U.S. based independent VC firms

in private entrepreneurial companies headquartered in the U.S. with a successful exit (via an IPO

or a trade sale) between 1985 and 2012. The venture capital investment sample is drawn from

Thomson Financial’s VentureXpert and includes data on investment dates, investment amounts,

identities and characteristics of venture capital firms and their funds, and the exit outcomes of

VC-backed portfolio firms. We supplement VentureXpert with IPO data from Thomson

Financial’s Global New Issues and acquisition data from Merger and Acquisitions databases.

Our focus on VC funds’ liquidity pressure requires complete data on VC funds’ identities and the

dates for inception, investment, and exit from portfolio companies. We exclude investments by

angel investors and subsidiary VC firms (i.e., venture capital operations of corporations,

insurance companies, and financial institutions), which do not typically have limited lifespans.7

We obtain monthly return data from the Center for Research in Security Prices (CRSP) database

to calculate industry returns. We collect data on patents granted to the entrepreneurial companies

in our sample from the US Patent and Trademark Office using fuzzy name and headquarter

location matching.

7 Funds with Investment type ‘PRIV’ and VC firms that have firm type “Private Equity Firm” are classified as

independent VCs. Corporations, insurance companies, and financial institutions are classified as subsidiary VCs.

Investments by funds with incomplete identification and missing inception dates are excluded. We also exclude

investments that occur more than ten years after VC funds’ inception since the majority of such investments are

erroneously attributed to an earlier fund in the VC organization due to unknown identity information.

51

Lock-up expiration data for VC-backed IPOs is from the Global New Issues Database in

SDC Platinum (Securities Data Corporation) provided by Thomson Reuters between 1985 and

2012. We apply the conventional filters and exclude firms that issue a security other than

common equity, financial firms (SIC codes 6000-6999), spinoffs and carve-outs, reverse LBOs,

ADRs, foreign listings, and those with an offer price less that $5. For firms with multiple share

classes, we calculate total shares outstanding by summing up shares outstanding across all

classes. IPO firms with multiple share classes are obtained from Jay R. Ritter’s website.8 We

obtain daily returns, daily trading volume and shares outstanding from CRSP database for

portfolio firms with successful initial public offerings. IPOs with missing lockup dates are

excluded from tests that require an exact date for the lockup expiration.9

2.3.2. Variable definitions and summary statistics

Table 2.1 presents descriptive statistics for the variables used in our empirical tests. Panel A

reports variables that are measured at the individual VC investment level. VC fund age at

investment is calculated as the number of years between a fund’s inception and its financing

round in a portfolio firm. Time until exit is calculated as the number of months between an

investment and the VC’s exit from the portfolio company via an IPO or trade sale. First VC

round dummy equals one if the investment round marks the first time an entrepreneurial

company received capital from a venture capital fund, and zero otherwise. 19% of all venture

capital investments are made at the first VC financing round. Syndicate size is the number of

8 http://bear.warrington.ufl.edu/ritter/ipodata.htm

9 For firms with multiple lockup expiration days, we pick the earliest date reported by SDC if the percentage of the

shares released on that date is larger than 15%, otherwise we choose the date with the greatest percentage of shares

released. We exclude firms with multiple lockup expiration dates if data on the percentage of shares released is not

reported.

52

distinct VC funds participating in the financing round. On average, each financing round has

participation by 3.14 VC funds.

Panel B reports variables that are measured at the portfolio firm – VC fund level, and thus do

not vary across multiple investments of the same VC fund in the same entrepreneurial firm. Fund

age at exit is the number of years between a fund’s inception and the portfolio firm’s exit event

via an IPO or trade sale. We define the lead VC firm as the one that makes the largest total

investment across all rounds of funding after participating in the first VC financing round (see

also Nahata (2008) among others). Following Nahata (2008), we measure VC capitalization

share as the cumulative market value of all companies taken public by the VC firm over the five

years prior to the VC’s first investment in the portfolio firm, normalized by the aggregate market

value of all VC-backed companies that went public during the same time period. Following

Hochberg et al. (2007), we measure VC connectedness as the number of unique VCs each VC

has syndicated with during the five years prior to the VC’s first investment in the portfolio firm,

normalized by the number of all possible combinations during the same time period. Following

Chen et al. (2010), VC center dummy equals one if the VC firm is located in the Combined

Statistical Areas of San Francisco, New York, or Boston, and zero otherwise. Chen et al. (2010)

find that both VCs and their portfolio companies concentrate in these three geographic regions

and VC firm located in these VC centers exhibit better performance.

Panel C reports variables that are measured at the portfolio firm level. IPO dummy equals one

if the portfolio firm has an IPO, and zero if it is exited via a trade sale. 29% of the successful

exits in our sample are via an IPO. # of VC rounds is the number of distinct VC financing rounds

received by the portfolio firm prior to an IPO or trade sale. Each portfolio firm receives an

average of 3.42 VC financing rounds prior to a successful exit. We collect the number of patents

53

granted to the entrepreneurial by the United States Patent and Trademark Office with an

application date that falls between the first VC financing round and the exit date. We measure

the number of IPOs in prior quarter as the number of completed VC-backed IPOs in the same

industry during the three months prior to the month of the exit. Lagged # of IPOs (qtrs. -2:-9) is

the number of completed VC-backed IPOs during the two year period ending three months prior

to the month of the exit. Market returns in prior quarter is the equally-weighted stock returns of

public firms belonging to the high-tech industries (three-digit SIC codes of 283, 481, 365-369,

482-489, 357, and 737) during the three months prior to the month of the exit.

2.4 Timing of VC exits

According to the ‘VC liquidity pressure’ hypothesis, independent VCs face a pressure to exit

their investments as their funds approach maturity. In this section we examine the empirical

relation between VC funds’ time to maturity and the timing of their portfolio firms’ exit events,

and investigate if funds’ limited lifespan acts as a binding constraint.

First, we split the time between a fund’s closing date and its exit from a portfolio company

into two periods: (i) the time between the closing of the fund and the date of the fund’s

investment in the portfolio company, and (ii) the time between the investment and the fund’s exit

from the portfolio company. If the timing of exit is unrelated to the VC funds’ liquidity

considerations, solely dictated by the start-ups’ characteristics (e.g., growth rate, profitability

etc.) and market conditions instead, we should not observe a significant relation between the

funds’ age at the time of investment and the time until exit. On the contrary, VC liquidity

pressure hypothesis predicts a negative relation between the two: a start-up backed by a VC fund

closer to maturity will experience a quicker exit.

54

In Table 2.2 we sort VC funds by their age at the time of an investment in a portfolio

company and report summary statistics for the number of months between the investment and the

portfolio company’s exit event. The first two columns report the mean and median time until exit

for all VC investments. We find a monotonic negative relation between the age of the fund at the

time of the investment and the number of months until exit. The mean (median) number of

months between investment and exit is 54.9 (47) months for portfolio companies that receive

financing from a VC that is one year old at the time of the investment. In comparison, the mean

(median) number of months until exit is 37.4 (29) months for VC funds that are 10 years old. The

mean and median difference of -17.5 and 18 months, respectively, are statistically highly

significant.

The significantly negative relation between the age of the VC fund and the time until exit

suggests that VC funds’ limited lifespan is a binding constraint on the timing of their portfolio

firms’ exit. There are two mutually non-exclusive possible explanations for this relation. First,

VC funds might choose their portfolio firms strategically and avoid investing in start-ups that are

expected to take too long to mature when the fund is nearing maturity—the sorting channel.

Second, VC funds might exercise their control rights and influence their portfolio firms towards

earlier exit events when they are under liquidity pressure—the influence channel. The distinction

is important: if VC funds’ liquidity pressure works through the `influence channel’, it might

impose externalities on the portfolio companies, whereas through the `sorting channel’, it would

not.

One way to distinguish between the sorting and influence channels is to focus on VC-backed

portfolio firms’ first VC financing round. We posit that while sorting might play an important

role in later stages when the portfolio firm is close to an exit, sorting is unlikely to be a factor in

55

early stages. To that end, Table 2 also reports summary statistics for the number of months until

exit for the subsample of first round VC investments. We limit this analysis to VC funds that are

five years old or younger at the time of the first round of investment given the standard covenant

in VC partnership agreements that restricts initial investments in new portfolio companies to the

first five years of funds’ lives.

According to Table 2.2, first round investments also exhibit a significantly negative relation

between VC fund age and time until exit. Portfolio firms that receive their initial VC financing

from a fund that is five years old have on average seven fewer months until exit compared to

those financed by a fund at its first year. Moreover, the difference is observed primarily on the

right tail of the distribution, which is consistent with the idea that the primary effect of liquidity

pressure is on portfolio companies that take relatively longer to exit.

In Table 2.3 we explore the determinants of exit timing in a multivariate regression

framework and further distinguish between the sorting and the influence channels. The

dependent variable is the natural logarithm of the number of months between the first round of

VC financing received by a portfolio firm and its exit date. We relate the time until exit to the

age of the VC fund at the time of the investment (in years), the exit method (IPO vs. trade sale),

the size of the VC syndicate in the financing round, the natural logarithm of the adjusted number

of patents granted to the portfolio company from applications prior to its exit10

, VC capitalization

share, VC connectedness, VC center dummy, the natural logarithm of the number of IPOs in the

same industry during the prior three months, the average stock return of public companies in the

high-tech industries during the prior three months, and industry fixed effects. In the first column

the sample includes all investments made by independent VC funds in portfolio companies that

10

We calculate the natural logarithm of the adjusted number of patents as the residual from an OLS regression of the

natural logarithm of one plus the number of patents on the number of years between the firm’s first VC financing

round and exit, the number of years squared, and year- and industry-fixed effects.

56

are subsequently exited via an IPO or trade sale.11

In columns 2 and 3 we limit the sample to

portfolio firms’ initial VC financing round only.

In specification 1, we find a very significantly negative relation between the age of the fund

at the time of an investment and the time until exit, confirming the univariate results from Table

2.2. In column 2 with the subsample of first round VC investments, the relation remains

significantly negative. Column 3 adds an interaction between the age of the fund at the time of

the first round investment and an indicator that equals one if the VC fund is the lead VC for that

investment and zero otherwise. The interaction variable is intended to capture the marginal

impact of the liquidity considerations of VC funds with larger influence over the management of

their portfolio companies. According to the `sorting channel’, the coefficient on the interaction

term should be insignificant since all VCs are expected to have similar strategic motives in

choosing their portfolio firms regardless of the amount of influence they have over the portfolio

firm. On the other hand, the `influence channel’ predicts a significantly negative coefficient on

the interaction term since lead VCs are expected to have a greater influence on their portfolio

firms’ exit decisions. We indeed find that the interaction variable has a significantly negative

coefficient whereas the coefficient on the stand alone fund age variable becomes only marginally

statistically significant with a t-statistic of -1.9. Overall, the multivariate results in Table 2.3

confirm the univariate results, and provide support for the argument that VC funds’ liquidity

pressure affects the timing of their portfolio firms’ exit events via the influence channel.

Several other factors affect the timing of exits. We find that IPOs are associated with quicker

exits after investment. After controlling for the method of exit, proxies for the quality of the

11

Therefore, each exit event is represented multiple times since each portfolio firm typically receives multiple

rounds of financing from multiple VC funds.

57

portfolio firm and the VCs appear to be positively related to the time until exit. We find that

start-ups with more patents and those backed by a larger number of VCs at the financing round

and by VCs with larger market shares have longer time until exit. Finally, we find that exits that

occur during more favorable IPO conditions tend to be quicker exits, consistent with the idea that

VCs are eager to take advantage of better market conditions before the window of opportunity

closes (see also Giot and Schwerenbacher, 2007).

The results in Tables 2.2 and 2.3 are consistent with the idea that independent VC funds’

liquidity considerations impose a binding constraint on their exit policy. In Table 2.4 we

investigate if this binding constraint causes a loss of flexibility in the VCs’ ability to time the exit

market. The dependent variable is a dummy that equals one if the exit occurs during cold IPO

market conditions. We classify an exit as one occurring during a cold market if the number of

IPOs during the prior three months is below the median for all successful exits. We relate the

market conditions at the time of the exit to the age of the fund at the time of the investment and

exit, along with the control variables from Table 2.3 with the exception of proxies related to

market conditions. In Table 2.4 we report the marginal effects of the independent variables. In

addition, we standardize the continuous independent variables such that they have a mean of zero

and a standard deviation of one. As a result, the reported marginal effects capture the effect of a

one standard deviation change in the regressor on the probability of the exit occurring during

cold exit market conditions.

Column 1 of Table 2.4 reveals a significantly positive relation between the age of the VC

fund at the time of the investment and the probability of exit during a cold market. The marginal

effect is 0.024, indicating that a one standard deviation increase in the age of the VC fund is

associated with a 2.4 percentage point increase in the likelihood of exit during a cold market.

58

Column 2 adds the age of the fund at the time of the exit. We observe that the age of the fund at

both the time of investment as well as the exit have significantly positive coefficients when

included together. The marginal effect of age at exit is 0.023, indicating a further increase in the

likelihood of exit during a cold market of 2.3 percentage points as a result of a one standard

deviation increase in the age of the fund at the time of the exit. Altogether, the results in Table

2.4 indicate that the liquidity pressure documented in Table 3 is also associated with a decline in

the flexibility to time the exit market. To the extent that conducting an exit during colder markets

is less desirable, these results suggest that VC liquidity pressure is associated with a deviation

from the optimal exit policy.

2.5 Exit Choice

In this section, we investigate the impact of VCs’ liquidity pressure on the method of exit.

More specifically, we relate the age of the VC fund at the time of the exit to the decision to exit

via an IPO or trade sale. The liquidity pressure hypothesis predicts a negative relation between

fund age at exit and the probability of IPO for two reasons. First, the results in Table 2.4 show

that later exits are more likely to occur during colder IPO markets. Given the well-documented

positive relation between IPO market conditions and the likelihood of an IPO over a trade sale

(see, e.g., Nahata 2008), later exits should also be less likely to be via an IPO due to the reduced

flexibility of aging funds to time the market. Second, the liquidity pressure should be more

severe with IPOs due to the increased time commitment and illiquidity associated with a

prolonged exit process, and the associated increase in the sensitivity of deal success to uncertain

future market conditions. In other words, a trade sale might be preferable to an IPO on an

uncertainty- and illiquidity-adjusted basis for a VC fund that is under liquidity pressure, even if

an IPO might generate larger exit proceeds conditional on success.

59

Figure 2.1 presents a histogram of the age of VC funds at the time of successful exits

separately for IPOs and trade sales. Exits appear to reach a peak when VC funds are around 6 or

7 years old. Notably, exits are relatively more likely to be via IPOs in VC funds’ early years and

less so as funds age. The decreasing likelihood of IPOs in funds’ later years is surprising in light

of the fact that it typically takes firms a considerably longer time to prepare for and execute an

IPO compared to a trade sale, and that prospective firms are generally expected to have reached a

certain level of maturity before becoming a publicly listed company.12

On the other hand, the

trend in Figure 2.1 is consistent with the negative influence of liquidity pressure on the

likelihood of IPOs. In the remainder of section 5, we undertake a thorough examination of the

relation between VC fund age and the exit method after controlling for other factors that might

affect the choice between an IPO and trade sale.

2.5.1. Baseline results in exit choice

Table 2.5 reports probit regressions of the exit method on fund age at exit and investment,

along with several proxies for portfolio firm quality, VC reputation, market conditions, and

industry and year fixed effects. Marginal effects with standardized coefficients reflecting a one

standard deviation change from the mean are reported along with robust standard errors clustered

at the portfolio firm level in parentheses. Since our primary variable of interests--VC fund age at

exit and the exit method--do not vary across multiple investments by the same VC fund in a

portfolio firm, we conduct the regressions at the portfolio firm–VC fund level by aggregating VC

12

After deciding to go public, prospective IPO firms prepare for the offering by appointing independent board

members, creating an audit committee, evaluating corporate governance practices, hiring investment bankers, a law

firm, accounting advisors, and an independent auditor, registering the offer with the SEC, preparing the IPO

prospectus, and marketing the company to investors in road shows (PWC, 2011). Boehmer and Ljungqvist (2004)

analyze the duration between the date firms announce their intention to go public and the IPO date for a sample of

German IPOs and find an average waiting time of more than two years. It is difficult to conduct a similar duration

analysis for U.S. IPOs since intentions to go public are not systematically announced and recorded.

60

funds’ multiple investments in each portfolio firm. As a result, our main sample includes 20,860

total observations belonging to 6,966 successful exits (2,010 IPOs and 4,956 trade sales), with

each portfolio company backed by three unique VC funds on average.

Column 1 reports that the likelihood of an IPO is positively related to the size of the

syndicate at the first VC financing round, the number of patents assigned to the portfolio firm,

the reputation of the VC firm as measured by its IPO capitalization share during the five years

prior to its initial investment in the portfolio firm (VC capitalization share), whether the VC firm

is headquartered in one of the three VC centers (VC center dummy), and recent market

conditions. The two control variables with the largest economic significance are number of

patents and recent IPO market conditions, with a one standard deviation change from the mean

causing a 8.3 and 11.5 percentage points increase in the likelihood of an IPO from a baseline

probability of 28.9%.

In column 2 we add the age of the VC fund at the time of the exit to the probit regression. We

find that a one standard deviation change in VC fund age at exit (from the mean of 6.96 to 9.65)

is associated with a 5.0 percentage points decline in the probability of an IPO, with a t-statistic of

-6.5. In column 3 we include an interaction between the fund age at exit and a dummy for lead

VC to explore whether the age of the VC funds with more influence over their portfolio firms

has a stronger relation to the exit choice. We find that the coefficient on the interaction term is

significantly negative, consistent with a larger impact of the liquidity pressure of the more

influential VCs.

In columns 4 and 5, we run the baseline specification from column 2 in two subsamples. In

column 4, we exclude early exits that occur when the VC fund is 4 years or younger, which are

disproportionately less likely to be trade sales and thus may not represent a realistic choice

61

between an IPO and trade sale.13

The marginal effect of the VC fund age at exit is -3.7

percentage points with a t-statistic of -4.3 after excluding early exits, indicating that the earlier

results from the full sample are not driven by a higher likelihood of IPOs in early years. In

column 5, we exclude trade sales with low (or missing) transaction values under the assumption

that these portfolio firms were less likely to have had a realistic IPO option.14

The marginal

effect of the VC fund age at exit is -3.6 percentage points with a t-statistic of -3.8 after excluding

trade sales with low or missing transaction values. The results from the subsample analyses

indicate that a higher likelihood of IPOs in early years or an excess of low quality trade sales in

later years does not drive the full sample results.

2.5.2. Identification

In this subsection we address the possibility that the negative relation between the age of the

VC fund at exit and the likelihood of an IPO documented in Table 5 might be spurious. The

primary concern is that portfolio firm quality might not be fully captured by the control variables

included in our tests. If omitted portfolio firm quality is correlated with the age of the VC fund at

exit, this could cause a spurious relation between fund age and exit choice. In particular, if

portfolio firms exited late are of lower quality, their propensity to be sold off instead of taken

public might be due to low portfolio firm quality rather than VC funds’ liquidity pressure.

We control for such potential endogeneity in VC fund age using three approaches. First, we

conduct two-stage least-squares regressions using lagged market conditions as the instrumental

variable. Second, we conduct a propensity score matching analysis to identify portfolio firms in

13

The fraction of observations that are IPOs is 62% in VC funds’ first year, 49% in their second year, and 39% in

their third and fourth years. 14

More specifically, we limit the sample to trade sales with a non-missing transaction value at least as large as the

market capitalization of a VC-backed IPO in the same industry during the same time period. We split the full sample

period to 1985-1992, 1993-1998, 1999-2000, and 2001-2012. This filter leaves 1,080 trade sales with 3,340 total

observations.

62

the treatment group (late exits) that are as similar as possible to the firms in the control group

(earlier exits) in terms of observable measures of quality. Finally, we investigate the relation

between portfolio firm quality and the age of the VC fund at exit directly to explore if later exits

are more likely to be via trade sales due to declining portfolio firm quality.

2.5.2.1 Instrumental variable approach

The primary motive behind the instrumental variable approach is to decompose VC fund age

into an exogeneous component uncorrelated with portfolio firm quality and an endogeneous

component potentially correlated with portfolio firm quality. To that end, we need an

exogeneous variable that is correlated with fund age at exit for reasons unrelated to the quality of

the firm being exited. Our strategy is to exploit past exit market conditions as a source of

exogeneous variation in VC liquidity considerations. For example, an abnormally cold IPO

market in the past is likely to cause a delay in exits for market-wide reasons unrelated to the

quality of a particular portfolio firm. In contrast, exit choice for late exits that follow favorable

market conditions is more likely to be dictated primarily by firm quality. Therefore, we use the

natural logarithm of the lagged number of IPOs in the industry during the two years ending three

months prior to the exit as our instrument.15

Table 2.6 presents the two-stage least squares results. Column 1 reports the first-stage OLS

regression with the age of the VC fund at exit as the dependent variable. The coefficient on the

instrumental variable is negative and statistically very significant with a t-statistic of -28.9,

indicating that the instrumental variable satisfies the inclusion restriction. As expected,

unfavorable IPO market conditions in the past are associated with later exits from portfolio

15

We exclude the number of IPOs in the most recent three-month period from the instrumental variable and instead

separately control for recent market conditions in the second-stage to ensure that the instrument does not pick up any

variation in market conditions correlated with firm quality through a short-term demand channel.

63

companies. Columns 2-4 report the second-stage probit regressions of exit method. The

dependent variable is a dummy that equals one for IPOs and zero for trade sales. Marginal

effects with standardized coefficients reflecting a one standard deviation change from the mean

are reported along with robust t-statistics clustered at the portfolio firm level. Column 2 adds the

predicted VC fund age at exit from the first-stage regression. The marginal effect of predicted

VC fund age at exit is -0.137 with a t-statistic of -5.2, indicating an economically very significant

13.7 percentage points drop in the likelihood of an IPO associated with a one standard deviation

increase in the age of the VC fund at exit.

In column 3, we include the residual from the first-stage regression to explore the relation

between exit method and the endogeneous component of VC fund age that is potentially

correlated with portfolio firm quality. The coefficient on the residual component is also

significantly negative with a t-statistic of -4.5. However, the economic significance is

considerably less than the predicted component with a one standard deviation increase from the

mean causing a -2.8 percentage points drop in the likelihood of an IPO. Finally, in column 4, we

interact both the predicted and residual components with an indicator for lead VCs. Notably, the

interaction with the predicted component is significantly negative with a t-statistic of -5.8,

indicating that lead VCs’ liquidity pressure associated with past market conditions has a larger

impact on exit choice. In contrast, the interaction with the residual component is statistically

insignificant.

2.5.2.2. Matched sample approach

In this section, we further explore the relation between VC funds’ age at exit and the method

of exit using a treatment effect method. The primary purpose of this approach is to ensure that

the treatment effect (the impact of a late exit on the likelihood of an IPO) is estimated by

64

comparing treated subjects (late exits) with control subjects (earlier exits) that are as similar as

possible across various observable characteristics considered important in explaining the

outcome (IPO vs. trade sale). This is achieved by estimating the counterfactual unobserved

outcomes of treated subjects using the observed outcomes from a subsample of similar subjects

from the control group. We use the propensity score matching method to construct the subsample

of control subjects. Roberts and Whited (2011) proposes propensity score matching as “a useful

robustness test for regression based analysis”. In particular, matching avoids the functional form

restrictions imposed by linear regressions.

Table 2.7 presents exit choice results using propensity score matching. The treated group

consists of the exits of VC funds that are nine years or older at the time of the exit. The control

group consists of the exits of VC funds that are eight years or younger. For each observation in

the treated group, we locate an observation from the control group with the closest propensity

score. Propensity scores are estimated using a probit regression of a dummy indicating a late exit

(VC fund age at exit >=9) on the age of the VC fund at 1st investment, the size of the initial

syndicate, the natural logarithm of the adjusted number of patents, VC capitalization share, VC

connectedness, and VC center dummy. In Panel A, the treatment effect is reported for the

unmatched and matched samples. The unmatched treatment effect of -0.052 indicates that late

exits are -5.2 percentage points less likely to be IPOs in the full sample. The matched treatment

effect of -0.106 indicates that late exits are -10.6 percentage points less likely to be IPOs

compared to matching early exits with the closest propensity scores. The more negative

treatment effect estimate from the matched sample indicates that the late exit group consists of

observations associated with a higher than average propensity to conduct an IPO if not for the

liquidity pressure. This is consistent with the results from propensity score matching that

65

assignment to the treated group is positively related to the size of the VC syndicate, the number

of patent assignments, and the VC’s capitalization share (untabulated), which are all significantly

positively related to the propensity of an IPO (see, e.g., column 1 in Table 2.5).

Panel B reports the results of a full-specification probit regression of exit choice using the

subsample of matched observations, and compares them to results from the full, unmatched

sample. Consistent with the results in Panel A, we find that the treatment effect is more negative

in the matched sample. The coefficient on Dummy (Age at exit>=9) has a marginal effect of -

0.084 vs. -0.072 in the unmatched sample. The results in Panel B confirm that the negative

impact of late exits on the likelihood of an IPO is larger in matched samples after controlling for

market conditions and including industry and year fixed effects in a regression framework.

2.5.2.3. VC age and portfolio firm quality

Finally, we directly examine the relation between portfolio firm quality and the age of the

VC fund at exit to investigate whether later exits are associated with a decline in portfolio firm

quality. First, we repeat the probit regressions of exit choice in Table 5 using only proxies for the

quality of the portfolio firm and its investors as independent regressors and excluding all other

variables associated with the VCs’ liquidity and market timing considerations. We posit that if

the decline in the likelihood of IPOs as VCs age is driven by early exits of higher quality

portfolio firms and the associated decline in the quality of the remaining firms in the portfolio,

then the predicted probability of an IPO based on observed measures of quality should also

decline with VC fund age.

Figure 2.2 plots the predicted probability of an IPO vs. a trade sale by VC fund age. We find

that the likelihood of an IPO increases slightly over the first five years from 30% to 35% and

66

remains roughly flat thereafter. In other words, there does not appear to be a decline in portfolio

firm quality with increasing VC fund age based on observable quality proxies.

In Figure 2.3, we conduct a closer examination of important characteristics of acquired

portfolio firms. According to the results in Table 2.5, the number of patents granted to VC-

backed companies is a statistically and economically important predictor of exit choice, and thus

is likely to be a useful proxy for firm quality. In Panel A of Figure 2.3, we investigate if the

patent intensity of VC-backed firms is negatively related to the age of their VC at the time of the

exit. Since the number of patents granted to a firm is likely to increase over time with firm age,

we control for this time effect by focusing on the number of patents granted per year of VC

backing. More specifically, we measure patent intensity as the number of patents granted to the

portfolio firm with an application date prior to the exit date as recorded by the U.S. Patent and

Trademark Office divided by the number of years between initial VC financing and the exit date.

Furthermore, we scale patent intensity with the average patent intensity of IPO firms in the same

VenturExpert ten-industry classification in order to account for industry effects. We find that the

patent intensity of acquired portfolio firms actually increase with VC fund age and reach a

maximum of 56% of the patent intensity of IPO firms in the same industry by the end of VC

funds’ life cycle.

In Panel B of Figure 2.3, we examine the observed valuations of a subsample of acquired

portfolio firms at the time of the exit event for which the transaction values are reported by the

SDC (available for approximately 45% of the trade sales, distributed sporadically over the

sample period). For each trade sale, we adjust the transaction value for inflation using the

Consumer Price Index and scale it by the average market capitalization of IPOs in the same

67

industry at the time of the offering during the same time period.16

We find that the valuation of

trade sales relative to IPOs declines at first with VC fund age reaching a minimum of 23% in

year 7, and starts to increase thereafter to 33% by year 12. The increase in valuations at the tail

end of funds is inconsistent with a decline in portfolio firm quality and is observed despite a

likely decline in the bargaining power of the acquired firms vis-à-vis the acquirers.

Altogether, the evidence from figures 2.2 and 2.3 is inconsistent with the notion that the

quality of portfolio firms declines with VC fund age. This presents further evidence against the

notion that the negative relation between fund age and IPO likelihood documented in tables 2.5,

2.6, and 2.7 is driven by an omitted variable bias caused by unobserved systematic variation in

portfolio firm quality.

2.6 Which funds succumb to liquidity pressure?

In this section, we investigate which VC funds are more likely to modify their exit strategy

due to liquidity considerations. We consider two proxies for VCs’ incentives to engage in such

liquidity management. First, we examine whether liquidity considerations are more important for

younger VC firms with limited track record. Gompers (1996) documents that young venture

capital firms take companies public earlier at less favorable terms, and attributes this to young

VC firms’ desire to establish a reputation quickly and raise capital for new funds even at the

expense of greater initial IPO underpricing.17

While Gompers (1996) focuses only on IPOs, it is

possible that the grandstanding incentive of young VC firms might influence the timing and

method of exits more generally.

16

We group exits by the following four time periods: 1985-1992, 1993-1998, 1999-2000, 2001-2012. 17

Lee and Wahal (2004) document a positive relation between IPO underpricing and young VC’s future fundraising

success, consistent with the idea that VCs that lack a track record benefit from grandstanding.

68

Second, we investigate if fund performance affects liquidity management. On the one hand,

aging funds that have not had successful exits might be more incentivized to accelerate their

exits in an effort to return capital to their investors and earn performance-based compensation

without further delay. On the other hand, the lower likelihood of an IPO associated with liquidity

pressure documented in section 5 might be considered less costly for VC funds that have already

had successful IPOs from the same portfolio.

Table 2.8 presents the results. The first two columns examine how fund sequence affects the

relation between fund age at investment and exit timing (column 1), and fund age at exit and exit

method (column 2). Column 1 repeats the OLS regression from column 2 of Table 2.3 after

including a dummy that equals one if the VC fund is the parent firm’s first fund as an interaction

with fund age at first-round investment and as a stand-alone regressor. The dependent variable is

the natural logarithm of the number of months between the first VC round of investment and the

exit event. We find that both the stand-alone regressor and the interaction term have statistically

insignificant coefficients, indicating that first funds are not more prone to accelerating their exits.

Column 2 repeats the probit regression from column 2 of Table 2.5 after including the first-fund

dummy. The dependent variable equals one for IPOs and zero for trade sales. Once again, both

the stand-alone regressor and the interaction term have statistically insignificant coefficients,

indicating that aging funds’ tendency to favor trade sales over IPOs is not related to fund

sequence.

In columns 3 and 4, we repeat the exit timing and exit method analyses from the first two

columns using the number of prior IPOs in the fund as the incentive proxy. We find that the

negative relation between fund age at first round and time until exit is greater for funds with a

larger number of IPOs prior to the exit. It appears that portfolio firms of aging VC funds are

69

exited more quickly if the VC fund has already established a track record of IPOs. In column 4,

we find evidence of intra-portfolio performance persistence: the coefficient on the number of

prior IPOs is positive and statistically highly significant. However, despite performance

persistence, the likelihood of an IPO declines more with fund age for funds with a greater

number of prior IPOs.

Altogether, the evidence in Table 2.8 suggests that liquidity considerations have a greater

influence on exit strategies of VC funds that have exhibited better performance. We interpret this

as evidence that the costs of liquidity management (e.g., exiting companies earlier and via trade

sales instead of IPOs) are lower for VC funds that have already established a track record of

IPOs from the portfolio. In contrast, we do not find any evidence that first time funds are any

more likely to engage in liquidity management compared to more experienced VC funds. We

conclude that the influence of liquidity considerations on the VC exit cycle is distinct from the

grandstanding behavior documented by Gompers (1996).

2.7 Liquidity pressure at IPO lock-up expirations

Field and Hanka (2001) report that the lockup expiration phenomenon—a permanent decline

in stock prices and abnormally high trading volume around the IPO lock-up expiration—is

stronger for newly public firms with venture capital backing and attribute this phenomenon to a

particularly large amount of selling by VCs following the expiration. If VC funds’ limited

lifespan causes liquidity pressure, we expect older funds approaching their liquidation date to be

more likely to sell shares at the lock-up expiration. Specifically, we investigate whether the age

of the VC fund at the time of expiration is positively related to trading volume and negatively

70

related to stock returns around lock-up expirations of their portfolio firms that recently went

public.

We measure abnormal trading volume (AVOL) relative to each firm’s mean daily trading

volume during the 45 trading days ending six trading days prior to lock-up expiration:

where is the average daily trading volume for firm i surrounding the lock-up expiration

window beginning at day 0 and ending at day +5. Following Field and Hanka (2001), we

compute cumulative abnormal returns surrounding lock-up expirations as follows:

where is the cumulative abnormal return of firm i,

is the daily stock return on day t

relative to the expiration date, and is the CRSP equal-weighted market index return. If the

lock-up expiration falls on a non-trading day we take the next trading day as the date of

expiration. The estimation window for CAR begins at day -5 and ends at day +1, capturing price

changes both in anticipation of future insider sales as well as simultaneously with actual sales

upon expiration.

We use two proxies to capture VC funds’ liquidity pressure: i) the age of the oldest fund at

the time of the lock-up expiration, and ii) the number of independent VC firms that are nine

years or older at the time of the expiration. The second proxy is motivated by the idea that

multiple VC funds facing liquidity pressure is likely to cause a more pronounced effect around

,

6

,50

11

45

i T

i tt

VAVOL

V

 1,

5 ,

11

1

i t

i

t m t

RCAR

R

iCAR ,i tR

71

lock-up expirations. Control variables include the percentage of the firm’s shares that were

locked up prior to the expiration, the cumulative abnormal stock returns during the 45 trading

days ending six days prior to the expiration, a dummy that equals one if pre-IPO shareholders

sold any shares at the IPO, the natural logarithm of IPO proceeds, and year fixed effects. Robust

t-statistics clustered at the industry level are reported in parentheses.

The first three columns in Table 2.9 present regression results for the average abnormal

volume observed around lock-up expirations. In column 1, the coefficient on the age of the

oldest fund at the time of lock-up expiration is positive and statistically highly significant with a

t-statistic of 3.5, revealing evidence consistent with pronounced selling around lock-up

expirations by VC funds that are closer to liquidation. We also find a larger abnormal volume for

firms with a larger fraction of shares released at the expiration and smaller abnormal volume

following larger IPOs. Column 2 adds the number of independent VC firms that own shares of

the IPO firm. The coefficent on the number of independent VCs is positive and statistically

highly significant with a t-statistic of 2.5, suggesting that a larger number of VCs is associated

with more selling at the lock-up expiration. Next, we split independent VCs into two groups by

their age at expiration, and include their numbers separatly in column 3. We classify VCs that are

9 years or older at the time of the expiration as under liquidity pressure. We find that the number

of VCs under liquidity pressure is significantly positively related to abnormal volume with a t-

statistic of 2.3, whereas the number of VCs that are not yet under liquidity pressure is only

marginally significantly positive with a t-statistic of 1.7. The results in column 3 indicate that the

positive relation between the number of VCs and abnormal volume documented in column 2 is

driven primarily by older VCs under liquidity pressure.

72

In columns 4 through 6, we investigate the relation between VCs’ liquidity pressure and

abnormal stock returns around lock-up expirations. In column 4, the coefficient on the age of the

oldest VC fund at the time of the expiration is negative and statistically significant with a t-

statistic of -2.2, providing evidence that the increased trading volume documented in column 1 is

associated with a significant decline in stock prices around lock-up expirations. Column 5

includes the number of independent VC firms, which turns out to be significantly negatively

related to abnormal stock returns. Finally, column 6 splits the number of independent VCs into

two groups by liquidity pressure. We find that the number of VCs that are nine years or older at

the time of the lock-up expiration is significantly negatively related to abnorman returns with a t-

statistic of -4.3, whereas the number of VCs that are not under liquidity pressure does not have a

statistically significant coefficient. The coefficient of -0.004 on the number of VCs that are nine

years or older indicates that each additional VC firm under liquidity pressure is associated with a

40 basis points decline in stock returns around lock-up expirations. A one standard deviation

increase in the number of VCs under liquidity pressure (from a mean of 2 to 4.9) is associated

with a 1.16 percentage points decline in stock returns, which is economically significant

compared to an unconditional average CAR of 4.25% for all VC-backed IPOs in our sample.

Overall, the results from abnormal trading volume and abnormal stock returns analyses are

consistent with each other and provide evidence consistent with the VC liquidity pressure

hypothesis.

2.8 Conclusion

In this paper, we investigate whether independent venture capital funds’ limited lifespan

imposes a constraint on the general partners by subjecting the fund to liquidity pressure at the

tail-end of the funds’ lifecycle.

73

We find that portfolio firms backed by independent VC funds approaching maturity are

associated with quicker initial public offerings and selloffs, consistent with the idea that the

venture capital exit cycle is influenced by liquidity pressure faced by older funds. These portfolio

firms are also more likely to have a liquidity event during unfavorable market conditions and are

more likely to be sold off rather than taken public. These findings raise a concern that the

liquidity pressure faced by VC funds might lead to suboptimal exit outcomes. Turning our

attention to initial public offerings of VC-backed firms, we find that IPO firms backed by VCs

under liquidity pressure experience significantly larger trading volume and lower stock returns

around their lockup expirations, and this lockup effect increases with the number of independent

VC funds under liquidity pressure.

Our results suggest that VC funds’ liquidity constraints impose externalities and

influence the IPO process. Our evidence is consistent with the presence of agency conflicts

between venture capitalists and their portfolio firms, as several key exit-related choices appear to

be made in the VCs’ self interest. In addition, our finding that the significant stock price decline

observed around lockup expirations is related to the VCs’ liquidity pressure supports the view

that this enduring market anomaly is caused by downward sloping demand curves.

74

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76

Figure 2.1. Histogram of exits by VC age categorized by exit method

This chart depicts the frequency of VC-backed exits by the age of the VC fund at the time of the exit,

categorized by the method of exit. The sample includes VC investments by independent VC firms in

companies with successful exits (IPO or trade sale) between 1985 and 2012. Fund age at exit is the age (in

years) of the VC fund at the time of the exit.

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

1 2 3 4 5 6 7 8 9 10 11 12

Fre

qu

en

cy

VC Fund Age at Exit

Series1 Series2

77

Figure 2.2 Predicted probability of IPO based on observable quality proxies

This chart depicts the predicted probability of IPO from a probit regression of exit method (IPO vs. trade sale) on

portfolio firm and VC firm characteristics only. The independent regressors include the natural logarithm of the

adjusted number of patents, the size of the initial VC syndicate, industry dummies, VC capitalization share, VC

connectedness, and VC center dummy. The variables are described in Table 2.1.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

1 2 3 4 5 6 7 8 9 10 11 12

Pre

dic

ted

pro

bab

ility

of

IPO

VC age at exit

78

Figure 2.3. Acquired firm characteristics by VC age at exit

Panel A depicts the patent intensity of acquired VC-backed start-ups scaled by the average patent intensity

of VC-backed IPOs in the same industry. Patent intensity is the number of patents granted to a portfolio

firm with an application date prior to the exit date as recorded by the U.S. Patent and Trademark Office

divided by the number of years between initial VC financing and the exit date. The scaled patent intensity

is winsorized at the 1% on both tails. Panel B depicts the transaction value of VC-backed trade sales scaled

by the average market capitalization of VC-backed IPOs in the same industry as priced at the offering and

during the same time period. We group exits by the following time periods: 1985-1992, 1993-1998, 1999-

2000, 2001-2012. Both transaction values and IPO market capitalizations are inflation-adjusted for 2012

using the CPI. The scaled transaction value is winsorized at the 1% level on both tails. Exits that occur

during the first year of VC funds are omitted due to lack of valid observations.

Panel A: Patent intensity

Panel B: Scaled transaction value of trade sales

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

2 3 4 5 6 7 8 9 10 11 12

Scal

ed t

ran

sact

ion

val

ue

VC age at exit

0

0.1

0.2

0.3

0.4

0.5

0.6

2 3 4 5 6 7 8 9 10 11 12

Scal

ed

pat

en

t in

ten

sity

VC age at exit

79

Table 2.1

Summary statistics This table presents descriptive statistics for VC investments in companies with a successful exit via an IPO or trade sale between

1985 and 2012. Investments by unidentified VC funds, by subsidiary VC firms, by VC funds older than 10 years, and investments

made after the exits are excluded from the sample. In Panel A, the unit of observation is the individual VC investment. In Panel B,

the unit of observation is the individual portfolio company - VC fund pair. In Panel C, the unit of observation is the individual

portfolio company. Lead VC is the firm that participated in the first venture capital round and made the largest total investment in the

company across all rounds prior to the exit. VC capitalization share is the cumulative market capitalization at IPO of the companies

taken public by the VC firm, scaled by the market capitalization of all VC-backed IPOs during the five years prior to the VC's first

investment in the portfolio company. VC connectedness is the number of unique VCs each VC has syndicated with during the

previous 5 years, divided by the total number of possible pairings. VC center dummy equals one if the VC firm is headquarterd in the

San Francisco, Boston, or New York metropolition areas. # of patents is the number of patents granted to a portfolio firm with an

application date between the first VC investment and exit dates as recorded by the US Patent and Trademark Office. # of IPOs in

prior quarter is the number of completed IPOs in the same industry during the three months prior to the exit. Lagged # of IPOs (qtrs -

2:-9) is the number of completed IPOs during the previous two year period ending three months prior to the exit. Market returns in

prior quarter is the equally-weighted stock returns of public firms in the high-tech industries (three-digit SIC codes of 283, 481, 365-

369, 482-489, 357, and 737) during the three months prior to the exit.

Min 25% Median Mean 75% Max N

Panel A: Observations at VC investment level

Fund age at investment (years)

1.00

3.00

4.00

4.34

6.00

10.00

50,217

Time until exit (months)

0.00

20.00

39.00

46.68

64.00

303.00

50,217

First VC round dummy

0.00

0.00

0.00

0.19

0.00

1.00

50,217

Syndicate size

1.00

2.00

4.00

4.78

6.00

28.00

50,217

# of VC funds in round

1.00

2.00

3.00

3.14

4.00

16.00

50,217

80

Panel B: Observations at portfolio firm - VC fund level

Fund age at exit (years)

1.00

5.00

7.00

6.96

9.00

12.00

20,860

Lead VC dummy

0.00

0.00

0.00

0.34

1.00

1.00

20,860

VC capitalization share %

0.00

0.00

0.50

1.19

1.67

8.37

20,860

VC connectedness %

0.00

1.03

3.48

5.12

7.49

22.55

20,860

VC center dummy

0.00

0.00

1.00

0.67

1.00

1.00

20,860

Panel C: Observations at portfolio firm level

IPO dummy

0.00

0.00

0.00

0.29

1.00

1.00

6,966

# of VC rounds

1.00

1.00

3.00

3.42

5.00

23.00

6,966

# of patents

0.00

0.00

0.00

3.83

3.00

67.00

6,966

# of IPOs in prior quarter

0.00

1.00

2.00

4.24

5.00

50.00

6,966

Market returns prior quarter %

-42.18

-5.15

4.41

5.36

13.65

89.52

6,966

Lagged # of IPOs (qtrs -2:-9)

0.00

11.00

18.00

28.79

33.00

191.00

6,966

81

Table 2.2

Number of months between investment and exit, by fund Age at investment This table presents summary statistics for the number of months between VC investments and the exit

event, categorized by the age in years of the VC fund at the time of the investment. The sample includes

all VC investments by independent VC firms in companies with successful exits (IPO or trade sale)

between 1985 and 2012. Investments by unidentified VC funds, by subsidiary VC firms, and investments

made after the exit event are excluded from the sample. The unit of observation is the individual VC

investment. The last row reports summary statistics for the difference between the oldest and the

youngest age groups along with p-values from significance tests of equality. p-values are estimated using

the t-statistic for the means, Wilcoxon rank-sum test for the medians, and bootstrap confidence intervals

from 5,000 replications with replacement for the 25th and 75th percentile breakpoints.

Round of investment: All Only First Round

Fund Age at Investment

Mean Median 25% Mean Median 75%

1

54.9 47

32 61.2 55 84

2

52.9 45

32 60.0 53 81

3

49.7 42

33 58.9 52 80

4

47.2 40

31 57.6 51 77

5

43.5 36

30 54.1 48 72

6

41.4 34

7

39.8 33

8

36.7 31

9

37.4 30

10

37.4 29

Old - Young

-17.5 -18

-2 -7.1 -7 -12

[p-value]

[0.000] [0.000]

[0.166] [0.000] [0.000] [0.000]

82

Table 2.3

OLS analysis of time between VC investment and exit This table presents OLS regressions of the number of months between a VC investment and

the exit event (IPO or trade sale). The sample includes VC investments by independent VC

firms in companies with successful exits (IPO or trade sale) between 1985 and 2012. The

unit of observation is individual VC investments. Column 1 includes all VC investments

whereas columns 2 and 3 presents regression results for the subsample of first round VC

investments. Fund age at investment is the age (in years) of the VC fund at the time of the

investment. Lead VC Dummy equals one for the VC fund with the largest dollar amount of

aggregate investment in the portfolio firm across all rounds, and zero otherwise. IPO dummy

equals one if the exit is via an IPO, and zero if via a trade sale. Syndicate size is the number

of distinct venture capital funds participating in the financing round. Ln(Adjusted # of

patents) is the natural logarithm of the number of patents assigned to the portfolio firm prior

to its exit, adjusted for year and industry effects as well as the number of years of VC-

backing prior to the exit. The remaining regressors are described in Table 2.1. Industry

(VentureXpert ten-industries classification) fixed effects are included. Robust t-statistics,

clustered at the VC financing round level are in parentheses.

Stage of investment: All First VC Round Only

Column: (1) (2) (3)

Fund age at investment -0.066

-0.026

-0.015

(-30.1)

(-3.9)

(-1.9)

Fund age x Lead VC

-0.020

(-3.1)

IPO dummy -0.145

-0.114

-0.116

(-9.1)

(-4.2)

(-4.2)

Syndicate size 0.008

0.012

0.007

(2.9)

(1.5)

(0.8)

Ln(Adjusted # of patents) 0.016

0.024

0.025

(2.5)

(2.1)

(2.1)

VC capitalization share 3.461

1.760

1.860

(8.9)

(2.5)

(2.6)

VC connectedness -0.150

0.304

0.303

(-1.1)

(1.3)

(1.3)

VC center dummy -0.048

-0.062

-0.062

(-4.7)

(-3.2)

(-3.1)

Ln(# of IPOs in prior quarter) -0.073

-0.086

-0.086

(-8.4)

(-6.2)

(-6.2)

Market returns in prior quarter 0.000

-0.058

-0.060

(0.0)

(-0.9)

(-1.0)

Industry FE Yes

Yes

Yes

Adjusted R2 5.9%

4.5%

4.6%

N 50,217 8,585 8,585

83

Table 2.4

Probit analysis of exit market conditions This table presents probit regressions of exit market conditions. The dependent variable is a dummy that

equals one if the number of IPOs during the prior three months were below the median for all exits

during the sample period. Marginal effects with standardized coefficients reflecting a one standard

deviation change from the mean are reported. The sample includes VC investments by independent VC

firms in companies with successful exits (IPO or trade sale) between 1985 and 2012. The unit of

observation is individual VC investments. Fund age at exit is the age (in years) of the VC fund at the

time of the exit. Fund age at investment is the age of the VC fund at the time of the investment.

Ln(Adjusted # of patents) is the natural logarithm of the number of patents assigned to the portfolio

firm prior to its exit, adjusted for year and industry effects as well as the number of years of VC-

backing prior to the exit. The remaining regressors are described in Table 2.1. Industry (VentureXpert

ten-industries classification) fixed effects are included. Robust t-statistics, clustered at the VC financing

round level are in parentheses.

Dependent variable: Pr(Cold Market)

Column: (1) (2)

Fund age at exit

0.023

(2.4)

Fund age at investment 0.024

0.012

(5.4)

(2.1)

Syndicate size -0.016

-0.016

(-2.0)

(-2.0)

Ln(Adjusted # of patents) 0.010

0.010

(1.1)

(1.1)

VC capitalization share 0.050

0.048

(5.7)

(5.5)

VC connectedness -0.152

-0.151

(-16.1)

(-16.1)

VC center dummy 0.019

0.020

(1.6)

(1.6)

Industry FE Yes

Yes

Adjusted R2 4.9%

5.0%

N 50,217 50,217

84

Table 2.5

Exit choice - Probit analysis This table presents probit regressions of the exit method. The dependent variable is a dummy that equals one if

the exit is via an IPO, and zero if a trade sale. Marginal effects with standardized coefficients reflecting a one

standard deviation change from the mean are reported. The sample includes VC investments by independent

VC firms in companies with successful exits (IPO or trade sale) between 1985 and 2012. Fund age at exit is the

age (in years) of the VC fund at the time of the exit. Fund age at 1st investment is the age of the VC fund at the

time of its first investment in the portfolio firm. Initial syndicate size is the number of VCs that participated in

the first VC round raised by the portfolio firm. Ln(Adjusted # of patents) is the natural logarithm of the number

of patents assigned to the portfolio firm prior to its exit, adjusted for year and industry effects and the number

of years of VC-backing prior to the exit. The remaining regressors are described in Table 2.1. Columns 1-3

include the full sample of observations the portfolio firm-VC fund level. Column 4 excludes observations with

VC funds that are 4 years are younger at the time of the exit. Column 5 excludes trade sales with low or

missing transaction values as described in footnote 13. Industry (VentureXpert ten-industries classification) and

year (first VC financing round) fixed effects are included. Robust t-statistics, clustered at the portfolio firm

level are in parentheses.

Sample: All

Age ≥ 5

High

Value

(1)

(2)

(3)

(4)

(5)

Fund age at exit

-0.050

-0.046

-0.037

-0.036

(-6.5)

(-5.6)

(-4.3)

(-3.8)

Fund age at exit x Lead VC

-0.012

(-2.7)

Fund age at 1st investment 0.003

0.028

0.027

0.026

0.022

(0.7)

(4.9)

(4.6)

(4.6)

(3.1)

Initial syndicate size 0.021

0.021

0.020

0.019

0.016

(2.3)

(2.3)

(2.2)

(2.0)

(1.5)

Ln(Adjusted # of patents) 0.083

0.084

0.084

0.076

0.066

(9.9)

(10.0)

(10.0)

(8.8)

(6.8)

VC capitalization share 0.032

0.032

0.033

0.031

0.037

(4.6)

(4.7)

(4.9)

(4.4)

(4.2)

VC connectedness -0.011

-0.011

-0.011

-0.012

-0.029

(-1.6)

(-1.5)

(-1.6)

(-1.6)

(-3.5)

VC center dummy 0.032

0.031

0.031

0.033

0.006

(3.1)

(3.0)

(3.0)

(2.9)

(0.5)

Ln(# of IPOs prior quarter) 0.115

0.110

0.110

0.102

0.051

(13.0)

(12.4)

(12.4)

(10.4)

(4.4)

Market returns prior quarter 0.046

0.046

0.046

0.052

0.034

(6.2)

(6.3)

(6.2)

(6.3)

(3.5)

Industry and Year FE Yes

Yes

Yes

Yes

Yes

Adjusted R2 24.1%

24.7%

24.7%

24.0%

14.7%

N 20,860

20,860

20,860

16,600

10,506

85

Table 2.6

Exit choice - 2SLS analysis This table presents the 2SLS regressions for exit choice. Column 1 reports the first-stage OLS

regression results. The dependent variable in column 1 is the age of the VC fund at the time of the exit.

The instrumental variable (IV) is the natural logarithm of the lagged number of IPOs in the industry

during the two years ending three months prior to the exit. Columns 2-4 report the second-stage probit

regression results. The dependent variable is a dummy that equals one if the exit is via an IPO, and zero

if a trade sale. Marginal effects with standardized coefficients reflecting a one standard deviation

change from the mean are reported. The sample includes VC investments by independent VC firms in

companies with successful exits (IPO or trade sale) between 1985 and 2012. Pred. VC age at exit is the

predicted and Res. VC age at exit is the residual value from the first-stage regression in column 1. The

remaining regressors are the same as in Table 2.5. Robust t-statistics, clustered at the portfolio firm

level are in parentheses.

Dependent variable:

Age at

Exit Pr (IPO)

Column: (1) (2) (3) (4)

Pred. VC age at exit

-0.137

-0.140

-0.137

(-5.2)

(-5.4)

(-5.3)

Pred. VC age at exit x Lead VC

-0.025

(-5.8)

Res. VC age at exit

-0.028

-0.030

(-4.5)

(-3.9)

Res. VC age at exit x Lead VC

0.004

(0.9)

Ln(Lagged # of IPOs) (IV) -0.177

(-28.9)

Fund age at first investment 0.498

0.128

0.132

0.131

(85.1)

(5.2)

(5.4)

(5.3)

Initial syndicate size 0.022

0.038

0.038

0.035

(3.7)

(4.0)

(4.0)

(3.6)

Ln(Adjusted # of patents) 0.007

0.077

0.077

0.076

(1.3)

(9.5)

(9.6)

(9.5)

VC capitalization share 0.024

0.042

0.042

0.044

(3.0)

(5.6)

(5.6)

(5.9)

VC connectedness 0.014

0.034

0.034

0.034

(1.7)

(4.4)

(4.5)

(4.4)

VC center dummy -0.051

-0.002

-0.002

-0.002

(-3.9)

(-0.2)

(-0.2)

(-0.2)

Ln(# of IPOs prior quarter)

0.135

0.133

0.133

(15.1)

(14.9)

(14.9)

Market returns prior quarter

0.051

0.051

0.051

(6.7)

(6.7)

(6.6)

Industry FE Yes

Yes

Yes

Yes

Adjusted R2 29.8%

18.4%

18.7%

18.8%

N 20,860 20,860 20,860 20,860

86

Table 2.7

Exit choice - Propensity score matching This table presents treatment effects models of exit choice. The sample includes VC investments by

independent VC firms in companies with successful exits (IPO or trade sale) between 1985 and 2012. Panel A

reports average treatment effect on the unmatched and propensity-score matched samples. The estimates reflect

the difference in the odds of an IPO for portfolio firms with a late exit (VC fund age at exit > 8) compared to

all or matched portfolio firms with an earlier exit (VC fund age at exit <=8). The covariates used in matching

are the age of the VC fund at 1st investment, the size of the initial VC syndicate, natural logarithm of the

adjusted number of patents, VC capitalization share, VC connectedness, and VC center dummy. Panel B

reports probit regressions of exit choice using the full sample vs. the subsample of propensity-score matched

observations. The dependent variable is a dummy that equals one if the exit is via an IPO, and zero if a trade

sale. Marginal effects with standardized coefficients reflecting a one standard deviation change from the mean

are reported. Dummy (Age at exit>=9) is a dummy that equals one if the VC fund is 9 years or older at the time

of the exit, and zero otherwise. Robust t-statistics, clustered at the portfolio firm level are in parentheses.

Panel A: Average Treatment Effect

Sample: Unmatched Propensity Score Matched

Treatment Effect

-0.052

-0.106

(-7.2)

(-4.4)

Panel B: Matched Sample Regression

Sample: Unmatched Propensity Score Matched

Dummy (Age at exit>=9) -0.072

-0.084

(-5.4)

(-5.0)

Fund age at 1st investment 0.016

0.034

(3.4)

(4.0)

Initial syndicate size 0.022

0.018

(2.4)

(1.7)

Ln(Adjusted # of patents) 0.084

0.076

(10.0)

(7.7)

VC capitalization share 0.032

0.054

(4.7)

(4.3)

VC connectedness -0.011

-0.027

(-1.6)

(-2.1)

VC center dummy 0.031

0.022

(3.0)

(1.2)

Ln(# of IPOs prior quarter) 0.114

0.126

(12.8)

(10.9)

Market returns prior quarter 0.046

0.058

(6.2)

(5.9)

Industry and Year FE Yes

Yes

Adjusted R2 24.4%

27.9%

N 20,860 12,532

87

Table 2.8

Liquidity Pressure and Fund Incentives This table examines how two incentive proxies affect the relation between fund age and exit timing

(columns 1 and 3) and exit method (columns 2 and 4). The two incentive proxies are i) a dummy that

equals one if the VC fund is the parent VC firm's first fund, and ii) the number of prior IPOs by other

portfolio firms in the VC fund. Columns 1 and 3 repeat the exit timing OLS regression from column 2

of Table 3 with the addition of the two incentive proxies as interactions with the VC fund age at the

time of the investment and as stand-alone regressors. The dependent variable is the natural logarithm

of the number of months between the first VC round and the exit event. The sample includes first-

round VC investments by independent VC firms in companies with successful exits (IPO or trade sale)

between 1985 and 2012. Columns 2 and 4 repeat the exit method probit regression of column 2 of

Table 2.5 with the addition of the two incentive proxies as interactions with the VC fund age at the

time of the exit and as stand-alone regressors. The sample includes VC investments by independent

VC firms in companies with successful exits (IPO or trade sale) between 1985 and 2012. Robust t-

statistics are reported in parentheses.

Incentive proxy:

Dummy(First Fund) = 1 # of prior IPOs in fund

Dependent variable:

Ln(Months

until Exit) Pr(IPO)

Ln(Months

until Exit) Pr(IPO)

Column: (1) (2) (3) (4)

Fund Age at Investment

-0.029

0.029

-0.031

0.028

(-3.8)

(5.0)

(-3.6)

(5.0)

Fund Age at Investment x

0.011

-0.006

Incentive

Proxy

(0.8)

(-5.9)

Fund Age at Exit

-0.051

-0.028

(-6.4)

(-5.5)

Fund Age at Exit x

0.006

-0.027

Incentive

Proxy

(0.6)

(-2.0)

Incentive Proxy

-0.006

-0.026

0.076

0.028

(-0.1)

(-1.1)

(21.5)

(2.2)

Other Controls

Yes

Yes

Yes

Yes

88

Table 2.9

Liquidity pressure at IPO lockup expiration

This table reports OLS regressions of abnormal trading volume and stock returns around lock-up expirations on

several proxies for VC liquidity pressure. The sample includes VC-backed IPOs with non-missing lock-up dates

between 1985 and 2012. In columns 1-3, the dependent variable is the average daily trading volume between days

0 and +5 around the lock-up expiration scaled by the average daily trading volume between days -50 and -6. In

columns 4-6, the dependent variable is the cumulative market-adjusted return between the days -5 and +1 around

the lock-up expiration. Robust t-statistics clustered at the industry level are reported in parentheses.

Dependent variable: Abnormal Trading Volume CAR

Column: (1) (2) (3) (4) (5) (6)

Max. VC fund age at expiration

0.081

-0.003

(3.5)

(-2.2)

# of independent VC firms

0.166

-0.004

(2.5)

(-3.2)

# of independent VC firms w/

age>=9

0.175

-0.004

(2.3)

(-4.3)

# of independent VC firms w/

age<9

0.082

-0.002

(1.7)

(-0.8)

Percentage of Shares Locked

2.923

2.436

2.514

-0.095

-0.083

-0.084

(10.7)

(7.7)

(7.7)

(-2.9)

(-2.4)

(-2.4)

CAR(-50,-6)

0.072

0.084

0.073

-0.045

-0.045

-0.045

(0.5)

(0.6)

(0.5)

(-3.3)

(-3.3)

(-3.2)

Secondary Selling Dummy

-

0.170

-

0.030

-

0.099

0.004

-0.001

0.001

(-1.4)

(-0.3)

(-0.9)

(0.8)

(-0.0)

(0.3)

Ln (IPO Proceeds)

-

0.180

-

0.209

-

0.190

0.003

0.004

0.004

(-2.1)

(-2.2)

(-2.1)

(0.8)

(1.2)

(1.1)

Intercept

-

1.770

-

1.364

-

1.291

0.070

0.052

0.051

(-3.9)

(-3.0)

(-3.1)

(1.7)

(1.5)

(1.5)

Time Controls

Yes

Yes

Yes

Yes

Yes

Yes

Adjusted R2

5.2%

5.7%

6.1%

6.0%

5.8%

5.9%

N 1,382 1,382 1,382 1,382 1,382 1,382

89

Figure A.1: VC investment by Washington State Investment Board between December 2002 and December

2012

23.8%

4.2% 0.5%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Cap

ital

Cal

led

an

d V

alu

e E

xite

d

VC Fund Age

Value remaining in portfolio Capital called