Venture Capital: Performance, Persistence,...
Transcript of Venture Capital: Performance, Persistence,...
Electronic copy available at: http://ssrn.com/abstract=1432858
Venture Capital: Performance, Persistence, and Reputation
Richard Smith A. Gary Anderson Graduate School of Management
University of California, Riverside
Roberto Pedace
Department of Economics Scripps College
Vijay Sathe
Drucker Graduate School of Management Claremont Graduate University
This Draft: July 11, 2009
Abstract
By combining data from two sources, we are able to examine how venture capital fund performance, measured as fund IRR or cash‐on‐cash return, is related to fund outcomes (IPO and acquisition percentages) and abandonment option exercise practices after the fund’s initial investment in a venture. We also are able to relate fund performance to the track record of the venture capital firm, its experience, agility, reputation, and investment and exit style. Our primary findings include: (1) M&A success is almost as important as IPO success in explaining fund performance; (2) funds with aggressive exercise of abandonment options after the first investment tend to outperform those that continue to support a large percentage of their initial investments; (3) prior performance of the firm, in terms of success percentages and abandonment practices, is strongly related to fund performance; (4) firm experience in the same industry sector is positively related to fund performance, but there is some evidence that agility, as reflected by the firm’s ability to move to a new sector, is also valuable; (5) separate from experience, generic firm reputation also is positively related to performance; (6) and style, in terms of the mix of exit percentages and abandonment practices, persists over funds of the same firm. These results are robust to controlling for selection bias of the reporting entities, as well as biases related to looking‐back at initial fund performance, survivorship, and attrition. Quantile regression estimates establish that our results hold across the full range of realized performance levels, though we do find evidence that top performing funds offered by highly reputable venture capital firms behave differently.
Keywords: venture capital, persistence, reputation, IPO, acquisition
JEL Codes: G14, G24, G32, G34
Corresponding Author: Richard Smith A. Gary Anderson Graduate School of Management University of California Riverside 900 University Avenue Riverside, CA 92521 [email protected] 951‐827‐3554
Electronic copy available at: http://ssrn.com/abstract=1432858
Venture Capital: Performance, Persistence, and Reputation I. Introduction
We combine data from two sources to study how venture capital fund (“VC fund” or “fund”)
performance is related to investment outcomes, such as the fund’s IPO percentage, and how fund
performance is related to the track record, experience, and reputation of the venture capital firm (“VC
firm” or “firm”). Contrary to conventional wisdom, we find that M&A success is almost as important as
IPO success in explaining fund performance. We also find that funds which aggressively exercise
abandonment options after the first investment tend to outperform those that continue to support a
large portion of their initial investments. The firm’s track record of outcomes, in terms of success
percentages and abandonment practices, is strongly related to fund performance. The firm’s experience
in the industry sector of the fund is also positively related to fund performance, but there also is
evidence that agility, as reflected by the firm’s ability to move to a new sector with its next fund, is
valuable. Separate from a firm’s specific experience, its generic reputation also is positively related to
performance, especially for the extreme upper tail of funds. By examining style persistence, in terms of
the mix of exit percentages and abandonment practices, we find evidence that persistent skill of the VC
firm is a contributor to fund performance. This does not preclude the notion that that a firm’s initial
success may be due to luck, but it does indicate that persistent performance of the firm is derived at
least partly from acquired skill.
Among practitioners and in academic research, VC fund success is perceived to be largely a
result of “home runs.” The funds that produce the highest returns for their investors are believed to be
those whose portfolios include a few high‐valued IPO exits. Consistent with the view that fund success
is predominantly due to IPO exits, in academic studies, IPO success often is used as an indicator of VC
firm reputation. Nahata (2008), for example, defines VC firm “reputation” based on the aggregate
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market capitalization of successful IPO exits, and Ivanov, Krishnan, Masulis, and Singh (2008) define VC
firm reputation as the firm’s share of VC‐backed IPOs.
Although the realized returns to investors in a VC fund assuredly are related to the fund’s IPO
successes, the narrow focus on IPOs provides little insight into why returns to limited partners persist
over funds of the same VC firm. For example, both of the above mentioned measures of VC firm
reputation do well in predicting the IPO exit percentages of subsequent funds, but neither takes direct
account of the VC firm’s potential contribution to value creation, such as initial selection of portfolio
companies and abandonment choices, and neither reputation measure has been tested on more
comprehensive measures of performance.
There is compelling evidence that performance, measured in various ways, persists over
different funds of the same VC firm. Kaplan and Schoar (2005), in the most comprehensive study of
fund‐level performance to date, find that VC firm fixed effects are a statistically important determinant
of fund‐level performance, where performance is the fund return indexed by the contemporaneous S&P
500 return. They also find economically significant elasticities between fund performance and the
performance of each of the firm’s prior two funds.
While persistence could arise from the superior ability of some VC firms, it also could be simply
the result of herding by investors and entrepreneurs, both of which groups may seek to affiliate with VC
firms who have track records of success (even if the initial success may just be a result of luck).
In this paper, we seek a better understanding of the factors that affect the returns to limited
partners of VC funds. By combining information from two different publicly available data sets, we are
able to construct a unique database that combines fund‐level internal rates of return (“IRRs”) and cash‐
on‐cash return ratios (“COCRs”) with performance metrics such as IPO and M&A success percentages.
By combining the data sets, we are able to examine the drivers of fund performance more
comprehensively than previous researchers have been able to do. The analysis yields some surprising
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results. While we support the common perception that VC fund returns are related to IPO successes, we
also find that fund returns are importantly affected by the VC firm’s success in exiting by acquisition, and
by the firm’s skill in making early abandonment decisions to focus resources on the winners. In fact,
these other decisions of the firm are of similar magnitudes of importance to IPOs in explaining VC fund
IRRs and COCRs.
Regarding persistence, we find that both VC firm investment style (i.e. the mix of IPO and M&A
exit approaches and practice with regard to exercise of abandonment options) and more generic
reputation are related to fund performance. VC fund IRRs and COCRs are significantly related to the VC
firm’s prior track record of abandonment option exercise, exit by acquisition, and exit by IPO.
Moreover, in separate regression models of the mix of exit choices, investment style persists in that the
mix of exit choices in the firm’s previous VC funds is significantly related to the mix in the subject fund.
We also find that the firm’s experience in a sector is positively related to the IRR and COCR percentages
of a subsequent fund in the sector. In contrast to sector expertise, we find some evidence of the value
of agility. VC firms that are able to quickly refocus on new sectors may be able to produce higher VC
fund IRRs and COCRs.
Overall, our evidence indicates that fund performance depends on more than just luck. VC firms
can add value in a variety of ways, including sector expertise, agility, effective abandonment option
exercise, and skill and access to networks for achieving exits by IPO and acquisition. While the
persistence of performance across funds of the same VC firm appears to be due partly to the propensity
of entrepreneurs and investors to want to partner with experienced VC firms, our evidence indicates
that these preferences are warranted by the differential value added by such firms.
The remainder of the paper is organized as follows: In Sections II and III, respectively, we review
prior academic literature and evidence on VC fund performance and persistence across funds of the
same firm. Section IV, contains a description of the data, including review of procedures for refining the
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sample and merging information from the two databases upon which we rely. The empirical analysis is
presented in Section V. We conclude in Section VI.
II. Performance
Neither the fund IRR nor the COCR is ideal as a measure of fund performance, but the two
together have offsetting shortcomings. Comparing IRRs across funds is problematic because the re‐
investment assumption is unspecified. Depending on re‐investment opportunities, a fund that produces
a high IRR but returns cash quickly can be less desirable than a lower IRR fund that invests for a longer
period. In contrast, comparing COCRs across funds is problematic because the cash returns are
undiscounted. A fund that produces a high COCR but returns cash slowly can provide a return below
opportunity cost and be less desirable than a lower COCR fund that pays back more quickly.
Academic researchers usually do not have access to fund‐level IRR or COCR information and are
compelled to rely on outcome measures that are imperfectly related to realized performance. Kaplan
and Schoar (2005) is an exception in that the authors were able to gain access to fund‐level IRRs through
an arrangement with Venture Economics.1 Because Venture Economics did not provide information on
the identities of the VC firms or certain other performance statistics, Kaplan and Schoar are not able to
relate the reported IRRs to other outcome measures such as the IPO and acquisition percentages.
Kaplan, Sensoy, and Stromberg (2002) establish that attempts to infer project‐level IRRs from standard
databases, such as the Venture Economics database or VentureSource (formerly Venture One), are
problematic because of missing or incomplete information on financing rounds. Hence, fund‐level IRRs
also cannot be derived from the investment round information on projects in which a fund has invested
1 Based on 746 funds that were officially liquidated or were established before 1995, Kaplan and Schoar compare five different measures of fund performance: the reported IRR, an IRR calculated by the authors, an IRR calculated 5 years after the first closing, the cumulative total value to paid‐in capital (i.e., the cash‐on‐cash ratio), and a measure they refer to as the public market equivalent (derived by discounting fund cash flows at the returns earned on investment in the S&P 500). All five measures are highly correlated. The correlation between reported and computed IRRs was found to be 0.98; the correlation between the reported IRR and the 5‐year computed IRR was found to be 0.92, and the correlation between the reported IRR and the public market equivalent was found to be 0.88. The weakest relationship of reported IRR was to the cash‐on‐cash ratio, and was found to be 0.74.
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Academic studies of fund performance employ a variety of outcome measures as “success” or
“performance” metrics. Gompers, Kovner, Lerner, and Scharfstein (2008) define success as going public
or registering to go public. Hochberg, Ljungqvist, and Lu (2007) define performance as “exit rate,” the
fraction of portfolio companies successfully exited via IPO or sale to another company. Nahata (2008)
defines success as exit by IPO or acquisition and measures VC firm performance based on successful exit
percentages. Sorensen (2006) defines performance at the venture level as IPO probability.
The association of high‐investment‐multiple IPOs with fund performance and investment
success is well inculcated into both practitioner views and academic research. In one of the earliest
academic studies of venture capital, Sahlman (1990) cites a 1988 study by Venture Economics, which
reports that, for a sample of investments by 13 VC firms between 1969 and 1985, portfolio investments
with exit values per share of 10 or more times the investment per share (10X multiples) accounted for
only 6.8% of invested capital but 49.4% of ending value. Based on a proprietary sample of 216
investments we obtained from 3 leading VC firms, the extreme skewness of returns to VC investments
persists. Of the investments, which were made between 1995 and 1999, only 13% had investment
multiples of 10X or more but accounted for 85% of ending value.
From these empirical regularities and the correlation evidence from Kaplan and Schoar (2005),
one might infer that a focus on high‐investment‐multiple exits can serve as a reliable performance
metric. To our knowledge, however, the association between high investment multiples for some
portfolio companies and fund IRR or COCR has not been subject to rigorous testing. Nor has the
association between fund and firm outcomes to the persistence of performance been tested. Major IPO
successes are infrequent and likely to be subject to high degrees of randomness. Moreover, there is no
evidence that the occurrence of a high‐investment‐multiple IPO in a fund has any ability to explain the
realized returns on other investments of the same VC fund or other funds of the same VC firm.
Illustrating with the percentages reported by Sahlman, assuming an average investment holding period
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of 5 years and an average annual fund return of 15% (100% compounded over 5 years), the return on
10X investments would just put the fund at breakeven, in terms of nominal undiscounted cash flows.
Thus, how the VC firm does with its other investments is also important to overall fund performance and
is likely to be a less noisy indicator of VC firm skill.
In this study, we merge two venture capital data sources, the Venture Economics (“VE”)
database and the Preqin Private Equity (“Preqin”) database. From Venture Economics, we extract
detailed information on investment rounds and successes of specific investments by a fund. From
Preqin, we extract VC fund and vintage year IRRs, COCRs, fund status (closed or liquidated), the percent
of target fund size that was called, and the date on which the valuation information was reported. By
combining the two databases, we are uniquely able to relate fund IRRs and COCRs to the commonly
used success and performance metrics, IPO and acquisition percentages. Among other things, our
results challenge the folklore that venture capital success is determined by home runs. Except in the
extreme upper tail of fund performance, we find little relation between home runs (defined as high‐
valued IPO exits) and fund IRRs or COCRs and we find that M&A exit percentages are almost as
important as IPO percentages for explaining VC fund IRRs and COCRs.
III. Persistence
Persistence of financial performance has been studied for mutual funds, hedge funds and
private equity funds, including venture capital. Whereas the evidence of persistence for mutual funds
and hedge funds is mixed,2 there is compelling evidence of persistence over different VC funds of the
2 Carhart (1997) finds that, except for underperforming mutual funds, common factors in stock returns and expenses almost completely explain the persistence of risk‐adjusted returns. Carhart, Carpenter, Lynch, and Musto (2002) find that conditioning for survivorship bias weakens the evidence of persistence of mutual fund performance. Pastor and Stambaugh (2001) examine top mutual fund performers and find that optimal portfolios exclude hot‐hand funds even if momentum is priced. Busse and Tong (2008) find that persistence of mutual fund performance is due to industry selection, not stock selection. Kat and Menexe (2002) report that there is little evidence of persistence in the mean returns of hedge funds. Agarwal and Naik (2000) find that performance persistence in hedge funds is driven mainly by poor performers. After controlling for survivorship bias, Baquero, Verbeek, and Horst (2005) find that hedge fund performance persists significantly for only one quarter. Using quarterly hedge fund performance, Baquero and Verbeek (2007) find that sophisticated investors exhibit a hot‐
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same VC firm. As noted, Kaplan and Schoar (2005) find that VC fund IRRs are significantly explained by
the IRRs of at least the two prior funds of the same VC firm.
Hochberg, Ljungqvist, and Lu (2007) find that fund exit rate is positively related to VC firm
experience and the IPO or acquisition exit rate of the most recent prior fund. Persistence from fund to
fund is of an economically significant magnitude in their study. They also consider an intermediate
indicator of success, whether the venture survived to another round. They find that survival probability
is related to fund exit rates and the experience of the VC firm, among other factors.
Gompers, Kovner, Lerner, and Scharfstein (2008) seek to assess the role of skill in explaining
entrepreneur and VC success. They find that entrepreneurs with records of prior success are more likely
to succeed than others and that funding by VCs with industry‐specific experience enhances the
probability of success for entrepreneurs who do not have track records. Their interpretation is that
experienced VCs can indentify first‐time entrepreneurs who are more likely to become serial
entrepreneurs. Gompers, et al. associate performance persistence with skill, reasoning that high
performance may be due to (1) experienced VCs being better able to identify good entrepreneurs or (2)
being able to add more value.
Sorensen (2006) finds that companies funded by experienced VCs are more likely to go public
but that the success follows from both the direct influence of VC experience and from sorting that
enables experienced VCs to invest in better companies. To estimate the effect of experience, Sorensen
models sorting and finds that sorting is more important than experience as a determinant of success.
He does not seek to assess the means by which VC experience contributes to value, but he observes that
experienced VCs may be better monitors, have access to larger networks, or be better able to certify
value.
hand bias. Funds with longer histories of success experience higher future success and greater flows of investment capital.
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Hsu (2004) finds that VC firm reputation contributes positively to the probability that an
entrepreneur will accept the offer of investment by a fund and that experienced firms are able to invest
on more favorable terms. Nahata (2008) finds that ventures that are backed by more reputable VCs
(based on IPO capitalization share) are more likely to exit successfully and can access public markets
faster. He argues that his measure of reputation captures the screening and monitoring expertise of the
VC firm. Ivanov, Krishnan, Masulis, and Singh (2008) find that a VC’s share of VC‐backed IPOs is
positively related to post‐IPO long‐run performance and to the frequency with which a VC’s portfolio
firms are able to go public. They explore possible reasons for superior performance and find that
reputable VCs are associated with stronger networks, higher IPO demand, more active post‐IPO
involvement, and better corporate governance.
The research to date indicates that persistence is far more important as a driver of VC fund
returns than it is for mutual fund or hedge fund returns. The transmission mechanism between prior
performance and current fund performance, however, is far from clear. While there is evidence that VC
firms with records of success do some things differently than do other VC firms, the causal connection
between doing things differently and value creation is not established by the current research. It is
unclear whether fund success derives from internal skills and expertise of the VC firm, strength of the
firm’s network, opportunistic timing with respect to fund focus and timing of investment and exit
decisions, or simply from entrepreneur and investor reliance on VC firm track record when they decide
with whom to partner. Our evidence adds to that of other studies that seek to understand the reasons
for persistence.
IV. Data
The unit of analysis in our study is the VC fund. The SDC Venture Economics database is a
relational database with a complex file structure. Some information is provided at the investment round
level, some at the venture level, some at the fund level, and some at the firm level. In addition, through
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links to the SDC Global New Issue and Merger and Acquisition databases, the database includes
information on VC‐backed ventures that went public and on VC‐backed ventures that were acquired.
These different files can be related using key information that is common to each type of record. The
database includes domestic (US) and foreign VC funds of domestic and foreign‐based VC firms.
Ultimately, we focus on US funds that are managed by conventional VC firms and where investment
outcome information is available.
Any study of VC fund performance confronts problems of sample bias. Venture Expert and
Preqin acquire their data from VC firms and limited partners and through Freedom of Information Act
(FOIA) disclosures. The likelihood that Venture Expert acquires the data may depend on such things as
the willingness of VC firms to report, the number of limited partners who might decide to provide the
information, and the characteristics of limited partners (some of whom are subject to FOIA disclosure
requirements). Some VC firms that do poorly may not report at all; others may not report all of their
initial investments, so that success percentages may be overstated. Preqin faces the same under‐
reporting challenges and both data sources may, themselves, make choices as to the VC firms and funds
about which they compile data.
Identification of the Usable Sample
The full sample of funds we were able to extract from VE for vintage years from 1958 through
early 2006 included 23,619 unique fund names by 7,568 firms. Because of the potential for bias in the
VE and Preqin data, we track the effects of the filters on a number of sample properties. Results of the
filtering process are described in Appendix A.
When VE cannot specifically determine which fund of a VC firm made an investment, it identifies
the investment as having been made by an “unspecified” fund of the VC firm. Approximately 25% of the
fund names include the term “unspecified.” Of the total, 8,391 funds are designated as foreign (i.e.,
non‐US). The total also overstates the number of funds because sometimes slight variations on the fund
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name appear in the data. For example, the database includes (i) 3V Source One Venture Funds ‐
Unspecified Fund, (ii) 3V Source One ‐ Unspecified Fund, and (iii) 3V Source One Ventures Fund Ltd. The
differences, such as the presence or absence of a space in Source One, cause problems for assessing
fund‐level performance. We use information on fund nation, fund year, and fund name together to
resolve questions related to fund names that appear to be redundant.
Because VE receives primary data from many sources, data related to fund investments can
include duplicate or redundant records. When the records from different sources are identical, it is easy
to exclude the duplicate information. However, sometimes records are not specific as to the identity of
the fund making the investment. A record might indicate, for example, undisclosed investor,
undisclosed venture fund investor, or an unspecified fund of a particular VC firm. Investments by
undisclosed investors are dropped from the analysis. To avoid double counting related to firm‐level
experience, we also exclude funds and investment rounds by funds designated as “unspecified” unless
“unspecified” is the only fund of a given VC firm. Exclusions of funds for which investment round
information was not available reduces the number of funds to 15,002. For the most part, this resulted in
dropping some very recent funds and funds where information on the VC firm type was missing from
the VE database.
The fact that information is often reported by more than one source, while it makes working
with the data more difficult, also mitigates the potential for under‐reporting of fund investments. For
example, whereas one data provider might have reported an investment as having been made by an
unspecified fund, another may have provided the fund information. In such cases, we would not lose
the fund‐level outcome information.
Restricting this sample to funds with a US investment focus reduced the sample further, to
8,015 funds. Funds focused on the US tended to be somewhat older than those focused outside the US,
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either because the industry developed first in the US or because Thompson first emphasized collecting
data for US funds.
Restricting to conventional VC firms further reduced the sample to 6,206 conventional US VC
funds, with 1969 being the earliest vintage year. This is the filtered sample from VE on which our results
are based. We define conventional VC firms to include: private equity firms investing their own capital
(75.4% of the sample), investment banks and merchant banks or their subsidiaries or affiliates involved
in VC (10.8%), commercial bank affiliates or subsidiaries involved in VC (3.0%), other financial firms or
their subsidiaries or affiliates involved in VC (9.8%), and other firms and service providers focused on VC
(1.0%). The filtered sample excludes funds managed by non‐financial firms, endowments, universities,
pension funds, angel groups, incubators, business development companies, government entities,
individuals, and where the firm type is unknown. Our main reasons for excluding these types are first
that we are more concerned with the potential for selective reporting and second that some types, such
as government entities, may have low accountability for financial performance or may have objectives
other than financial performance.
To add IRR and COCR information to the observations, we matched the VE conventional US VC
funds to the Preqin database. Usually, we matched on firm and fund name and fund year, but because
of differences in how names are reported on the databases, this procedure had to be performed
manually. For this, we first used a team of students to identify tentative matches, and then one of the
coauthors reviewed all of the matched and non‐matched funds. Ultimately, we were able to match
1,518 of 2550 Preqin US‐focused funds to the VE sample of conventional US VC funds.3 Matched funds
are somewhat more recent than the VE fund sample, predominantly from 1980 on; are more likely to be
funds that were established as independent private partnerships (78.9%); and are more likely to be
3 The unmatched Preqin funds include conventional VC, but also other types such as corporate venture funds and limited partnerships focused on real estate, timber, or petroleum exploration.
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classified as having a focus on buy‐outs (28.7%) relative to earlier stages. Appendix B provides detailed
fund year information on US‐focused VC funds from VE and Preqin.
Descriptive Statistics and Benchmarks
The matched sample includes 1,285 funds with usable IRRs. The others have IRRs that are
designated by Preqin to be either “not available” or “not meaningful.” For the most part, these
designations are skewed toward very recent funds that had not yet had a closing or were still in the
process of raising capital. The matched sample also includes 1,438 funds with usable COCRs. Again,
missing values tend to be associated with more recent funds. The matched sample includes 1,203
observations with both IRR and COCR information.
In Table 1, we report descriptive statistics for the 1,518 observations where we are able to
match fund‐level data from Venture Economics and Preqin. These funds represent a subset of the 6,206
conventional US VC Funds we identified from VE. For the 1,285 with net IRR data, the simple average of
fund IRRs is 13.7%, a return that perhaps is well below common perceptions of typical VC fund returns.
However, the IRR distribution is highly skewed, with the top 10% of funds reporting IRRs of 39.2% or
greater. For the 1,438 with COCR data, the simple average is a multiple of 1.79 times, but again, the
distribution is highly skewed, with the top 10% reporting COCRs of 3.11 or greater. As we are concerned
about the potential for selective reporting, it also is noteworthy that 10% of the sample report IRRs of
negative 11.7% or lower, and 10% report multiples of 0.64 or lower. Thus, if there is selective reporting,
the sample statistics on performance are not indicative of extreme bias.
A central contribution of this paper is to relate IRR and COCR measures of performance to the
simple metrics that have been used in most other studies, IPO percentage and, sometimes, IPO‐plus‐
acquisition percentage. Table 1 shows that an average of 56% of the companies in which a fund invests
receive some form of next stage financing (either an additional private round that is classified by VE as
being at a later stage, or exit financing as an IPO or an acquisition). The VE statistics indicate that, on
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average, 14.3% of a fund’s original investments eventually go public and 12.6% eventually are acquired.
The IPO and M&A percentages are similar to those reported in other studies that rely on the Venture
Economics database.4 These outcomes are included in the percentage that receives next stage
financing, indicating that, for the average fund, about half of the companies that receive next‐stage
financing are eventually harvested.
A second contribution of the paper is to assess persistence by relating fund performance to firm
performance. We do this in two ways. First we assess the relationships of fund IRR and COCR to firm‐
level outcome statistics measured over funds prior to the subject fund. Table 1 shows that the outcome
statistics for prior funds of the same firm are, on average, similar to those of the subject fund. Second,
we test for style persistence by examining the relationship of subject‐fund outcomes to the outcomes of
prior funds of the same firm.
A third contribution is to more directly examine the relationships of fund performance variables
to firm experience. Table 1 shows, for example, that prior funds of the same firm had made an average
of 62.4 investments in the same sector as the sector focus of the subject fund. However, agility may
also be of value and the table show that 43.7% of subject funds are focused on different sectors than
the immediate prior fund of the same firm.
A fourth contribution is to assess whether, separate from the quantifiable metrics of prior fund
performance, generic reputation has a role in explaining subject fund performance. For this, we
consider two different reputation measures that recently have been proposed: (1) the Nahata (2008)
measure, which is based on the dollar capitalization share of a VC firm in the IPO market, cumulated
until the end of 2005, and (2) the Ivanov, Krishnan, Masulis, and Singh (2008) measure, which is based
on averaging each firm’s annual IPO market share over 1996‐2002.
4 To better understand the validity of the Venture Economics database, we contacted Susan Woodward, founder of Sand Hill Econometrics. Sand Hill conducts extensive research on returns to VC funds, and has investigated the completeness of information available from various sources, including Venture Economics. Doctor Woodward has advised us that Venture Economics captures virtually all IPOs and economically significant acquisitions.
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Because VC fund performance is volatile and episodic, we employ several performance
benchmarks that are intended to control for the typical performance of funds launched in the same
vintage year and market conditions around the normal time for exits to occur. Additional information
on these benchmarks, and others, as well as the yearly effects of selection due to matching are provided
in Table 2.
Sample Statistics by Reputation
In most of the empirical analysis, we focus on Firm Reputation 1 as our proxy for reputation.
Table 3 shows comparison statistics for funds classified by reputation. The table includes two sets of
comparisons. The first set is based on all conventional US VC funds or all matched funds and the second
is based on only funds with vintage years of 1990 or later and also excludes the first three funds of any
VC firm. The second comparison is intended to mitigate the effect, if any, of selection or look‐back bias
in the sample comparisons.
Funds offered by ranked firms exhibit much higher IRRs and COCRs and higher exit outcome
percentages than funds offered by non‐ranked firms. They also are larger and invest in more deals than
funds offered by non‐ranked firms. Ranked firms have better outcome track records and much higher
levels of sector‐specific experience than do non‐ranked firms. Differences in vintage‐year and
benchmark IRRs suggest that ranked firms may be somewhat better at timing the market with respect to
launching new funds.
Sample Statistics by Fund IRR
In Table 4, we report mean statistics for funds sorted into deciles based on fund IRR. The table
reveals that the 10th decile funds are fundamentally different from the other funds in the sample. The
top performers have much higher IRRs and COCRs than even the 9th decile. Over all but the top decile,
better performance is associated with lower percentages of deals receiving next stage funding (more
aggressive exercise of abandonment options), rising IPO percentages, and fairly flat M&A percentages.
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In contrast, in the top decile, the percentage of deals receiving next‐stage funding is sharply higher, the
IPO percentage is higher but follows the trend, and the M&A percentage is sharply higher. Similar
patterns are apparent with regard to firm characteristics. Additionally, funds in the top decile are
offered by firms that are much more likely to be ranked and that have much greater levels of sector‐
specific experience. Not surprisingly, higher average fund IRR also are associated with higher vintage
year and benchmark IRRs.
The evidence in Table 4 suggests that the top performing funds owe their success partly to
investment selection, which may arise from a richer deal flow or better selection, so that abandonment
is less likely; and partly to greater success with exits, particularly with regard to M&A exits. Given the
much higher IRRs, these funds also are likely to be associated with high‐valued IPO exits. To the extent
that anecdotal studies of the drivers of fund performance are focused on samples that are drawn from
the top performing funds or funds offered by ranked VC firms, the inferences drawn from those studies,
such as the inference that performance is driven by home runs, may be due to the special characteristics
of the sample. 5
Changes in Sector Focus
Some of our evidence suggests that fund IRRs and COCRs are higher when the VC firm has the
ability to change sector focus in response to the anticipated values of investment opportunities. One
implication is that when a firm launches a new fund in a sector that is different from that of its
immediate prior fund, it is doing so partly in response to a shift in perceived investment opportunities.
As evidence on the motivation for sector changes, we can examine whether changes in focus appear to
correspond with changing market‐wide perceptions.
Figure 1 is based on the sample of all observations where a subject fund is focused on a
different sector than the VC firm’s immediate prior fund. For the purpose of this analysis, we classify
5 Generally, as discussed earlier, the VE data are not sufficient to determine the realized returns or multiples on fund investments. Thus, we rely on inference drawn from the descriptive data and our empirical analysis.
16
funds into 5 sectors: computer, Internet, biotechnology, medical, and non‐technical. From 1980 through
the end of our sample period, there are 825 sector changes in our database. Figure 1 is constructed by
expressing the number of switches to a given sector in each year as a percent of all switches that year
and then calculating a centered 3‐year weighted average of the percentages, where the weights are
based on the total number of switches over the 3 years. The figure shows a shift of focus mainly from
computer to non‐technical in the early 1980s; a shift back into computer and away from biotech and
non‐technical in the late 1980s; a gradual shift into computer and Internet and away from biotech and
non‐technical during the 1990s; and a shift away from computer and Internet and into non‐technical,
biotech, and medical in the 2000s. Overall, these changes of focus follow general market perceptions
about changes in sector investment opportunities over time.
V. Empirical Analysis
The empirical analysis comprises 3 parts: OLS regressions of fund performance, selection‐bias
corrected regressions of fund performance, and OLS outcomes regressions. We also conduct additional
tests of look‐back, selection, and attrition bias and find similar results. Overall, the results: (1) quantify
the relationships of fund outcomes (next‐stage funding, IPO, and M&A) to realized performance (IRR,
COCR, and a hybrid measure that combines the two); (2) quantify persistence effects through the
relationships of firm‐level prior outcomes to realized performance of the fund; (3) assess the
relationships of firm experience and agility to realized performance; (4) quantify the persistence of firm
style over time; (5) assess the separate effect of firm reputation on performance; and (6) verify that our
results are robust to sources of potential bias. While we demonstrate that look‐back, selection, and
attrition biases are important for understanding which funds have information reported on
performance, they do not materially affect the results. Accordingly, we focus discussion on the OLS
results.
OLS Regression of Fund Performance
17
Table 5 contains several different models of fund performance where the fund’s net IRR is the
dependent variable (Models A‐1 through A‐6), as well as one model where COCR is the dependent
variable (Model B) and one where we combine IRR and COCR into a measure that is more closely aligned
with the true dependent variable of interest, fund NPV (Model C).
In all models, we find evidence of the value of abandonment options. Fund performance is
better, the more quickly the fund abandons early investments that apparently are not promising. The
outcome variables are scaled in consistent units (percentages of fund or firm initial investments) so that
the results can be interpreted easily. For example, in Model A‐4, which is our final IRR model, a 1
percentage point reduction in the fraction of initial investments that receive next‐stage funding is
associated with an IRR that is 0.077 percentage points higher. A one‐standard‐deviation (25.0
percentage point) reduction in next‐stage funding implies a 1.93 percentage point increase in fund IRR.
While the relationship weakens and becomes lower with the addition of variables, given the expected
negative sign associated with the hypothesis that abandonment options are valuable for investors, it is
always significant at beyond the 10% level.
The IPO and M&A percentage coefficients are highly significant across all models. The
difference in coefficients reflects the relative importance of IPOs and acquisitions in terms of their
contributions to fund performance. The relative relationship is quite stable and indicates, contrary to
common beliefs that acquisitions are almost as important in driving performance as are IPOs. In some
specifications, we cannot reject the hypothesis that the coefficients for IPOs and acquisitions are equal.
Based on Model A‐4, a one‐standard‐deviation (20 percentage point) increase in IPO percentage is
associated with a 6.18 percentage point increase in fund IRR, whereas a one‐standard‐deviation (15.7
percentage point) increase in M&A percentage is associated with a 3.58 percentage point increase.
If fund performance reflects persistence, then performance should be related to the prior
outcomes of the firm. The firm outcome variables parallel the fund outcomes and are comparably
18
scaled. Coefficients are similar to those for fund outcomes. Again, we find evidence that past
abandonment practice leads to higher current‐fund performance, as do higher previous IPO and M&A
exit percentages. As the standard deviations of firm performance variables are similar to those of fund
outcomes, the partial effects are similar.
We also test the importance of sector‐specific experience and agility. As expected, fund
performance is strongly related to the firm’s prior experience in the sector on which the fund is focused.
We measure experience as the number of previous investments by the firm in the sector on which the
subject fund is focused. In Model A‐4, a one‐investment increase in experience is associated with a
0.042 percentage point higher IRR and a one‐standard deviation increase in experience (93.9
companies) is associated with a 3.96 percentage point higher IRR.
Agility has only a suggestion of importance. We use an indicator of agility by identifying the
change in sector focus from one fund to the next, which we measure in the negative (staying in the
same sector as the prior fund). While sector‐specific experience is valuable, there is also potential value
in being able to shift resources to a sector where the rate of innovation is and concomitant capital
demand are high. The coefficient is only significant (at the 10% level in a one‐tail test) in Model A‐2,
where firm reputation and all control variables are omitted. The coefficient estimates in all
specifications of Model A except A‐6 (where sector experience is dropped) indicate that agility is
associated with a fund IRR that is about 2 percentage points higher. Thus, given the crudeness of our
measure of agility and the fact that a return difference of more than two percentage points only
approaches statistical significance, we cannot convincingly reject the hypothesis that agility is not
important.
Separate from the firm’s track record of prior successful exits and its experience, there may also
be a role for more generic reputation, particularly if firm reputation has effects like enriching the deal
flow, attracting better employees, lowering the cost of raising capital, and facilitating exits, all of which
19
are identified as possibilities in the previously cited literature. We test two different indicators of firm
reputation. Both are snapshots at points in time and we are applying them to funds launched at
different times, so the measures are imperfect for our purposes. Our first measure, Firm Reputation 1,
is from Nahata (2008) and is based on the total market capitalization of a VC firm’s prior IPOs as of the
end of 2005. The measure is likely to increase with the age of the VC firm, its overall level of activity,
and the ultimate and long‐run success of companies it has brought to market. We classify firms listed in
the Appendix to Nahata as “reputable” compared to the others. In Model A‐4, the reputation measure
is associated with a 22.7 percentage point higher IRR. Our second measure, Firm Reputation 2, is from
Ivanov, Krishnan, Masulis, and Singh (2008) and is based on the firm’s average IPO market share over
the period from 1996 through 2002. The measure, again, is likely to increase with firm age and activity
level. When we introduce this variable to Model A‐5, the coefficient is near zero and is not significant.
We believe this is because IPO market share is closely related to firm experience. When, in Model A‐6,
we drop sector experience, the reputation measure gains significance and the coefficient implies an IRR
that is 8.89 percentage points higher than for funds of other firms. 6
The vintage year and benchmark control variables have the expected effects; IRRs are higher
when vintage year results are higher. We find no significant relationship between fund IRR and either
new commitments to venture capital in the fund vintage year or the average S&P P/E ratio in years 6
through 8 after the vintage year, which we use as the most likely years for harvesting.7 Other than fund
6 There is, of course, some contamination between the reputation measures and the fund’s performance, particularly for funds that had matured before 2005 or during the 1996 to 2002 window. However, for several reasons, this spurious effect should be small: the reputation measure is binary and is based on all of a firm’s IPO exits, whereas our performance measures are continuous and are based on the results for a single fund, and the reputation measure and fund performance are measured at different times. 7 The finding of no statistically significant partial relationship between new capital commitments in the vintage year and IRR may seem to conflict with Gompers and Lerner (2000). Gompers and Lerner find that vintage year valuations are driven up during periods when capital flows to VC are high. Presumably, it follows that fund IRRs and COCRs should be lower when vintage year capital commitments are high. While the regression models show no partial effect, univariate decile sorts similar to those in Table 4 reveal that IRRs and COCRs are higher when new capital commitments, numbers of VC investments, and S&P P/E ratios all are low in the vintage year. There is no
20
age, our fund‐level controls are not significant, indicating that IRRs are not systematically different from
the model for funds designated as “early‐stage,” first funds, or funds that are too young for market
conditions at exit to be measured.
The COCR performance results in Model B are similar to the IRR results in Model A‐4. COCR is
measured in ratio form. A one‐standard‐deviation reduction in the percent of companies getting next‐
stage funding is associated with a multiple that is 0.183 higher, a one‐standard‐deviation increase in IPO
percentage is associated with a 0.365 higher multiple and a one‐standard‐deviation increase in
acquisition percentage is associated with a 0.179 higher multiple. Firm performance variables have
weaker effects on COCR and significance levels are lower. Firm experience is also less important than
for IRRs and agility is not significant. Firm Reputation 1, is highly significant and implies a multiple that is
2.22 higher than for other firms. Among the controls, early‐stage funds have higher multiples, perhaps
due to the longer expected holding period for early‐stage investments.
As discussed in Section II, neither IRR nor COCR is an ideal measure of fund performance. If fund
NPV is the desired measure, IRR can be biased because venture funds do not have equal lives. A high‐
IRR fund that liquidates quickly may pose a reinvestment problem that is not reflected in the measure.
The COCR measure has the opposite bias. A multiple can be high if the holding period is unusually long,
rather than because the fund is performing well. To exploit these offsetting biases, we construct a
hybrid measure, IRR:COCR, which is constructed by adding the log of one plus the IRR in decimal form
and the log of COCR. In log form, the two variables are similarly scaled with similar volatilities and the
distribution of the hybrid variable is approximately normal. Using this measure, high values are more
clearly “good” results and low values are more clearly “bad.” Model C parallels Models A‐4 and B, but
uses IRR:COCR as the dependent variable. The resulting r‐square is substantially higher, and the
significance levels and relative importance indicators are similar to those in the other models.
apparent relationship between fund performance and number of IPOs in the vintage year or S&P P/E at approximate exit time, but a positive relationship to number of IPOs at approximate exit time.
21
Returning to the Firm Reputation 1, the Table 5 results show that even after controlling for fund
outcomes, firm track record and experience, and market controls, reputation measured in this way still
is associated with much higher IRRs and COCRs. Table 4 also suggests that high‐reputation firms offer
funds that are somehow different from the rest of the sample. To better assess the nature of the
difference, we tried interacting reputation with the fund‐level outcome measures, extending Model A‐4.
While the coefficient on IPO percentage is not significantly different from that for all observations, we
do find sharp differences for the other two outcome variables. Whereas, overall, there appears to be
value in exercising abandonment options, for high‐reputation firms we find the opposite. The percent of
investments that receive next‐stage funding is strongly positively related to fund IRR (the summed
coefficient is 1.12 and the difference relative to the full sample is significant with a p‐value of 0.043).
Similarly, the percent of investments that are acquired is significantly more positive for funds of high‐
reputation firms (the summed coefficient is 2.25 and the difference is significant with a p‐value of
0.001). Segregating the high‐reputation funds in this way reduces the relative importance of the M&A
percentage for the other funds in the sample, but the partial effect remains statistically significant and is
about half as high as the IPO percentage. Results are similar for the other performance measures and
these findings also hold when we drop observations for the first three funds of each VC firm.8
Overall, these additional results suggest that funds offered by high reputation firms benefit from
a richer deal flow and/or better investment selection so that more of their investments reach successful
exits; that while Table 4 shows that the percentage reaching IPO is higher than for other firms, the value
added per IPO is similar to that of other firms; and that the M&A percentage is much higher than for
other funds and also the acquisition exits are substantially more valuable. The results are consistent
with our overall finding that there is more to success than just home runs, and that being able to find
successful exits for other investments in the portfolio is also important.
8 The full fund performance models upon which this discussion is based are available from the authors.
22
Selectivity
Because selective reporting could be biased toward successful outcomes, we take a number of
steps to mitigate the potential for our results to be affected by this type of bias. By focusing on
conventional VC funds, we exclude funds such as corporate funds, where it seems that managers could
be most likely to fail to disclose unsuccessful investments and where there is less potential for external
verification through limited partners and others. We also exclude non‐US funds because of similar
concerns and because the nature of venture capital investing is quite different across countries. Still,
Preqin does not report performance for the majority of venture‐backed funds that are identified by VE.
We were able to match only about one‐fourth of the funds reported by VE, and of the matched firms,
Preqin did not report IRRs for about 15% and did not report COCRs for about 5%. The resulting sample
may be biased either in the selection by Preqin as to which funds to track, or in the selective reporting of
fund performance. In Appendix B, we already have provided evidence that the Preqin sample is
underrepresented in the early and most recent years, relative to the VE sample.
To address the potential for bias more directly, we use a Heckman two‐step model with an
instrumental variable to estimate a probit model of selection and, based on the probit results, to
introduce a control for selection. Table 6 presents the results of this analysis for each of the three fund
performance measures (IRR, COCR, and IRR:COCR). The probit selection equations are estimated in a
single step over both possible sources of selectivity bias. For example, from the full sample of
conventional US venture capital funds, we code the dependent variable as 1 if an IRR is reported by
Preqin.9
9 We originally tried a nested two‐step approach of first estimating a selection adjustment for matching with Preqin and then using this as an additional variable in a second probit model of performance reporting in the matched sample, with the resulting probit being used to form the selectivity variable in the final regression. Through the attempt, we determined that the selection factors that affect the match are also the factors that affect performance reporting. Because nothing was gained by the more involved approach, we report only the standard Heckman results.
23
The results of the selection models in Table 6 are similar for all 3 performance measures, as
differences are only due to variations in the second‐stage sample. We include all of the variables from
the performance models, as well as a binary instrument that indicates that a fund is backed by a
commercial, investment, or merchant bank or affiliate or subsidiary. Among this group, we expect the
probability of underreporting and of exclusion in the Preqin data to be relatively severe. As expected,
the coefficient on the variable is negative and significant. Other variables in the selection models
indicate that, as expected, reporting is more likely when the IPO and M&A percentages of the fund and
of the firm are high; when the firm has a more established track record, as indicated by sector
experience and the first‐fund variable; and when the fund is older, but was not launched in a very early
year compared to the rest of the sample. Performance is more likely to be reported for funds launched
when new capital committed to venture capital is high (generally reaching a peak around the year 2000,
as shown in Table 2). Other market controls are not significantly related to selection.
Based on the inverse Mills ratios, selection is marginally significant in the IRR model. However,
the overall effect of selectivity on the performance models is modest. Usually, the coefficients related
to fund outcomes, firm outcomes, and firm experience are slightly weaker after bias correction, the
coefficients on Firm Reputation 1 are slightly stronger, and the coefficients on the control variables tend
to be modestly stronger. We infer from Table 6 that our qualitative conclusions about the relationships
of fund outcomes, firm outcomes, firm experience, and firm reputation are not attributable to bias
related to which funds have performance data reported by Preqin.
Style Persistence
Results in Tables 5 and 6 demonstrate persistence in that a firm’s prior track record of
outcomes, its sector experience, and its reputation all are positively related to fund performance,
measured in various ways. What drives the relationships between prior outcome variables and current
fund performance remains an open question. In one view, if entrepreneurs and investors interpret the
24
firm’s track record as evidence of skill, then the firm might face a richer deal flow, lower costs of raising
funds, and higher fund performance in the next fund, even if the firm’s track record is purely a result of
good luck and the capabilities of the management team are no better than those of the typical manager.
In the other view, prior success indicates superior skill. Even if the firm was initially just lucky, the
success may help attract better employees or the exits may contribute to a stronger network that
enhances subsequent harvesting opportunities.
One way to address the question of whether what the manager does really adds to the value of
subsequent funds, as a contributing factor to performance persistence, is to test whether funds of the
same firm exhibit style persistence. More specifically, does a track record of high percentages of IPO
exits imply that subsequent IPO exits will also be high? Moreover, does a track record of high M&A
percentages to IPO percentages imply that the difference will also be high in subsequent funds? In
Table 7, we find both kinds of evidence of style persistence. Abandonment option exercise practices
(the percent of companies receiving next‐stage funding) of the subject fund, IPO percentage, and M&A
percentage all are strongly related to the same outcome percentages for the firm, based on its prior
funds. Also, the difference between M&A percentage and IPO percentage is positively and significantly
related to the same measure for the firm based on its prior funds. While, as implied by style
persistence, a high IPO percentage is not significantly related to the firm’s prior M&A percentage, there
is a significant relationship when the dependent variable is the M&A percentage. Perhaps the greater
visibility of past IPO successes enables the firm to be more successful with acquisitions later on.
There is, however, also a slightly different interpretation that is not as positive for style
persistence. While the coefficients are significant, they are all less than 1.0. This is true even if only the
firm performance variables are retained in the models and even if only the directly corresponding
variable is retained.10 In fact, dropping the other variables does not materially change the coefficients of
10 These results are available from the authors.
25
interest. Thus, there is evidence of substantial regression toward the mean. Regression toward the
mean of the magnitudes reflected in Table 7 argues for luck also being an important factor in realized
success (or failure). After all, if the success of a fund leads investors to believe that the VC firm has skill,
should we not expect that the resulting enriched deal flow and easier access to capital would, all else
equal, lead to even greater success in the next fund.
Robustness and Other Checks
Quantile regression: An implicit assumption of OLS is that estimation of the conditional mean is
appropriate for all segments of the dependent variable’s distribution. This is no more appropriate than
suggesting that the mean is the best measure of central tendency, without even considering the
distributional characteristics of the random variable. This limitation can be addressed by using quantile
regression techniques as proposed by Koenker and Bassett (1978).
Generalized quantile regression enables us to estimate changes in the marginal effects of the
covariates along different sectors of the distribution. Bassett and Chen (2001), in their study of the
effects of investment style on mutual fund performance, state that quantile regression is critical for
identifying how style affects returns over the full distribution of returns.11 The conditions required to
solve the optimization problem and the estimator properties are well‐known. Parameter estimates are
attained through linear programming and an iteration process.12 For these purposes, the bootstrap
procedure is ideal.13 We apply this technique by performing simultaneous quantile regression for each
decile of the IRR and COCR distributions. In each case, the standard errors are produced by randomly
sampling (with replacement) from all of the observations in the analysis dataset, estimating the quantile
11 In principle, quantile coefficients could be estimated using OLS on subsets of the data. However, the distributional assumptions about the error term are unlikely to hold and the OLS estimates are more sensitive to outliers within subsets that contain relatively few data points. This can be addressed by using generalized quantile regression to estimate an equation to describe any section of the distribution. 12 See, Koenker and Bassett (1982), Rogers (1993), and Greene (2008). 13 Jeong and Maddala (1993) argue that bootstrapping is appropriate for most applications of hypothesis testing because distributional assumptions are typically unreliable. In addition, Johnston and DiNardo (1997) demonstrate that bootstrapped standard errors produce consistent estimates when they cannot be derived analytically.
26
regression coefficients, performing 100 replications, and calculating the observed variation in the
parameter estimates.
In Figure 2, we use least‐absolute deviation (LAD) quantile regression to examine how the
estimated coefficients change over quantiles ordered by the dependent variable. In the LAD versions of
Model A‐4, where fund IRR is the dependent variable, the percentages of both fund and firm companies
receiving next‐stage funding are consistently negatively related to fund IRR. IPO and M&A success
percentages of the fund and firm are consistently positive, with IPO being consistently more important
than M&A, though the difference is not great. Fund IRR increases with increases in the firm’s historical
IPO percentage and declines with increases in the firm’s historical acquisition percentages. Sector
experience is consistently positive and agility (measured in the negative, as remaining in the same sector
as the prior fund) is consistently positively related to fund IRR. The coefficients on our agility measure
generally are significant at the 10% level in one‐tailed tests. Finally, firm reputation is very important for
explaining IRRs in the highest quantiles (80 and 90%), consistent with earlier evidence on the
relationship between top performing funds and firm reputation. Results for the COCR are similar in all
important respects.
Survivorship and look‐back biases: Our performance and persistence results may be affected by
survivorship effects in two ways. First, it is possible that Venture Economics and/or Preqin selects the
firms on which it reports based partly on success of an early fund. If so, then the IRRs and COCRs of the
first‐reported fund of a firm could be positively biased compared to a representative first fund that was
free of this look‐back bias. Second, as our sample of funds begins prior to when Venture Economics and
Preqin began to compile and report data, it is possible that firms may have been selected for inclusion
partly because they had survived through the period before the services had begun to collect data. If so,
the empirical estimates of persistence could be overstated.
27
We have tried to limit the potential for survivorship and look‐back biases by limiting the sample
to conventional venture capital funds in the US. To further assess these potential sources of bias, in
Table 8, we (1) estimate Model A‐4 dropping first funds, and (2) estimate the model on a sample that
excludes firms with their first fund investment years earlier than 1990, and (3) do both. As shown, the
fund outcome coefficients are somewhat stronger and the persistence results are somewhat weaker.
These shifts are in the directions implied by look‐back and survivorship biases, but do not fundamentally
alter any of our conclusions.
Attrition bias: Attrition bias is related to survivorship bias, but arises for a different reason.
Because our data are collected for the same firms over a number of years, it is possible that some may
drop out of the sample prematurely. More specifically, a firm may continue to launch funds, but the
fund performance information not be reported or a firm may cease to exist or discontinue its venture
capital fund business. Attrition can result in bias if firms that drop out are systematically different from
those that remain.
Attrition can bias the sample in two ways. First, if some groups of firms are more likely to drop
out of the sample than others, results based on the remaining sample cannot be generalized to the
original population. Second, systematic attrition can negatively affect internal validity by altering
correlations among variables. Attrition bias can be addressed using inverse propensity score weighting
similar to that suggested by Little and Rubin (1987) and McGuigan, et al. (1995).
In this case, the weighting adjustment for attrition uses the probability that a given firm will be
present (“surviving”) in a subsequent time period with a new fund. This probability is estimated by using
the predictions from a logit regression, where the dependent variable is equal to 1 if the firm is a
“survivor” and 0 otherwise. The explanatory variables are firm‐level covariates; specifically, the total
number of observed funds by the firm, the average fraction of companies with IPOs in all of the firm’s
funds, and the average fraction of acquired companies in all of the firm’s funds.
28
Unlike most panel surveys, which have a predetermined start and end date, our data collection
terminates on an arbitrary date and it is not possible to determine the “survivor” status of a firm with
absolute certainty. Thus, we rely on some sensible rules to classify “surviving” firms; namely, less than 6
years, less than 8 years, and less than 10 years between the data termination date and the year in which
the firm’s most recent fund was observed. The logit regressions are then estimated using each of these
assignment rules and each firm is subsequently assigned a weight equal to the inverse of their predicted
survivor probability. Therefore, those with a low probability of attrition receive lower weights and those
with high probability of attrition receive greater weights. The IRR and COCR regressions are then re‐
estimated using the 6‐, 8‐, and 10‐year probability weights. This is designed to take some of the skewed
explanatory power from the “surviving” firms and redistribute it back to that of the random sample. We
use the logistic odds to form attrition weights and then re‐estimate the Heckman selection Model A‐4H
by weighted least squares.
Generally, we find that the logit results are negatively related to IPO percentage and positively
related to M&A percentage and total number of funds launched by the firm. Table 8 demonstrates that
attrition‐weighting has only a modest effect, compared to Model A‐4H from Table 6. The main result of
interest is that the estimated effect of M&A percentage for the fund is somewhat weaker than in the
original Heckman specification.
VI. Conclusions
Evidence in this paper challenges the conventional wisdom that venture capital fund
performance is mainly driven by “home runs” in the form of successful IPO exits. While, among fund
outcomes, the percentage of successful IPO exits appears to be the most important one, the percentage
of exits by acquisition is almost as important. Also, aggressive use of abandonment options, as indicated
by the percent of a fund’s initial investments that do not receive later‐stage funding and do not exit
successfully, contributes positively to fund performance. These conclusions are robust to whether fund
29
performance is measured as fund IRR, fund cash‐on‐cash ratio (COCR), or a hybrid measure that
combines both and appears to be better aligned with fund NPV. The conclusions also are robust to
controlling for an array of potential biases, including selectivity in reporting, look‐back, survivorship, and
attrition bias.
While others have established persistence of venture capital firm performance over funds, the
question has largely remained open as to whether persistence is due to luck, skill, or some combination.
Because we are able to combine fund performance measures with fund outcomes, as well as firm‐level
prior outcomes, experience, and reputation, we are able to provide evidence that prior performance
matters, experience in the sector on which the fund is focused matters, and generic firm reputation
matters. We find some weak evidence that agility also matters, and we find evidence of style
persistence, in that the mix of exit types and aggressiveness in the exercise of abandonment options
persists over funds. The evidence is consistent with the hypothesis that performance persistence is at
least partly due to VC firm skill.
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N Mean Median Std Dev Skew
Fund Performance and Outcomes
IRR Net of Fees and Carried Interest (%) 1285 13.7 9.6 34.4 5.67
COCR: the Cash‐on‐Cash Ratio for Fund Investors (multiple) 1438 1.79 1.29 2.4 8.48
Pct. of Fund Cos. Rec'd. Next‐stage Funding (incl. IPO, MA) 1518 56.0 60.0 31.5 ‐0.43
Pct. of Fund Cos. that IPO 1518 14.3 6.3 20.0 2.04
Pct. of Fund Cos. Acquired 1518 12.6 9.1 15.7 2.43
Firm Outcomes
Cum. Pct. Of Firm Cos. Rec'd. Next‐stge Funding 1518 59.9 64.3 25.0 ‐0.63
Cum. Pct. of Firm Cos. that IPO (%) 1518 19.0 17.1 15.8 1.07
Cum. Pct. of Firm Cos. Acquired 1518 13.2 12.3 11.0 2.49
Cum. Pct. Rec'd. Next‐stage, Excluding Current Fund 1518 64.1 17.0 110.3 3.33
Cum. Pct. Firm Cos. That IPO, Excluding Current Fund 1518 17.3 15.0 17.0 1.02
Cum. Pct. Firm Cos. Acquired, Excluding Current Fund 1518 10.5 9.1 11.4 2.54
Fund Characteristics
Vintage Year (Year of First Captial Call or First Investment) 1518 1996.7 1998 5.8 ‐0.99
Fund is Designated Early‐stage by VX 1518 0.267 0 0.442 1.05
Total Amt. Invested by Fund (thousands) 1518 168885 61367 364962 6.20
No. of Cos. Invested by Fund 1518 17.0 12 17.3 1.92
No. of Fund Cos. Rec'd. Next‐stage Funding (incl. IPO, MA) 1518 11.2 6 13.3 1.93
Firm Experience
Fund Sequence Number for Firm 1518 4.93 3 5.14 2.69
First Fund (equals 1 if Firm has no prior fund) 1518 0.221 0 0.415 1.35
Cum. No. of Cos. Invested by Firm 1518 108.0 47 154.8 2.83
Cum. No. of find Cos. Rec'd. Next‐stage Funding 1518 75.3 26.5 115.5 3.12
No. of Firm's Prior Invstmnts. in same Sector as Present Fund Focus 1518 62.4 23.5 93.9 2.34
Present and Prior Fund have same Sector Focus (equals 1 if same) 1518 0.437 0 0.496 0.26
Firm Reputation
Firm Reputation 1 (Ranked as a top 30 VC Firm by Nahata) 1518 0.062 0 0.241 3.64
Firm Reputation 2 (Ranked as a top 25 VC Firm by IKMS) 1518 0.108 0 0.311 2.53
Benchmarks
Benchmark Average IRR Reported by Preqin (%) 1459 15.6 15.2 7.2 0.01
Vintage Year Average IRR Reported by VX (Avg) 1518 12.4 9.6 11.4 0.84
New Cap. Committed to VC funds over 4 quarters (millions) 1518 39433 24253 41375 1.48
Log of New Cap. Committed to Venture Funds (Rolling 4 Qtr Total) 1518 9.97 10.10 1.26 ‐0.99
Log of S&P 500 P/E Ratio at Exit (averag of years 6‐8 after start) 1518 3.23 3.26 0.16 ‐1.53
Controls
Inverse of Fund Age in 2007 (Equals 1/Fund Age in Years) 1518 0.138 0.111 0.097 2.03
Young Fund (equals 1 if Fund Vintage Year is after 1999) 1518 0.471 0.000 0.499 0.12
Log of (Vintage Year ‐ 1977) Missing if before 1978. 1515 2.92 3.04 0.41 ‐2.48
Table 1
Fund‐level Descriptive Statistics of Matched Sample
The matched sample includes 1518 funds where Venture Economics information on traditional US venture capital funds could be
matched with venture capital fund data from Preqin. Fund IRR and COCR are the primary performance variables in the analysis.
Percentages of next‐stage funding, IPO, and Acquired are based on the total number of companies in which the fund (or firm) invested.
Cumulative percentages excluding the current fund are base on all of the firm's funds that were launched prior to the subject fund.
Fund vintage year is reported by Venture Economics and normally is the year of the first capital call, which typically is the same as the
year of first investment. When first capital call date is not available, first investment is used. Fund sector focus is assigned by Venture
Economics based on their industry sector categories, and is determined by the percentage of investments made in a sector. Firm
Reputation 1 is from the Appendix to Nahata (2008) and is based on the dollar capitalization share of a VC firm in the IPO market,
cumulated until the end of the year 2005. Firm Reputation 2 is from Appendix B of Ivanov, Krishnan, Masulis, and Singh (2008) and is
based on averaging their annual IPO market shares over 1996‐2002. Benchmark and Vintage Year IRRs are respectively as reported by
Preqin and Venture Economics. New venture capital commitments are as reported by Venture Economics.
Obs Mean Obs Mean Obs Mean Obs Mean
Fund Performance and Outcomes
IRR Net of Fees and Carried Interest (%) 83 44.25 1202 11.60 55 53.37 456 11.56
COCR: the Cash‐on‐Cash Ratio for Fund Investors (multiple) 90 4.38 1348 1.62 63 5.04 518 1.43
Pct. of Fund Cos. Rec'd. Next‐stage Funding (incl. IPO, MA) 307 77.29 5899 58.77 179 73.74 1242 59.27
Pct. of Fund Cos. that IPO 307 24.74 5899 14.70 179 15.90 1242 10.97
Pct. of Fund Cos. Acquired 307 12.03 5899 11.31 179 13.01 1242 11.40
Firm Outcomes
Cum. Pct. Rec'd. Next‐stage, Excluding Current Fund 307 74.15 5899 33.22 179 80.04 1242 66.80
Cum. Pct. Firm Cos. That IPO, Excluding Current Fund 307 32.34 5899 10.88 179 33.90 1242 21.95
Cum. Pct. Firm Cos. Acquired, Excluding Current Fund 307 14.90 5899 7.72 179 17.80 1242 16.24
Fund Attributes
Vintage Year (Year of First Captial Call or First Investment) 307 1992.00 5899 1993.93 179 1998.25 1242 1998.93
Fund Age as of 2007 307 15.00 5899 13.07 179 8.75 1242 8.07
Fund Vintage Year after 1998 (binary) 307 0.32 5899 0.41 179 0.52 1242 0.60
No. of Cos. Invested by Fund 307 27.74 5899 11.72 179 20.09 1242 13.61
Capital Committed to Fund (millions) 296 314.46 5017 186.96 176 475.68 1195 403.17
Firm Experience
Fund Sequence Number for Firm 307 11.18 5899 3.09 179 15.34 1242 8.18
Cum. No. of Cos. Invested by Firm 307 367.16 5899 55.98 179 461.34 1242 161.84
No. of Firm's Prior Invstmnts. in same Sector as Present Fund Focus 307 217.85 5899 28.87 179 228.93 1242 76.16
Benchmarks
Vintage Year Average IRR Reported by VE (Avg) 304 13.57 5865 11.76 179 13.14 1242 11.71
Benchmark Average IRR Reported by Preqin (%) 91 10.04 1343 9.75 62 8.85 494 7.88
Log of New Cap. Committed to Venture Funds (Rolling 4 Qtr Total) 307 9.15 5899 9.59 179 10.23 1242 10.42
Log of No. of IPOs in year before Vintage Year 301 4.72 5848 4.86 179 5.15 1242 5.07
Log of Avg. No. of IPOs in years 6‐8 after Vintage Year 307 6.90 5899 6.96 179 7.07 1242 7.06
S&P P/E Ratio in year before Vintage Year 306 19.69 5887 21.41 179 24.68 1242 25.31
Avg. S&P P/E Ratio in years 6‐8 after Vintage Year 306 23.14 5893 23.82 179 26.96 1242 26.59
Subsample Comparisons Grouped by Firm ReputationTable 3
All Conventional US VC Funds Fnd No. > 3 and Vintage Yr. > 1989
Ranked Not Ranked Ranked Not Ranked
Comparison of Firm Reputation 1 funds with other conventiional US VC funds in the Venture Economics database. Firm Reputation 1 is based on Nahata (2008). To
mitigate the possible effects of look‐back and selection bias in the data, the first three funds of each VC firm and all funds with vintage years before 1990 are dropped in
some comparisons.
IRR
DecileIRR COCR
Pct Rec'd
Next Stage
Funding
Pct that
IPO
Pct
Acquired
Cum Pct Rec'd
Next Stage
Funding
Cum Pct
IPO
Cum Pct
Acquired
Sector
Experience
Firm
Reputation
1
Benchmark
IRR
Vintage
Year IRR
1 ‐22.9 0.46 62.5 4.4 13.6 53.8 14.5 11.7 46.4 0.031 1.0 12.1
2 ‐8.4 0.74 63.2 6.9 12.2 61.4 17.0 13.1 60.6 0.039 2.8 13.5
3 ‐1.8 0.94 59.3 9.3 11.5 54.1 15.7 11.9 52.7 0.054 4.3 14.2
4 3.2 1.17 58.0 12.0 12.0 50.6 16.6 12.0 49.4 0.078 7.9 15.1
5 7.9 1.43 52.1 15.8 12.1 48.0 18.4 9.5 60.0 0.031 9.4 14.9
6 11.3 1.64 51.4 17.1 13.3 44.3 18.4 8.5 75.8 0.070 9.9 15.0
7 15.3 1.88 55.8 21.0 13.6 40.8 16.4 9.6 54.7 0.039 12.5 17.4
8 21.0 2.13 53.8 18.6 15.6 41.2 16.6 9.2 55.3 0.047 15.0 17.0
9 31.0 2.82 52.7 23.2 13.2 45.8 20.3 8.5 75.1 0.055 17.4 18.6
10 81.1 5.18 70.5 26.0 19.0 59.8 27.0 10.8 124.1 0.203 20.2 19.1
Table 4
Average Fund Performance by IRR Decile
Statistics are averages for 1286 conventional US VC funds grouped into deciles on the basis of Preqin reported fund IRRs. COCR is the average cash‐on‐cash ratio
for each IRR decile. Fund outcome statistics are average percentages of fund initial investments. Firm outcomes are average percentages for the firm over all
funds prior to each subject fund. Sector experience is the number of company investments by the firm that are in the same sector as the subject fund, prior to
the subject fund. Firm Reputation 1 is from the Appendix to Nahata (2008). Benchmark and Vintage Year IRRs are respectively as reported by Preqin and
Venture Economics. New venture capital commitments are as reported by Venture Economics.
Fund Performance Fund Outcomes Firm Characteristics Market Benchmarks
IRR P-value IRR P-value IRR P-value IRR P-value IRR P-value IRR P-value COCR P-value IRR:COCR P-valueFund Investment Outcomes
Pct Rec'd Next-stage -0.097 0.014 -0.088 0.071 -0.063 0.133 -0.077 0.073 -0.063 0.134 -0.062 0.139 -0.0058 0.005 -0.0047 0.000Pct IPO 0.439 0.000 0.340 0.000 0.312 0.000 0.309 0.000 0.312 0.000 0.313 0.000 0.0183 0.000 0.0124 0.000Pct M&A 0.323 0.000 0.272 0.000 0.229 0.000 0.228 0.000 0.229 0.000 0.223 0.000 0.0114 0.001 0.0056 0.014
Firm PerformanceFirm Prior Pct Rec'd Next-stage -0.192 0.000 -0.185 0.001 -0.189 0.001 -0.185 0.001 -0.130 0.025 -0.0031 0.348 -0.0050 0.004Firm Prior Pct IPO 0.312 0.000 0.326 0.000 0.289 0.000 0.327 0.000 0.352 0.000 0.0071 0.104 0.0065 0.006Firm Prior Pct M&A 0.206 0.004 0.225 0.002 0.218 0.002 0.225 0.002 0.192 0.008 0.0070 0.280 0.0058 0.013
Firm ExperienceSector Experience 0.064 0.000 0.066 0.000 0.042 0.005 0.066 0.000 0.0013 0.166 0.0014 0.000Same Sector as Prior Fund (binary) -2.911 0.144 -2.131 0.324 -1.861 0.383 -2.133 0.299 1.314 0.505 -0.0253 0.859 -0.0557 0.324
Firm ReputationFirm Reputation 1 (binary) 22.694 0.013 2.2155 0.004 0.4071 0.011Firm Reputation 2 (binary) -0.016 0.997 8.892 0.010
Market ControlsVintage Year IRR (VE) 0.687 0.008 0.673 0.008 0.687 0.008 0.695 0.008 0.0153 0.302 0.0010 0.847Benchmark IRR (Perqin) 0.249 0.066 0.281 0.028 0.249 0.066 0.233 0.088 0.0291 0.000 0.0224 0.000Log New Capital Commit. to VC -0.840 0.372 -0.497 0.602 -0.840 0.369 -1.152 0.202 -0.0883 0.099 -0.0354 0.229Log S&P P/E at Exit 1.063 0.874 -0.382 0.954 1.063 0.875 -0.970 0.889 0.7839 0.221 -0.1411 0.521
Fund ControlsEarly-stage Fund 2.327 0.440 2.178 0.459 2.326 0.445 2.820 0.358 0.4779 0.023 -0.0520 0.482First Fund of Firm (binary) 2.366 0.460 0.948 0.757 2.365 0.455 3.617 0.264 0.0679 0.742 -0.0142 0.893Inverse of 2007 Fund Age 36.730 0.140 30.599 0.198 36.732 0.143 29.680 0.232 -3.0774 0.000 -0.5547 0.272Vintage Year after 1999 (binary) 5.177 0.435 5.139 0.427 5.178 0.433 5.778 0.384 -0.1508 0.637 -0.1398 0.310Constant 8.158 0.000 8.784 0.000 -8.564 0.682 -4.759 0.814 -8.561 0.682 0.329 0.988 -0.4921 0.815 0.9807 0.141
R-Square 0.064 0.099 0.144 0.164 0.144 0.129 0.187 0.247No. Obs. 1282 1282 1271 1271 1271 1271 1379 1191
Tests of Coef. EqualityPct IPO = Pct M&A 0.095 0.314 0.195 0.198 0.195 0.164 0.024 0.003Firm Pct IPO = Firm Pct M&A 0.135 0.177 0.336 0.169 0.036 0.994 0.781
Table 5
Model B Model C
Dependent variables are the Net IRR to fund investors as a percentage, the Cash‐on‐Cash Return as a multiple, and the sum of the natural logs of 1+ IRR in decimal form and the
COCR. Investment outcomes are percentages of companies in which the fund invested. Pct. Rec'd Next‐Stage includes IPO and M&A percentages. Firm performance measures are
cumulative percentages for all investments by prior funds of the same firm. Sector experience is number of prior investments in the same sector as the fund focus based on
Venture Economics sectors. Firm Reputation 1 is based on Nahata (2008), and Firm Reputation 2 is based on Ivanov, Krishnan, Masulis, and Singh (2008). Vintage year IRR is the
simple average of vintage year fund IRRs as reported by Venture Economics and Benchmark IRR is the value‐weighted average of vintage year IRRs as reported by Preqin, both in
percentage form. Coefficient P‐values are two‐tailed, based on robust standard errors. Tests of coefficient equality are P‐values for associated F statistics.
Model A‐1 Model A‐2 Model A‐3 Model A‐4 Model A‐5 Model A‐6
OLS Regressions of Fund Performance on Exit Success Percentages, Prior Firm Performance, Firm Experience, and Reputation
Coef. P-value Coef. P-value Coef. P-value Coef. P-value Coef. P-value Coef. P-valueFund Investment Outcomes
Pct Rec'd Next-stage -0.0038 0.000 -0.051 0.249 -0.0039 0.000 -0.0068 0.002 -0.0040 0.000 -0.0048 0.000Pct IPO 0.0052 0.000 0.275 0.000 0.0047 0.000 0.0197 0.000 0.0052 0.000 0.0125 0.000Pct M&A 0.0042 0.001 0.190 0.001 0.0037 0.002 0.0130 0.000 0.0042 0.001 0.0057 0.019
Firm PerformanceFirm Prior Pct Rec'd Next-stage -0.0059 0.000 -0.151 0.022 -0.0054 0.000 -0.0047 0.201 -0.0056 0.000 -0.0051 0.005Firm Prior Pct IPO 0.0065 0.001 0.246 0.002 0.0059 0.002 0.0089 0.064 0.0058 0.004 0.0067 0.006Firm Prior Pct M&A 0.0064 0.008 0.170 0.023 0.0061 0.010 0.0092 0.211 0.0059 0.015 0.0060 0.015
Firm ExperienceSector Experience 0.0019 0.000 0.030 0.081 0.0019 0.000 0.0018 0.096 0.0020 0.000 0.0015 0.001Same Sector as Prior Fund (binary) 0.0437 0.389 -2.159 0.300 0.0514 0.305 -0.0124 0.931 0.0449 0.381 -0.0548 0.334
Firm ReputationFirm Reputation 1 (binary) -0.2472 0.016 24.586 0.010 -0.2390 0.018 2.1343 0.006 -0.2388 0.021 0.4010 0.015
Market ControlsVintage Year IRR (VE) -0.0030 0.412 0.626 0.016 -0.0034 0.357 0.0176 0.267 -0.0025 0.509 0.0011 0.829Benchmark IRR (Perqin) 0.0040 0.254 0.317 0.012 -0.0014 0.679 0.0280 0.000 0.0030 0.395 0.0223 0.000Log New Capital Commit. to VC -0.0982 0.000 -0.368 0.707 -0.0929 0.000 -0.0917 0.087 -0.0905 0.000 -0.0358 0.226Log S&P P/E at Exit 0.1347 0.491 -7.079 0.384 0.2454 0.207 1.0682 0.091 0.1479 0.459 -0.1188 0.635
Fund ControlsEarly-stage Fund -0.2168 0.000 3.469 0.248 -0.2356 0.000 0.4237 0.060 -0.2447 0.000 -0.0562 0.470First Fund of Firm (binary) -0.8032 0.000 6.884 0.182 -0.7871 0.000 -0.1753 0.570 -0.7984 0.000 -0.0334 0.808Inverse of 2007 Fund Age -2.6118 0.000 31.513 0.189 0.7518 0.171 -3.2650 0.000 -2.3904 0.000 -0.5564 0.272Vintage Year after 1999 (binary) -0.0949 0.396 4.310 0.515 -0.2051 0.060 -0.0987 0.773 -0.0349 0.758 -0.1373 0.329Log Vintage Year minus 1977 1.0533 0.000 0.7727 0.000 0.9721 0.000
Selection VariablesFinancial Institution-Backed (binary) -0.3607 0.000 -0.3556 0.000 -0.3756 0.000Inverse Mills Ratio (selection) -9.641 0.106 0.3999 0.365 0.0311 0.851Constant -2.3520 0.000 25.200 0.359 -2.15 0 -1.7807 0.427 -2.3231 0.000 0.8814 0.307
Pseudo R-Square 0.132 0.131 0.130R-Square 0.166 0.188 0.247No. Obs. 5765 1271 5765 1379 5765 1191
Model C‐HSelection Model A‐4H Selection Model B‐H Selection
Net Internal Rate of Return (IRR) Cash-on-Cash-Ratio (COCR) IRR:COCR (Ln(1+IRR/100)+ Ln( OCR))
Table 6
Heckman Selection Models of Fund Performance on Exit Success Percentages, Prior Firm Performance, Firm Experience, and Reputation
Dependent variables in Probit selection models equal 1 if the related dependent variable is reported by Preqin. Dependent variables in bias‐corrected second‐stage Heckman models are the
Net IRR to fund investors as a percentage, the Cash‐on‐Cash Return as a multiple, and the sum of the natural logs of 1+ IRR in decimal form and the COCR. Investment outcomes are
percentages of companies in which the fund invested. Pct. Rec'd Next‐Stage includes IPO and M&A percentages. Firm performance measures are cumulative percentages for all investments
by prior funds of the same firm. Sector experience is number of prior investments in the same sector as the fund focus based on Venture Economics sectors. Firm Reputation 1 is based on
Nahata (2008). Vintage year IRR is the simple average of vintage year fund IRRs as reported by Venture Economics and Benchmark IRR is the value‐weighted average of vintage year IRRs as
reported by Preqin, both in percentage form. Log of Vintage Year ‐1977 is included in the selection equation to allow for the possibility that reporting likelihood increases in more recent years.
The variable is missing before 1977. The sample of traditional VC funds includes funds that are backed by financial institutions or their affiliates. Financial Institution‐Backed equals 1 for funds
backed by commercial, investment, or merchant banks or their subsidiaries or affiliates. The Coefficient P‐values are two‐tailed, based on robust standard errors. Tests of coefficient equality
are P‐values for associated F statistics.
Coef. P-value Coef. P-value Coef. P-value Coef. P-valueFirm Performance
Diff. in Firm Ln(MA) and Ln(IPO) 0.6430 0.044
Firm Prior Pct Rec'd Next-stage 0.541 0.000 0.003 0.889 0.006 0.705 0.0000 0.932
Firm Prior Pct IPO ‐0.003 0.939 0.312 0.000 0.070 0.007 0.0032 0.225
Firm Prior Pct M&A ‐0.052 0.359 ‐0.019 0.525 0.235 0.000 ‐0.0031 0.232
Firm ExperienceSector Experience ‐0.001 0.887 ‐0.003 0.486 0.000 0.999 0.0000 0.383
Same Sector as Prior Fund (bina 0.197 0.845 ‐0.364 0.610 0.916 0.145 0.0099 0.195
Firm ReputationFirm Reputation 1 (binary) 6.043 0.000 3.651 0.007 ‐0.735 0.414 ‐0.0349 0.006
Market ControlsVintage Year IRR (VE) 0.055 0.439 ‐0.151 0.002 0.155 0.000 0.0023 0.000
Benchmark IRR (Perqin) ‐0.161 0.016 0.157 0.000 ‐0.125 0.002 ‐0.0023 0.000
Log New Capital Commit. to VC 0.092 0.847 ‐2.863 0.000 0.485 0.086 0.0267 0.000
Log S&P P/E at Exit ‐2.884 0.341 ‐3.990 0.081 9.071 0.000 0.1089 0.000
Fund ControlsEarly-stage Fund 11.014 0.000 ‐1.191 0.026 0.877 0.097 0.0173 0.004
First Fund of Firm (binary) 29.314 0.000 3.156 0.013 4.347 0.000 0.0077 0.554
Inverse of 2007 Fund Age ‐106.979 0.000 ‐35.116 0.000 ‐49.119 0.000 ‐0.1270 0.028
Vintage Year after 1999 (binary) 2.155 0.339 ‐6.486 0.000 1.676 0.192 0.0626 0.000
Constant 45.505 0.000 56.595 0.000 ‐21.852 0.000 ‐0.6324 0.000
R‐Square 0.1743 0.2208 0.0547 0.1491
Difference in Fund
Ln(MA%) and
Ln(IPO%)
The usable sample included 5765 funds with complete data, from the Venture Economics sample of 6206 conventional US
venture capital funds. Dependent variables are fund investment outcome percentages for the subject fund, based on
companies in which the fund invested. Pct. Rec'd Next‐Stage includes IPO and M&A percentages. Also, the difference
between the fund's M&A percentage in logs (ln(1+M&A%/100)) and its IPO percentage in logs (ln(1+IPO%/100) is used to
test persistence of the proportion of acquisition exits to IPO exits. Firm performance measures are cumulative percentages
for all investments by prior funds of the same firm. Sector experience is number of prior investments in the same sector as
the fund focus based on Venture Economics sectors. Firm Reputation 1 is based on Nahata (2008). Vintage year IRR is the
simple average of vintage year fund IRRs as reported by Venture Economics and Benchmark IRR is the value‐weighted
average of vintage year IRRs as reported by Preqin, both in percentage form. Log of Vintage Year ‐1977 is included in the
selection equation to allow for the possibility that reporting likelihood increases in more recent years. The variable is
missing before 1977. The Coefficient P‐values are two‐tailed, based on robust standard errors. Tests of coefficient equality
are P‐values for associated F statistics.
Table 7
Regression Models of Firm Style Persistence
Percent Receiving
Next‐stage Funding
Percent of Fund
Companies that IPO
Percent of Fund
Companies that are
Acquired
IRR P-value IRR P-value IRR P-value IRR P-value IRR P-value IRR P-value IRR P-valueFund Investment Outcomes
Pct Rec'd Next-stage -0.051 0.249 -0.128 0.005 -0.067 0.188 -0.142 0.009 -0.053 0.171 -0.055 0.177 -0.053 0.200Pct IPO 0.275 0.000 0.301 0.000 0.338 0.000 0.362 0.000 0.283 0.000 0.272 0.000 0.271 0.000Pct M&A 0.190 0.001 0.272 0.000 0.228 0.001 0.314 0.000 0.115 0.119 0.151 0.022 0.165 0.009
Firm PerformanceFirm Prior Pct Rec'd Next-stage -0.151 0.022 -0.061 0.309 -0.148 0.079 -0.060 0.451 -0.125 0.023 -0.142 0.017 -0.147 0.018Firm Prior Pct IPO 0.246 0.002 0.180 0.026 0.235 0.009 0.170 0.080 0.210 0.005 0.219 0.008 0.228 0.005Firm Prior Pct M&A 0.170 0.023 0.118 0.104 0.156 0.080 0.119 0.180 0.164 0.027 0.176 0.019 0.175 0.019
Firm ExperienceSector Experience 0.030 0.081 0.029 0.091 0.047 0.068 0.045 0.093 0.032 0.055 0.033 0.051 0.032 0.060Same Sector as Prior Fund (binary) -2.159 0.300 -1.955 0.347 -2.384 0.299 -2.084 0.362 -2.425 0.211 -2.464 0.221 -2.363 0.247
Firm ReputationFirm Reputation 1 (binary) 24.586 0.010 26.155 0.007 32.040 0.019 34.028 0.016 25.290 0.009 24.981 0.009 24.846 0.010
Market ControlsVintage Year IRR (VE) 0.626 0.016 0.474 0.050 0.568 0.053 0.384 0.136 0.471 0.027 0.556 0.018 0.585 0.017Benchmark IRR (Perqin) 0.317 0.012 0.409 0.004 0.472 0.005 0.662 0.000 0.274 0.028 0.290 0.020 0.299 0.016Log New Capital Commit. to VC -0.368 0.707 0.319 0.781 -0.222 0.887 0.676 0.681 -0.386 0.687 -0.415 0.664 -0.410 0.669Log S&P P/E at Exit -7.079 0.384 -1.851 0.829 -17.783 0.214 -19.671 0.217 -7.806 0.298 -7.500 0.332 -7.346 0.350
Fund ControlsEarly-stage Fund 3.469 0.248 2.290 0.464 4.172 0.303 2.401 0.561 1.469 0.612 2.497 0.391 2.911 0.322First Fund of Firm (binary) 6.884 0.182 (dropped) 6.433 0.432 (dropped) 3.347 0.463 3.748 0.437 4.585 0.353Inverse of 2007 Fund Age 31.513 0.189 53.738 0.052 25.477 0.325 44.110 0.144 13.704 0.496 18.900 0.387 22.716 0.315Vintage Year after 1999 (binary) 4.310 0.515 -2.464 0.734 4.263 0.572 -2.841 0.735 5.174 0.341 5.131 0.391 4.876 0.433
Selection VariablesInverse Mills Ratio (selection) -9.641 0.106 -13.026 0.035 -8.490 0.455 -10.987 0.347 -9.056 0.103 -8.756 0.125 -9.017 0.119Constant 25.200 0.359 5.448 0.854 55.305 0.257 55.145 0.311 32.028 0.218 29.387 0.267 28.051 0.294
R-Square 0.166 0.212 0.185 0.236 0.1464 0.153 0.1573No. Obs. 1271 997 1080 851 1271 1379 1271
10‐Year
Table 8
Survivorship, Look‐back, and Attrition Bias Comparisons for Fund IRR Models
Model A‐H4 is repeated from Table 6. Look‐back and survivorship bias effects are assessed by dropping observations of first funds for each firm, dropping funds
launched before 1990, and dropping both. Attrition bias effects are assessed in Heckman selection models with attrition weights based on 6, 8 and 10‐year fund lives.
Dependent variables are the Net IRR to fund investors as a percentage, the Cash‐on‐Cash Return as amultiple, and the sum of the natural logs of 1+ IRR in decimal form
and the COCR. Investment outcomes are percentages of companies in which the fund invested. Pct. Rec'd Next‐Stage includes IPO and M&A percentages. Firm
performance measures are cumulative percentages for all investments by prior funds of the same firm. Sector experience is number of prior investments in the same
sector as the fund focus based on Venture Economics sectors. Firm Reputation 1 is based on Nahata (2008). Vintage year IRR is the simple average of vintage year fund
IRRs as reported by Venture Economics and Benchmark IRR is the value‐weighted average of vintage year IRRs as reported by Preqin, both in percentage form.
Coefficient P‐values are two‐tailed, based on robust standard errors.
Model A‐4H Drop 1st Funds Drop Before 1990 Drop Both 6‐Year 8‐Year
0%
10%
20%
30%
40%
50%
60%
70%
4 6 11 14 16 22 12 19 28 23 17 15 18 38 32 31 29 56 69 74 96 69 34 24 40 28
19801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005Centered 3‐year Weighted Average Percentage of Changes to Sector
Number of Sector Changes in Year/Year on which Average is Centered
Figure 1 ‐ Centered 3‐year Moving Average of Firm Changes of Fund Focus by Year of Change to Sector
(Weighted Averages of Annual Percentages of All Changes for Year)
To Computer Sector
To Internet Sector
To Biotech Sector
To Medical Sector
To Non‐technical Sectors
‐0.4
‐0.2
0
0.2
0.4
0.6
0.8
1 2 3 4 5 6 7 8 9
Regression Coefficient
Sample Quantile
Figure 2 ‐ Least Absolute Deviation ‐Quantile Regression Results for IRR
Pct Rec'd Next‐stagePct IPOPct AcquiredFirm Pct Rec'd Next‐stageFirm Pct IPOFirm Pct AcquiredSector ExperienceSame Sector as Prior FundFirm Reputation 1