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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.
iii
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
iv
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.
v
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.
vi
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
vii
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
viii
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
ix
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.
2
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.
6
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?
11
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
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Cooper, M. J., R. C. Gutierrez, and A. Hameed. "Market States and Momentum." The Journal of
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Daniel, K., and T. J. Moskowitz. 2012. “Momentum crashes.” Working paper, SSRN eLibrary.
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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
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George, T. J., and C. Y. HWANG. "The 52‐Week High and Momentum Investing." The Journal
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Hwang, S., and A. Rubesam. "The Disappearance of Momentum." SSRN eLibrary (2008).
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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
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McLean, R. D., and J. Pontiff. "Does Academic Research Destroy Stock Return Predictability?"
In AFFI/EUROFIDAI, Paris December 2012 Finance Meetings Paper (2013).
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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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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