Investment Risk Allocation and the Venture Capital Exit Market

Transcription

1 Investment Risk Allocation and the Venture Capital Exit Market Susan Chaplinsky a and Swasti Gupta-Mukherjee b a Darden Graduate School of Business, University of Virginia, Charlottesville, VA b Quinlan School of Business, Loyola University, Chicago, IL We examine how the reward-to-risk signaled by recent exits and failures of venture-backed companies affect venture capitalists risk allocations (proportion of early versus late stage investment), and returns. We show a sharply diminishing reward-to-risk over time that results in significantly lower allocations to early stage investments. Ceteris paribus, aggregate risk allocations and capital inflows have opposing effects on the returns to future exits. Conditional on exit, exit returns are positively (negatively) related to risk allocations (capital inflows) at the time of investment initiation. The negative effects on exit returns owing to lower risk allocations are a strong influence mitigating the positive effects of reduced capital inflows since the Dotcom collapse. JEL classification: G24 Keywords: venture capital, investment, risk, M&A exits, IPO exits First Draft: May 2010 Current Draft: August 2012 We thank Sean Carr, Richard Evans, Richard Fu, David Hsu, Robert Kolb, Elena Loutskina, Tom Nohel, Amiyatosh Purnanandam, members of the Darden Finance and Economics Research Workshop, Peter Barris of New Enterprise Associates, Jay Moorin of ProQuest Investments, and participants at the Batten Institute s Venture Capital and Innovation Summit in May 2011, and participants at the Kauffman Foundation Conference on Trends in Early Stage Financing in March 2012, Felix Meschke (discussant), for helpful comments and suggestions. Kulwant Rai and Evan Rust provided excellent research assistance. Author contact information: a. Susan Chaplinsky (corresponding), Darden Graduate School of Business, University of Virginia, Charlottesville, VA 22906, Tel: (434) , chaplinskys@virginia.edu b. Swasti Gupta-Mukherjee, Loyola University, Chicago, IL 60611, Tel: (312) , sguptamukherjee@luc.edu 1 Electronic copy available at:

2 1. Introduction Venture capitalists (VCs) have operated in a difficult exit market over the past decade. To date, the literature has focused on the amount of investment, generally referred to as capital inflows, as the predominant channel by which VCs respond to changing exit market conditions. Prior studies find that capital inflows are positively related to the strength of the exit market (Gompers, et al., 2008), and negatively related to future fund returns (Gompers and Lerner, 2000; Kaplan and Schoar, 2005; Harris, et al., 2012). The literature, however, has generally overlooked how VCs choose to allocate capital across investments that vary in risk ( risk allocation ) another potentially important aspect of how VCs respond to exit market conditions and how these choices affect future performance. In this study, we examine the changes taking place in the VC exit market and explore how they alter the risk allocation of investments. We find that, over time, VCs have significantly decreased the proportion of their funding to seed, start-up, and early stage companies (hereafter collectively referred to as early stage ), among the riskiest investments they make. This trend raises important questions for an industry whose reputation and returns rest on an ability to identify promising but often highly risky investments. The cyclical nature of the VC industry derives from variation in the strength of the exit market, which in turn produces variation in capital inflows, investment, and financial returns. Aggregate capital inflows and risk allocation are expected to have important, but opposing, effects on future financial returns. All else equal, reduced capital inflows should increase the likelihood of higher returns from exits, whereas less risky capital allocations should decrease the likelihood of higher returns from exits. Given that the cumulative impact of these two effects is not known, an important contribution of this paper is to attempt to empirically assess the relative importance of capital inflows and risk allocation on VCs investment performance. Risk allocation will be influenced by the signal of reward-to-risk that VCs observe from recent successes and failures in the exit market, where the perceived reward-to-risk increases with the ability of successful investments to absorb losses from failed investments. We use portfolio company exit outcomes as a means to associate a VC investment with a particular type of outcome. An exit return is 1 Electronic copy available at:

3 the aggregate return accruing to the group of VCs holding stakes in a portfolio company that has an initial public offering (IPO), sale or acquisition (hereafter referred to as M&A ), or failure as an outcome. Our sample of exit returns includes 1,436 IPOs, 1,222 M&A, and 4,468 failures from U.S. venture-backed companies that occur over 1986 to 2008, for which we are able to compute VC stakes and acquire sufficient data for our analyses. This framework based on disaggregated portfolio company-level tangible outcomes allows us to judge the value of exit ( upside ) and failure ( downside ) events as market signals reflecting exit market conditions that relate to the reward-to-risk of investment. Prior studies of VC performance typically use fund-level returns, which are cumulative returns that do not reveal the characteristics and timing of specific investments. 1 Further, the samples in existing studies generally end before 2000 and it is after this that the exit market experienced a severe downturn from the collapse of the Internet bubble ( ). To measure the signal of reward-to-risk, we develop the Exit-to-Failure Ratio (EXF), which is the ratio of the average dollars gained in the form of net money-out (gross realizations less investment) from exits, to those lost to failures or investments that do not achieve an exit, computed over rolling 12 month windows for each industry. EXF can intuitively be interpreted as the number of failures, on average, that are recuperated by an IPO or M&A exit. In comparison to alternative measures used in prior studies to assess the strength of the exit market (e.g., the volume of IPOs, or stock market performance), EXF captures both the upside potential and the downside risk of investments in addition to accounting for both forms of VC exit. Across the six major industries in our sample, nearly four failures on average are covered by an M&A or IPO exit, but this fluctuates from zero to over 12 in some periods. When the reward-to-risk signal (i.e., EXF) is perceived to be high (low), recent exits signal a more forgiving environment wherein VCs face a smaller (larger) penalty for mistakes, potentially leading to greater (lower) risk taking by VCs. 1 Prior studies of private equity frequently employ VC fund-level returns to assess the performance of the asset class (e.g., Ljungqvist and Richardson, 2003; Jones and Rhodes-Kropf, 2004; Kaplan and Schoar, 2005; Phalippou and Gottschalg, 2009; Robinson and Sensoy, 2011, and Harris, et al. 2012). 2 Electronic copy available at:

4 Although EXF exhibits considerable variation over time, several longer term trends in the VC exit market have affected the reward-to-risk on investment over the last decade. First, since 2000, the predominance of M&A exits has increased to the extent that in a recent survey, 73% of venture capitalists said they no longer viewed IPOs as the optimal exit strategy, and 59% believed the VC model had been permanently altered by the lack of IPOs. 2 Given that IPOs have long been considered the primary driver of wealth creation in the industry, the survey findings represent a fundamental change in VCs views about the forms of exit. 3 Our sample of exit returns shows that the mean return is 99.5% for M&A exits and 211.7% for IPO exits. Both forms of exit are capable of producing home runs and strikeouts, but IPOs more frequently generate the extremely high returns that help lift overall VC performance and cover losses from failures. Over time, the frequency of M&A exits has increased to where, over , the majority of exits are M&A and the majority of these have negative returns. These trends reduce the upside potential expected from exits and shift the return distribution to the left, implying, ceteris paribus, less coverage for failed investments (i.e., lower EXF) and less margin for error in VC decision-making. Second, there has also been an increase in the average funds lost to investments that do not pay off and result in 100% returns, also lowering EXF. As a consequence, VCs are likely to perceive lower reward-to-risk and be more concerned about downside risk and loss avoidance in making subsequent investments compared to periods with less constrained exit markets. Our main tests focus on how the signal of relative reward- to-risk from recent exits (EXF) affects the risk orientation of VC investments. VC risk allocation is measured by the proportion of funding provided to early stage investments within an industry. Our results show that the EXF computed from the exits in the prior 12 months is significantly positively related to the subsequent proportions of early stage 2 DLA Piper, Technology Leaders Forecast Survey, Fall As a consequence of the increase in M&A exits, concerns have been raised about the future of the industry and whether the VC model is broken. See for example Kedrosky (2009), Kaplan and Lerner (2010), and and 3

5 investments by VCs in an industry, and remains so after controlling for other indicators of exit market strength (e.g., NASDAQ, the number of industry IPOs), VC industry performance (IRRs), and capital inflows. Notably, relative to other frequently used measures of exit market strength, EXF has greater explanatory power and an economically significant effect on allocations to riskier VC investments. We next consider the important question of how VC risk allocations have changed over time. To conduct this analysis, we include a linear time trend (Time) and an interaction term (EXF Time) in the regressions explaining risk allocation. For the full sample, the coefficient on the interaction term EXF Time is significantly positive, indicating that the sensitivity of VCs risk taking to EXF increases over time. One interpretation of this result is that, in the more constrained exit market that followed the Dotcom collapse, VCs pay closer attention to market signals of upside potential relative to downside risk in making new investments compared to earlier periods. In addition, over the full sample period, the coefficient on Time is negative and significant, suggesting that the decline in the proportion of early stage investments over time is unexplained by market indicators and control variables. A nonparametric calculation shows that if the proportional allocations to early stage companies in the pre-dotcom period had continued over , approximately 1,200 additional early stage companies across industries could have received funding per year over When we decompose EXF into its components of average net money-out (Net Money-out) and losses in failure (Losses in Failures), both have the predicted signs the subsequent risk of investment is significantly positively related to net money-out and negatively related to losses in failure. Importantly, the results show that VC risk allocations have become less sensitive to signals of upside potential and more sensitive to downside risk over time. Over , the coefficient of Net Money-out is positive and significant, compared to where it has no significant influence on risk allocation. By comparison, over the same period, the coefficient of Losses in Failures is small ( 0.073) and insignificant in the early period, but increases to in the latter period and is highly significant. 4 We assume an average round size of $5 million per early stage investment. 4

6 We explore several alternative explanations for the changing risk allocations. If the establishment of startups is also positively related to the signal of reward-to-risk, then an alternative explanation for VCs allocating less capital to risky investments is that the capital demand of early stage start-up companies has declined. When we include a variable proxying for the number of startups seeking VC financing in the regressions, we find that the supply of startups is significantly positively related to the proportion of early stage financing, and otherwise the main results for EXF, EXF Time, and Time are similar. In addition, because fund sizes have increased over time, it has been argued that it is less economically viable for VCs to make smaller investments more typical of early stage funding. When we include the average round size in the regressions to control for changes in the size of investments over time, the main findings are unaltered, suggesting that growth in fund and investment size do not explain the changes we observe in risk allocation. If VCs risk taking decreases (increases), all else equal, future exit returns would be expected to decrease (increase) on average. Conditional on exit, we find a positive relation between overall VC risk allocations at the time of investment initiation and the future returns associated with these exits. For the full sample, regression analyses show that, ceteris paribus, a one standard deviation change in the proportion of early stage investments at the time of the first VC-backed financing round changes the median exit return of 50% by about 8.5% and the marginal effect increases to 16.3% over This suggests that risk allocation has a strong influence on the returns to future exits. Consistent with previous studies, capital inflows have a significant negative marginal effect on returns, which increases substantially over Consequently, all else equal, the decline in inflows experienced since 2000 has had a positive effect on increasing returns. Over , on a per industry basis, there has been approximately a 37% decrease in capital inflows and a 20% decrease in the proportion of early stage investments. Based on our regression estimates, the positive marginal effect of a 37% decrease in capital inflows on exit returns can be nullified by a 7% decrease in the proportion of early stage investments. Therefore, as VCs have responded to the signal of lower reward-to-risk, the resulting decrease in exit returns from lowered risk allocations is large 5

7 enough to offset the positive effects on exit returns from downsizing. Because there are important distinctions between fund and exit returns, which we later discuss in Section 2, care must be exercised in extending the findings for exit returns to fund returns. Nonetheless, the generally downward trend in exit returns over time suggests VCs must exhibit greater investment skill to be able to maintain fund returns. 2. Motivation and Hypotheses In this section, we first review the prior literature on how the exit market affects VC investments, and outline the basic differences between our empirical approach and existing studies. We then offer several hypotheses related to how the risk of investments should change in relation to VCs perceptions of the strength of the exit market, and how these factors affect future performance Related literature When venture capital is described as a cyclical business, it typically refers to the fact that capital inflows, investments, and financial returns are all heavily dependent on exit market conditions. Consistent with this, prior literature has found that signals from the exit market have a strong influence on VC decision making. Gompers and Lerner (1998) find, for example, that an increase in IPO valuations leads VC firms to raise more funds. Large inflows of capital give VCs the opportunity to put more money to work, so that capital inflows are positively related to the subsequent rounds of investment undertaken by VCs (Gompers, Kovner, Lerner, and Scharfstein, 2008). The amount of investment, in turn, influences the price of investments and the pre-money valuations of investments have been found to increase following periods of high capital inflows (Gompers and Lerner, 1998). As prices rise, however, there is a tendency for investments made in periods of high capital inflows to subsequently exhibit lower returns due to the money chasing deals phenomenon (Gompers and Lerner, 2000; Kaplan and Schoar, 2005; Harris, et al., 2012), where lower returns in turn reduce capital inflows. In sum, the literature to date has focused on capital inflows as the predominant channel by which VCs respond to changing exit market conditions. In response to recent concerns about depressed VC 6

8 returns, Kaplan and Lerner (2010) allude to the pro-cyclical nature of capital inflows as a reason to believe that the VC industry is not broken and will eventually be restored to greater health as this process plays out. The literature, however, has generally overlooked how VCs choose to allocate capital across investments that vary in risk ( risk allocation ) another potentially important aspect of how VCs respond to exit market conditions Measure of investment performance Prior studies of VC performance typically use fund-level returns, which are constructed as (since inception) Internal Rates of Return (IRRs), and multiples of invested capital (TVPIs) which ignore the time value of money. There are some basic differences between our measure of investment performance, Exit_Return, and IRR (or, interchangeably, TVPI), which we discuss in this section. Assuming that a VC fund i with an expected lifecycle of T time periods deploys all the capital in time period t= 0, the ex-ante aggregate expected fund return can be parsimoniously summarized using the following expression: (1) E( ),, E( ),, Here w i,j is the portfolio weight of company j in fund i s portfolio, and E(Outcome) i,j,t is the expected return associated with an M&A, IPO, or Failure of company j over the lifecycle of the fund. N is the set of portfolio companies in i s portfolio. The outcome for company j occurs in time period t, where t,, T. For simplification, we ignore the time value of money in assessing outcomes. Rewriting Equation (1) to match our empirical approach, we get: (2) E( ),, (, ),,,,, Here p f,t is the ex-ante probability of a failure, so that (1- p f,t ) is the ex-ante probability of a M&A or IPO exit ( success ) in the time period t in i s lifecycle of T time periods. Exit_Return i,j,t (Failure_Return i,j,t ) 7

9 is the estimated return to fund i from portfolio company j conditional on exit via M&A or IPO (Failure) in time period t. From Equation (2), exit outcomes are an important component of fund returns. Conditional on the occurrence of an exit, Exit_Return i,j,t measures the capital recovered from specific VC investments that exit as an M&A or IPO. Failure_Return i,j,t represents the dollars lost to failures, which we later assume to be 100% for empirical purposes. The disaggregated portfolio company-level tangible outcomes allows us to judge the value of exit ( upside ) and failure ( downside ) events as market signals capturing the variation in exit market conditions that relate to the reward-to-risk of investment. Because we do not estimate all the components of fund returns in Equation (2), our approach does not capture heterogeneities in skill across VC funds reflected in, for example, p f,t (the ability to identify failures) or w i,j (the ability to avoid failures and increase holdings in successes). On the other hand, to the extent there is a generally downward trend in investment returns (Exit_Return i,j,t ), it suggests VCs must exhibit greater investment skill to be able to generate superior fund performance. A priori, it is not clear from Equation (2) how capital inflows and risk allocation cumulatively affect the future performance of investments. Declining inflows have two reinforcing effects that elevate both fund returns and exit returns. All else equal, fund returns likely increase when inflows are low because VCs are more selective, which decreases the frequency of failures (p f,t ) and decreases the weight (w i,j ) assigned to lower quality investments. Similarly, exit returns likely increase because valuations are lower at initiation and the average quality of investments is higher when inflows are low. In contrast, the riskiness of investments could vary in the direction of its impact on the various components in Equation (2). First, less risky investments could increase fund returns because, all else equal, they tend to be later stage and have lower chance of failure (i.e., p f,t is lower). On the other hand, a greater proportion of less risky late stage investments should decrease the level of exit returns (Exit_Return i,j,t ) since valuations are higher at initiation. Therefore, conditional on exit, reduced capital inflows should increase future exit returns, whereas less risky capital allocations should decrease future exit returns. 8

10 2.3. EXF, risk allocation, and proposed hypotheses Prior research shows that VC fund returns are highly correlated with public market indices and measures of exit market strength based on IPO activity. But, as changes have occurred in the exit market, there are reasons to believe that inferences based on IPO activity alone might not fully measure the reward-to-risk on investment. In Figure 1, we plot the natural log of annual gross realizations to VCs from IPOs and M&A exits, and the average money invested in failed companies over 1986 to One observes in the figure that M&A exits contribute a significant and increasing proportion of realizations from VC exits over time. This is consistent with the results in Chaplinsky and Gupta-Mukherjee (2012) and Smith, et al. (2012) that show M&A exits make up a substantial fraction of total VC realizations. In addition, the measures of exit market strength used in prior studies do not account for failures and related downside risk, whereas Figure 1 also shows a large increase in the average losses from failures over time. 5 Prior studies find that failures of VC-backed companies (Sahlman, 1990) and nonexits from portfolio companies (i.e., living dead as described in Ruhnka, Feldman, and Dean, 992) occur in high enough frequency to suggest that this risk is not inconsequential to VCs and likely influences investment decisions. For example, in Valliere and Peterson s (2004) ethnography of the VC investment process, they state that Successes and failures of investments by other VC firms can be noted and analysed for intelligence about competitive conditions and the dynamics of the market sector in which the investee companies operated. To capture the dynamics of the exit market based on tangible outcomes observed by VCs, we construct an Exit-to-Failure Ratio (EXF) which intuitively measures, relative to breakeven, the ratio of the average dollars gained from exits to the average dollars lost to investments that do not pay off. When EXF is high, VCs can take more risk because fewer successes are necessary to produce acceptable returns 5 As we later discuss in more detail, we use the average cost of failures to measure downside risk rather than the total dollars lost in failures because of concerns about underreporting of the number of failures. 9

11 and when EXF is low, mistakes are more difficult to recover from and VCs are likely to reduce their risk exposure. These notions lead to our first hypothesis: Hypothesis 1. An industry s Exit-to-Failure Ratio (EXF) is expected to be positively related to VCs willingness to invest in riskier assets. Cochrane (2005) finds that VC returns are characterized by high positive skewness. All else equal, the greater the returns from successful investments and the degree of positive skewness, the more at liberty VCs are to deemphasize downside risk in making investment decisions. As the number of IPOs and the potential to achieve homerun exits diminish, downside risk should play a more prominent role in shaping investment decisions. On the other hand, it could be that VCs with reputational capital at stake have always considered downside risk and weighed the potential for success (net money-out) against the potential cost of failure in making investment decisions. One way to gauge this is to examine the sensitivity of VC investment decisions to net money-out and failures, and the extent to which this sensitivity varies over time. This leads to our second hypothesis: Hypothesis 2. All else equal, the proportion of investment in riskier assets is expected to be positively related to net money-out and negatively related to the cost of failures in an industry. If the effects of upside potential and downside risk on VC risk allocation are constant through time, we expect the coefficients of net money-out and the cost of failure to have the same sign and general level of significance over time. In the finance literature, risk is positively related to returns, which leads to our final hypothesis: Hypothesis 3. All else equal, future exit returns from new investments in an industry are expected to be higher on average when a larger proportion of investments in the industry are invested in riskier assets. As noted earlier, it is not clear how capital inflows and risk allocation cumulatively affect the future performance of investments. The later analysis of Hypothesis 3 sheds light on this issue. 3. Sample of Outcomes and Exit Returns 3.1 Sample description 10

12 This section describes the sample used to construct the returns from venture-backed companies. Our primary source of data on VC investments is Thomson Reuters s Venture Economics (VE) database. Because previous papers have raised concerns about both the self-reported nature of VC data, where it is plausible that under-reporting of negative outcomes creates a selection bias in the sample of companies in VE, and the completeness of VE data, we supplement the VE data with the Thomson Reuters s SDC Platinum New Issues database, SDC Platinum Mergers and Acquisitions (M&A) database, U.S. Securities Exchange Commission (SEC) EDGAR filings, and hand collection of data. Our initial sample consists of all U.S.-based portfolio companies with reported investments from VC firms that had final outcomes during the period 1986 to 2008 resulting in (1) mergers, acquisitions or buyouts (categorized broadly as M&A exits); (2) initial public offerings (IPOs); or (3) failures. In order to calculate returns from M&A or IPO exits, we require that the companies have a reported deal value (for M&A) or offering information (for IPOs), an exit date, and post-money valuation data. The exit date for an M&A exit is the effective date of the deal, and for an IPO exit is the offering date. The failures are companies listed as bankrupt (Chapter 7 or Chapter 11), defunct, or classified as living dead (those listed as active but without any financing rounds for at least five years as of December 2008). 6 Of the 4,468 total companies identified as failures in our sample, 126 are bankruptcies, 1,869 are defunct companies, and 2,473 are living dead. For failures, we assume that VCs lose their entire investment, i.e., earn a -100% return. For the empirical analyses, it is also important for us to capture the timing of failures. For bankruptcies, the exit date is the date of bankruptcy filing and for defunct or living dead investments, we assign an exit date that is five years from the date of the last reported financing round. We discuss the robustness of alternative scenarios in a later section. Due to the differences in the scope of reporting requirements, we lose more M&A observations from the initial sample because of missing data compared to IPOs. Missing M&A data results primarily 6 As we discuss in more detail later, to ensure that failures represent actual investments we restrict the sample to include only those companies that have two or more rounds of investment. 11

13 from two sources missing interim postmoney valuations data and, to a greater extent, missing disclosed deal values. We lose M&A observations in cases where private acquirers (who do not face mandatory disclosures) choose not to disclose the deal value, and because the deal value is required to be disclosed only for larger deals for public acquirers; for IPOs, it is required by the SEC to be reported for all deals. 7 To supplement and verify the VE data on M&A, we gather additional information from the SDC Platinum M&A database, and hand collect data from the press releases and financial statements of firms frequently listed in VE as acquirers (e.g., Intel, Cisco, Lucent, and Google). Through these additional sources, we are able to fill in approximately 400 missing M&A deal values (i.e., around one-third of our final sample) where the acquirer publicly disclosed the deal value but it was not recorded in VE and are able to verify the deal values where we find overlapping observations. 8 For IPOs, we augment the VE data with data from SDC Platinum New Issues database and SEC EDGAR S-1 filings. EDGAR contains IPO S-1 filings starting in 1994, so that our search is restricted to the period thereafter. We use these additional sources to confirm the VE IPO offering (exit) date, and to fill in missing exit dates and other terms of the offering. The use of several data sources helps to ensure greater accuracy and completeness of the VE data and allays some of the concern about self-reporting. Taken together, these steps result in a potential sample of 1,316 M&A exits and 1,447 IPO exits. To be able to estimate returns for M&A and IPO exits, we next require estimates of VCs equity stakes in a portfolio company. For this, we require data on postmoney round valuations for consecutive rounds and the amount invested in a round. A total of 1,222 M&A exits and 1,436 IPO exits have sufficient postmoney valuation data to calculate returns. Eighty-eight percent of the companies reporting postmoney valuations in our sample have complete data across all rounds with 98% missing not more than one postmoney round valuation. A previous study that uses postmoney valuations is Korteweg and 7 Deal values must be disclosed to the SEC if a private company is purchased by a publicly listed firm and, depending on the form of payment and year of sale, it represents more than 10% to 20% of its assets (Rodrigues and Stegemoller, 2007). 8 If we find discrepancies in deal values, we change the VE reported value to reflect the information in a company press release or financial statement or to that reported in SDC because it is more likely from public disclosures. 12

14 Sorensen (2010), which obtains its data from Sand Hill Economics, a proprietary database, and attempts to verify the accuracy of the data. Over the period 1987 to 2005, they report 1,001 IPO exits and 923 M&A exits, suggesting our sample has good coverage of the firms with postmoney valuations. Since the requirement of both postmoney data and deal values at exit results in a greater loss of M&A exits than IPO exits, a key issue is how this loss of observations affects the calculated returns for M&A exits. In the Appendix, we compare characteristics of the sample of M&A exits with postmoney values with and without disclosed exit values. Our sample with available exit valuations accounts for nearly 62% of the total dollars invested in portfolio companies exited by M&A. Qualitatively, the two samples are similar in characteristics, especially in terms of medians, but M&A exits with disclosed deal values have more capital invested on average than those without disclosed deal values. Although, all else equal, higher invested capital reduces the potential return, a likely scenario is that larger amounts of invested capital are associated with higher disclosed deal values. Since a primary concern with M&A exits is that they have lower returns on average than IPO exits, the exclusion of smaller M&A deals likely leads us to overstate the returns to M&A exits. Table 1 reports the annual frequency of M&A and IPO exits. One observes a sharp rise in the number and frequency of M&A exits over time. Several reasons have been advanced for the rise in M&A exits. Analysts have attributed part of the increase in M&A exits to a greater reluctance of companies to go public following the passage of the Sarbanes-Oxley Act in Since SOX was prompted by some of the excesses of Dotcom era IPOs, some observers have suggested that investors appear less willing to invest in riskier companies with negative cash flows, seemingly making IPO exits harder to achieve (Patricof, 2009). Others point to more macro factors and suggest that many of the industries that were in their infancy in the mid-90s have matured, and continued investment in these sectors has lower growth potential. Finally, there has been a sharp rise in the total dollars allocated to private equity over time, such that there may be more lower quality companies funded with less potential to go public. In Table 2, we provide descriptive statistics on the M&A and IPO exits and failures in our sample. Statistics are reported for the overall sample and for the six major industry groups used by VE. 13

15 In Panel A, for the overall sample of exited companies, a typical exit occurs after approximately five rounds of investment in which 7.4 VC firms participate. The round amount averages $14.1 million and the average total dollars invested per company ( money-in ) is $57.5 million. 9 The largest number of exits (and failures) occurs in the computer-related industry group and otherwise there is a good representation of exits across the six industries. For the overall sample of failures in Panel B of Table 2, the companies receive an average of 3.4 rounds of investment in which 4.0 VC firms participate. Not surprisingly, the average number of rounds for failures is less than the average for exited companies. VCs can usually monitor the intermediate success of companies after the initial rounds, and poor performers are less likely to receive further rounds of financing. The failures receive an average round amount of $6.5 million and the average money-in per company is $21.4 million, both averages being notably lower than for those for exited companies. 3.2 Construction of Exit Returns In this section, we describe our construction of exit returns and then examine the distributional properties of IPO and M&A exits. We derive estimates of VCs equity stakes in portfolio companies using the VC method of valuation, which posits a simple relation between the premoney (PRE) and postmoney (POST) values of a portfolio company for each round of investment (I) by VCs. 10 We assume before the first round investment that founders of the firm hold 100% of the equity ownership. For each round k, the fraction of ownership sold to VCs (F) is measured as F k = I k POST k. Each round of investment subsequent to the first round results in a larger cumulative share of the company being sold, as the first round investors stakes are diluted by additional equity sold to the subsequent round investors. 9 The average money-in is higher than the $31.4 million Nahata (2008) reports for successful exits because our sample extends to 2008 (versus 2001 in Nahata, 2008), and investments made after 2001 involve larger amounts of investment. 10 See Smith and Smith (2004) for a discussion of the venture capital method. Korteweg and Sorensen (2010) use this method to assess the risk and beta of venture investments. 14

16 To capture this effect, we calculate the total percentage of ownership stake sold to VCs ( for each portfolio company j that exits at time t after N financing rounds in the following manner: 11 ( ( (3) Using this approach, we find a first round investor in a portfolio company that eventually exits via an M&A has a 29.5% equity stake on average compared to a 24.9% equity stake for a first round investor in an IPO exit. At exit, VCs collectively hold 50.7% on average in companies exited by M&A and 56.7% in companies exited by IPOs. Because the accuracy of the VC stakes computed from VE postmoney valuations are critical to our analysis, we compare the VC stakes in IPO exits to a matched sample of stakes gathered from EDGAR S-1 filings. We search the S-1 filing of each company to identify the primary stakeholders ( Principal Stockholders ) for a match with venture capital firm names obtained from VE. When a match is found, we note the percentage of equity ownership the VC holds in the portfolio company, obtaining 4,194 VC firm-company level stakes in the process. We then aggregate the stakes of all VC firms linked to a company to obtain a measure comparable to our construct of. There is no statistically significant difference in the stakes estimated from the S-1 filings and VE postmoney valuations for IPOs. Although we do not have a comparable means to verify the stakes in M&A exits, the additional steps taken to confirm IPO equity stakes bolsters confidence in our estimated VC equity stakes. Having computed the total stakes held by VCs at exit for each portfolio company, we next compute the exit return (Exit_Return j,t ), the aggregate return accruing to the group of VCs holding stakes in a given portfolio company j that results in a M&A or IPO exit at time t, as follows: For example, assume that the fraction of equity stakes acquired by VCs in the first round is 30% and in the second round is 20%. The aggregate VC stakes at exit (accounting for dilution) will be calculated as VC% t,j = 30% (1 20%) + 20% = 44%. 15

17 VC % t, j Disclosed transacti on valuefor portfoliocompany j (M & A), -1 Totalamount investedin portfoliocompany by VCs Exit_Return j t j VC % t, j Pre- IPOshares IPOoffer pricefor portfoliocompany j (IPO), -1 Totalamount investedin portfoliocompany by VCs Exit_Return j t j (4) (5) Following Cochrane (2005), we use the offer price in calculating IPO returns to abstract from the issue of underpricing, which has been the subject of extensive discussion elsewhere in the literature. Bradley, et al. (2001) and Ofek (2001) find strong positive stock price performance between the offer date and IPO lock up expiration dates, which suggests the offer price assumption likely understates IPO returns to VCs. The exit returns we use are similar to the widely used investment multiple, (TVPI 1). In our construct, a 0% return indicates break even, i.e., the investment returns the capital invested in the portfolio company by VCs. These returns are more consistent with gross returns they are not net of carry or fees paid from distributions, although in the early years invested capital is after fees Exit Returns from IPO and M&A In Table 3, we report in Panel A exit returns (Exit_Return) for the sample of M&A and IPO exits. To examine the range of exit returns, we pool the overall sample of returns and sort them into three groups: Low (bottom 25%), Medium (middle 50%), and High (top 25%). The groups separate the middle 50% of returns, or most likely outcomes, from the extremes. The mean return to VCs from M&A exits is 99.5% compared to 211.7% for IPO exits, a statistically significant difference of 112.2%. Both forms of exit are highly skewed with means substantially exceeding the median returns ( 31.5% for M&A; 109.7% for IPOs). To account for the time taken to achieve an exit, we also compute in Panel B an annualized return (r) derived from the relation (1+ Exit_Return) = (1+ r) t, where Exit_Return is the 12 Note that this Exit_Return j,t measure is closely related to the Exit_Return i,j,t construct discussed in Section 2, with the main difference being that Exit_Return i,j,t is the return to a particular VC fund i, whereas Exit_Return j,t is the aggregate return to all VCs holding stakes in company j at exit. 16

18 company-level exit return and t is the number of years to exit. Consistent with results for Exit_Return, the average annual return for M&A exits of 25.0% is significantly lower than the average annual return of 55.0% for IPO exits. Having shown that the findings are robust to adjustments for time to exit, in our subsequent analyses we rely on the exit return construct because it provides a straightforward interpretation of returns (i.e., how much capital beyond breakeven did investors get back). 13 To examine the frequency of outliers, and upside and downside potential of exit returns, we compute High Low which summarizes the extremes in returns and is the difference between the mean and median returns in the top and bottom 25% of the sample. Both M&A and IPOs in the lowest 25% of the pooled returns have mean and median returns on the order of 80%, so that the difference in High Low of 543.3% for M&A exits and 358.0% for IPOs is generated by the top 25% returns. While some M&A exits are capable of producing extremely favorable outcomes, the frequency of M&A exits is much lower in the High group (32.7%) compared to the Low group (75.9%). This confirms the common suspicion that many M&A exits serve as last resort exits. In general, the results show that IPOs are more likely than M&A exits to generate capital sufficient to cover losses on subpar exits and failures Chronology of exit returns As VCs are likely to draw on past experience in making investment decisions, in Table 4, we examine the returns to M&A and IPO exits across three periods of time: (Pre-Dotcom), (Dotcom ), and (Post-Dotcom). The chronology of the three periods highlights the nature of the changes VCs have observed in the exit market over time. We sort the exit returns in each of the three periods into groups representing the top 25% (High), bottom 25% (Low), and the middle 50% (Medium) of returns. Apart from the Dotcom period, the median return to M&A exits is negative, and in the Medium (most likely) group the median return is at best zero. While a large literature focuses on the high 13 The subsequent results are not sensitive to whether returns are measured using exit returns or annualized returns. 17

19 level of IPO activity and returns in the Dotcom period (e.g., Ritter and Welch, 2002), M&A returns are also substantially higher in this period. The returns in the Medium group in the Dotcom era are virtually identical for M&A and IPOs, with a mean (median) of 197.2% (172.8%) for M&A and 198.7% (175.1%) for IPOs. The High Low range exceeds 800% for both forms of exit. Although the Dotcom period is remembered as the Golden Age of IPOs, it might better be referred to as the Golden Age of M&A exits. In the post-dotcom period, two important changes occur in the exit markets that should affect the perceived signal of reward-to-risk relative to prior periods. First is the sharp reduction in the proportion of IPOs, where only 36.6% of total exits occur by IPOs over While the overall mean return for IPOs of 170.5% compares favorably to the pre-dotcom period, the low frequency of IPOs suggests fewer dollars in total are recovered. The second observation is the highly negative nature of M&A exits, where, in the Medium group, the mean and median returns for M&A exits are negative. The overall mean M&A return over drops to 22.4%, and the majority of M&A exits (68%) have negative returns. Since exits provide a tangible sign of rewards in the VC industry, the infrequency of IPOs combined with their relatively lower maximum returns suggests less upside potential is signaled by recent exits. 3.3 Exit returns and market conditions In Table 5, we report the properties of M&A and IPO exit returns across exit market conditions. In relation to Table 4 where we anchor the time periods around the Dotcom bubble, here we use a commonly employed public market index to gauge the strength of the exit market in a more general setting. In Panel A, we examine equally weighted returns of the M&A and IPO exits occurring in a quarter in relation to the returns on the NASDAQ for the overall sample, and for the sample excluding exits that occurred in the Dotcom period. 14 Each quarter is categorized into hot, normal, and cold market 14 We also construct returns on a value-weighted basis in each quarter to assess the relative size of investments made at a particular time. For example, a $50 million investment in a particular company may be large in the abstract but the exit-quarter portfolio weights control for the size of the investment made in relation to others at the same time. The results are qualitatively similar to those reported for equally weighted returns. 18

20 conditions based on the six-month cumulative NASDAQ returns ending with the last month of the quarter. Hot markets are the top 25% of quarters (mean NASDAQ return = 39%), normal markets are the middle 50% of quarters (mean NASDAQ return = 7%), and cold markets are the bottom 25% of quarters (mean NASDAQ return 9%) of all quarters in the sample period. The results in Panel A of Table 5 show that, consistent with earlier studies (e.g., Ball et al., 2011), a disproportionate number of IPO (M&A) exits occur in hot (cold) markets. Consistent with earlier results, regardless of market conditions, the mean return for IPO exits exceeds that of M&A exits by a wide margin, and significantly so in normal and cold markets. In Panel B, we decompose the returns into their components of VC stakes, money-in, and moneyout. For the overall sample, IPO exits achieve high exit realizations in cold markets ($395.1 million) and hot markets ($335.0 million) compared to $151.5 million and $390.5 million, respectively, for M&A exits. So, the steadiness of high IPO returns is due largely to the fact that money-out (gross realizations) is less dependent on the strength of exit market conditions compared to M&A exits. The results also show that VCs invest greater amounts in portfolio companies that exit as IPOs than M&A. All else equal, this is consistent with VCs investing more in companies they perceive to have a higher likelihood of an IPO exit, or that less funds are needed to develop a company to achieve an M&A exit, a result that is consistent with Bayar and Chemmanur (2009). In Panel A, the exclusion of the exits from the peak in the Dotcom period materially reduces the mean and median returns for both forms of exits and, consistent with Table 4, considerably more so for M&A exits. In all market conditions, the median returns for IPOs are at least 3.5 times those of M&A. Further, in Panel B, absent the Dotcom period exits, the money-out from M&A exits averages about $100 million across all market conditions. To the extent that M&A exits become the norm with respect to the likely avenue of exit, it should influence the reward-to-risk VCs perceive on investment by limiting the upside potential of exits. 4. Impact of EXF on VC Investment 19

21 Because we are interested in how the trends in the VC exit market affect investment decisions, our empirical approach requires a high quality signal of recent exit activity that captures the reward-torisk signal perceived by VCs. In this section we describe the construction of our measure of the rewardto-risk tradeoff, EXF, and then examine its relation to capital inflows and risk allocation. 4.1 Construction of EXF To account for both upside potential of rewards and downside risk of losses, we construct an Exit-to-Failure Ratio (EXF) that reflects the risk-reward profile of the investment environment in an industry. For an industry g in month m, the measure is constructed as:, Avg. net money out from exits in industry during ( 2) to ( ) Avg. money invested in failures in industry during ( 2) to ( ) (6) where Avg. net money-out from exits is the mean of the exit values accruing to VCs net of the amount invested in exited companies in industry g during the 12 months preceding month m. Avg. money invested in failures is the average amount invested in companies in industry g that were recorded as failures during the 12 month period. 15 Intuitively, EXF is constructed as an indicator for how many failures on average an exit recuperates. Because we want the EXF ratio to be similar to a coverage ratio, if the value of net money-out from exits is not positive, we truncate EXF at a minimum level of zero. If there are trends in the exit market, such as lower exits returns or greater downside risk, EXF captures these effects and allows their timing to be observed prior to investment decisions. In doing so, it provides a signal of realized outcomes that is more reflective of recent exit market activity compared to other measures of VC industry performance like IRR, which is a cumulative measure of realized and unrealized investments since fund inception. 15 In unreported robustness checks, we estimate results using alternative windows to the 12 months preceding month m, ranging from 18 to 36 months. As the results are not sensitive to the length of window used, for brevity's sake we report only the results for the 12 month window. 20

22 We compute EXF for the six major industry categories used by VE for several reasons. First, it is well known that VCs tend to focus on particular industry sectors and gain expertise with the technology, product cycles, market dynamics, regulatory processes, and key personnel through repeated investments in an industry. Knowledge of an industry can be an important factor affecting a VC s decision to invest. Second, by computing EXF on an industry basis we account for potential differences in exit values and funding considerations across industries (e.g., size, capital-intensity, and scale requirements). To give a sense for how EXF tracks against a well-known public market index, in Figure 2 we plot the annual EXF (averaged across all industries) against the cumulative ending value of NASDAQ. The average value of monthly EXF is 3.62 and ranges from a low of zero to a high of EXF averages 3.62 in the pre-dotcom period, 6.25 in the Dotcom period, and 2.63 in the post-dotcom period. The peak of NASDAQ falls in the Dotcom period (correlation between NASDAQ and Dotcom dummy = 0.45), which is also the period where the highest values of EXF are realized. 16 Since the self-reporting bias is such that VCs are more likely to disclose information about successes than failures, our chief concerns about the data for failures are misreporting (e.g., understatement of actual investments in failures) and a potential underreporting of the incidence of failures. To minimize the effects of potential misreporting, we only consider the subsample of failures that have had at least two rounds of financing reported in VE to set a threshold for economically meaningful investments. 17 To address concerns about a potentially low frequency of reported failures, the denominator of EXF is the average amount invested in companies that failed in an industry over the previous 12 months. In this construct, a reasonable number of failures in an industry (something outside of a near zero occurrence) produces an average value that is relatively less sensitive to potential under- 16 Based on Figure 2 the average EXF appears highly correlated with NASDAQ especially at its peak, but for the overall time series, the correlation between NASDAQ and EXF over matching windows is We also consider alternative thresholds aimed at a similar purpose, such as restricting the subsamples of failures to companies that have had at least $1 million, $5 million, or $10 million in total capital invested. The restriction to have two or more rounds substantially decreases the number of failures from 12,856 with one round to 4,468 with two or more rounds. However, the subsequent results are not sensitive to the choice of failure sample, and we report the results based on the minimum two financing rounds criterion as our primary construct. 21

23 reporting of failures than alternative constructs that depend on the frequency of failures (e.g., sum of losses in failures). Due to the more limited failure and postmoney data in the earliest sample years, in the remainder of this paper when EXF is a variable of interest, we restrict the sample period to Finally, to prevent the highest values of EXF from unduly influencing the results we winsorize the top 5% of the computed EXFs. 4.2 Characteristics of failures Because prior studies typically use VC fund level returns, which cumulate the incidence and cost of failures into an overall fund return, failures have not been extensively studied in the VC literature. Defunct companies and bankruptcies (assuming no capital recovery) are companies VCs have written off or declared bankrupt, on which the returns are 00%. Some living dead companies that are later liquidated could end up with returns greater than 00% but, for our purposes, the issue is at what point does a prolonged absence of financing activity signal decreased return prospects to VCs? As discussed earlier, our main definition of living dead is based on the previous literature (i.e., five years without an investment), but the results are not sensitive to alternative definitions of living dead and failure date based on four, six, or seven years without an investment round. Intuitively, these alternatives change the number of firms classified as failures but, because EXF is constructed to be relatively insensitive to the frequency of failures, the results remain similar for different classifications of living dead. As shown earlier in Figure 1, there has been a sharp increase in the average cost of failure over time, which also potentially influences VCs risk allocation. The rise in the average cost of failure results from a large increase in the average round amount invested in companies that do not achieve an exit. For example, the average round amount invested in companies increases from $1.8 million for companies that fail during , to $7.2 million for companies that fail during As the expected cost of unsuccessful outcomes increases, it elevates downside risk in evaluating subsequent investments. 18 The results are similar if observations from the entire sample are used. 22

24 The results for failures parallel the findings for exited companies. In unreported results, we find a large increase in the average money invested in exited companies during the post-dotcom period. For M&A exits, average money-in increases to $61.1 million in the investments initiated in the post-dotcom period relative to $21.9 million and $29.6 million in the pre-dotcom and Dotcom periods, respectively. For IPOs, money-in averages $103.7 million in the investments started in the post-dotcom period relative to $47.9 million, and $71.2 million in the pre-dotcom and Dotcom periods, respectively. As a consequence, VCs may need to keep more capital in reserve to manage previously made investments, thereby decreasing the amount of capital available for new investments. The necessity of investing more money in both successful and unsuccessful investments, coupled with a longer average time horizon to exit (Table 4), is symptomatic of a difficult exit market. In addition, there is a sharp increase in failures in , which can be attributed to a large extent to an over-hang of investments made in the Dotcom period. Relative to earlier periods, all types of failure (bankruptcy, defunct, and living dead) occur in substantially higher frequency in A total of 69% of the investments that fail during this period received an initial investment during Living dead, which make up the largest number of failures, are companies listed by VCs as active investments. The large number of active companies in which VCs continue to invest time and effort (if not money) to yield an exit is another consequence of a difficult exit market, and might prompt more investment in later stage companies that require less monitoring and involvement. Therefore, losses from failures are broadly related to several challenges VCs face from variation in exit market conditions that affect the reward-to-risk on investment. 4.3 Capital inflows Our first regression analysis in Table 6 examines the explanatory power of EXF in relation to previous research that finds capital inflows are positively related to public market indices and the number of IPOs (Gompers, et al., 2008). The dependent variable in Table 6 is the capital inflows into an industry measured on a rolling basis over 12 month forward-looking windows, computed in each month m as the 23

25 natural logarithm of the total amount invested in the industry over the next 12 months m+1 through m+12. All specifications include industry fixed effects that capture industry-specific heterogeneities, and use standard errors that account for clustering by time. The independent variables include several signals of the strength of the exit markets similar to those used in prior studies, specifically, the cumulative monthly percentage return on the NASDAQ market index (NASDAQ) and the natural log of the number of IPOs in an industry (log (No. IPO Exits)) over a 12 month period m 12 to m 1. In addition, to control for VC industry performance based on fund-level returns, we include Industry IRR, which is the average since-inception IRR reported by VE for each of the six major industries in the most recent quarter prior to month m. 19 The sample in Table 6 is constrained to be identical across the specifications to allow for comparison of the incremental explanatory power of the exit market signals as measured by R-square. 20 For reference, the industry dummies by themselves explain 28.1% of the variation in capital inflows. Comparing models (1) to (3), consistent with Gompers, et al. (2008), the coefficient of log (No. IPO Exits) is highly significant and adds considerable explanatory power beyond NASDAQ and Industry IRR. In models (4) through (5), we introduce EXF as an additional explanatory variable. While the explanatory power of EXF is lower than that of log (No. IPO Exits), the coefficient of EXF is positive and significant even when included with other exit market signals and Industry IRR. To examine potential time-related effects, model (6) includes a time trend, Time, and an interaction term, EXF Time. Time is a linear trend variable that takes on a value of one for each industry starting in January of the first year of the sample interval (i.e., 1990) and is augmented by one each successive month through the end of the respective period (i.e., December 2008). In model (6), the coefficient on Time and EXF Time are significantly positive, suggesting that industry capital inflows have grown over time, and become more sensitive to EXF over time. 19 The regressions are also estimated with a Dotcom dummy, which is 1 if a company receives a first round of investment during and is 0 otherwise. None of the subsequent results is sensitive to its inclusion and given the high correlation between the Dotcom dummy and NASDAQ, we omit the variable from our main results. 20 Out of a maximum number of 1,368 observations, we lose observations because in some months in certain industries there are no net realizations from exits or failures, resulting in 1,203 observations. 24

26 4.4 Risk allocation of investments In this section, we explore the variation in VCs allocation to riskier investments in response to exit market signals. We use two measures of VCs risk allocation, % Industry Early Stage Rounds, and % Industry First Rounds, that are computed for each industry g in month m as follows:,, No. of early stage rounds in industry during ( ) to ( 2) No. of rounds in industry during ( ) to ( 2) No. of first rounds in industry during ( ) to ( 2) No. of rounds in industry during ( ) to ( 2) (7) (8) Here No. of early stage rounds, No. of first rounds, and No. of rounds in industry g are the number of financing rounds raised by early stage companies, number of first rounds, and total number of VC-backed financing rounds over the 12 months in industry g. The risk measures are forward-looking and computed on a rolling basis in month m over the next 12 months m+1 to m+12. The rationale for these measures of risk is as follows. % Industry Early Stage Rounds focuses on early stage investments (i.e., seed, start-up, and early stage) that typically occur without an extensive track record of company performance and require VCs to absorb greater amounts of developmental risk compared to later stage investments. % Industry First Rounds measures first round investments that usually require VCs to bear more risk because a VC making a first round investment will not have its own track record of performance to observe before making the investment. On the other hand, not all first round investments are made in early stage companies. Nevertheless, it indicates VCs willingness to initiate positions in portfolio companies and put capital at risk rather than keeping it as dry powder. In Table 7, we present summary statistics on measures of risk allocation. For the full sample, on average, 30.4% and 31.2 % of rounds are early stage rounds and first rounds, respectively. In general, there is a close correspondence between risk allocation based on early stage and first round investments. Across all industries, on average 31.1% of the rounds in an industry are invested in early stage companies over , and this proportion increases to 36.0% over , and decreases to 27.9% in the final 25

27 period. All industries, however, do not experience the same pattern in the proportion of early stage investments over time, with the exceptions being Medical/Health/Life Sciences and Non-high Tech. Higher values of EXF imply greater failure coverage and a lower penalty for investment mistakes, and therefore, based on Hypothesis 1, an industry s EXF should be positively related to VCs allocations to early stage or first round investments in the industry. In the regressions in Panel A of Table 8, the dependent variable is % Industry Early Stage Rounds, and % Industry First Rounds in Panel B. Comparing models (1) (5), the coefficients of NASDAQ, log (No. IPO Exits), Industry IRR, and EXF are significantly positive, but EXF has the highest explanatory power. This suggests EXF has more influence on risk allocation compared to other exit market signals. By contrast, the coefficient of log (Capital Inflows) in model (3) is insignificant and has little marginal explanatory power beyond industry dummies. The coefficient of EXF remains positive and significant in model (6) after controlling for the other indicators of exit market strength and industry capital inflows. Because it has been alleged that the excesses of the Dotcom era were primarily driven by investments in high tech sectors like information technology, computers, software, and semiconductors, in model (7) we exclude them from the sample ( All, Ex-High Tech ). The coefficient of EXF is positive and significant suggesting the results are not dependent on high tech industries. It has also been argued that the Dotcom period was an anomalous period. To investigate this issue, we divide the sample into observations over in model (8), and in model (9). 21 In model (9), the coefficients on log (No. IPO Exits) and Industry IRR lose significance, suggesting their relation with risk allocation attenuates over time, but the coefficients of EXF in both periods remain positive and significant. The magnitude of EXF s effect on VC risk allocation is economically meaningful. Based on estimates in model (5), a one standard deviation change in EXF (5.6) leads to a 2.76% change in rounds allocated to early stage companies. The mean proportion of financing rounds allocated to early stage 21 Because it might be argued that the results are driven by inclusion of the bust years of 200 and 2002 in the post-dotcom period, we repeat all of the analysis excluding those years and the results are similar. 26

28 companies in our sample across all periods is 30.4%, indicating that such a change is equivalent to an about 9% (= 2.76/30.4 %) change from the mean. Thus, the recent signals of reward-to-risk have a strong influence on the subsequent risk allocation of investments. The results in Panel B for the proportion of first round investments in an industry are similar to those in Panel A. Similar results hold for risk allocation measured by % Industry First Rounds in later analyses, and therefore for the sake of brevity we only report results based on early stage financing Change in risk allocation over time We now turn to an important question of how VC risk allocations have changed in response to the signal of reward-to-risk over time (Hypothesis 2). In Table 9, we examine the determinants of risk allocation by including Time and an EXF Time interaction term. The coefficients of EXF and EXF Time are significantly positive in all specifications, and the coefficient on Time is significantly negative in the overall sample and in the post-dotcom period. When we divide the sample into pre- and post-dotcom observations in models (5) and (6), the magnitude of the coefficients on EXF and the interaction term increase considerably in the later period. The positive coefficient on EXF Time implies that risk allocations have become more sensitive to signals of reward-to-risk over time. One interpretation of this result is that in the more constrained exit market that followed the Dotcom collapse, VCs pay closer attention to signals of reward-to-risk in making investments compared to the earlier period. In models (1)-(3), based on the full sample over , the significantly negative coefficient of Time suggests that there is a downward trend in the proportion of early stage investments VCs undertake within industries over time. In model (5) for the pre-dotcom period, the coefficient on Time is positive and significant, and this reverses in model (6) for the post-dotcom period and becomes significantly negative. It should also be noted that the significance of Time persists after controlling for 22 The results presented for % Industry Early Stage Rounds are also qualitatively similar if risk is measured by % Industry Early Stage-First Round, i.e., the number of first rounds raised by early stage companies. We do not use the latter measure because it is based on sparser observations that lead to less precise estimates. 27

29 other market indicators, alluding to a decrease in the proportion of early stage investments that is unexplained by market performance and other controls Upside potential versus downside risk A natural question is whether the previous results for EXF are primarily driven by net money-out or the losses incurred from failures. In Table 10, we decompose EXF into its components of Net Moneyout (numerator) and Losses in Failures (denominator), and include these variables along with their interaction with Time to explain risk allocations. In model (1), both the components of EXF have the predicted signs the subsequent risk of VC investments significantly increases with the signal of upside potential and significantly decreases with the losses in failure. These results confirm the predictions of Hypothesis 2 that both upside potential and downside risk influence VCs subsequent investment decisions. On the other hand, the results also show that VC risk allocations are less influenced by signals of upside potential and more influenced by downside risk over time. For example, in model (2), the marginal positive effect of Net Money-out decreases as Time increases, whereas the marginal negative effect of Losses in Failures increases as Time increases. Also, in model (5), over , the coefficient of Net Money-out is positive and significant compared to model (6), where it insignificant over Over the same periods, the coefficient of Losses in Failures is small ( 0.073) and insignificant in the early period, but increases to in the latter period and is highly significant. A one standard deviation increase in the Losses in Failures in model (6) results in a 7.9% decline in the proportion of early stage investments relative to the mean. The results suggest that downside risk has lowered the perceived rewards to investment, leading VCs to allocate proportionally less funds to riskier investments. 4.5 Dollar amount invested Our results so far are based on the proportion of rounds invested in early stage companies, and it is insightful to also assess the impact in terms of dollars invested. If VCs had allocated the same percentage of dollars to early stage companies in the post-dotcom period as in the Dotcom period, we 28

30 estimate that approximately $1 billion more per industry, or $6 billion more in aggregate, would be available per year to invest in early stage companies over On the other hand, it could be argued that too much capital was allocated to early stage companies during the Dotcom period. Because the average proportion of total dollars invested in early stage companies is similar between the periods, the estimated fall-off in funding is similar if it is measured relative to the pre-dotcom period. Over , at an average round size of $5 million per early stage investment, approximately 200 additional companies in each industry, or 1,200 in total across industries, could have received funding per year. Some have argued that informal capital and angel investors have helped to fill some of the funding gap left by VCs moving away from funding, especially seed and startup companies. Although it is difficult to accurately measure the size of informal capital, based on data provided by the Center for Venture Research, angel investing peaked at $50-$60 billion in the Dotcom period and has since fallen to about $22 billion per year over Of this amount, angels have also committed more funds to early and expansion stage investments and Sohl (2009) attributes the declining interest in seed and startup investments among angel investors to a more cautious approach to investment. 23 It is unlikely that only VCs would be influenced by market signals of lower reward-to-risk. 4.6 Alternative explanations In this section, we explore alternative explanations that may account for changing risk allocations. Similar to VCs, entrepreneurs and, therefore, the establishment of startups are likely to expand (decrease) when the signal of reward-to-risk is higher (lower), thereby increasing (decreasing) the set of opportunities available to VCs to invest in. If the supply of startups seeking funding declines, VCs could choose to allocate less funding to these companies. To investigate this, we include the variable Starts m as an additional control variable in the regressions in Table 9. Starts m is the natural logarithm of 23 Angel Investor Market Declines in First Half of 2009, citing Jeffrey Sohl, UNH Center for Venture Research at the Whittemore School of Business and Economics. 29

31 the number of establishment entries one year in age or less from the Longitudinal Data Series on business development from the U.S. Census Bureau for the most recent calendar year before month m. Limitations of this data are that it is not available on an industry basis and it does not measure the quality of startups. Assuming the quality of startups remains similar across time, Starts m is expected to be related to the opportunity set of investments available to VCs. Replicating the most inclusive specification in Table 9 including Starts m as an additional explanatory variable, we get the following estimated regression, , 0.009, Controls (4.05) (2.44) (2.13) ( 5.73) (1) The coefficient of Starts m is positive and significant in Equation (9) and all other specifications, suggesting that allocations to early stage investments increase when the available startup opportunities increase. In addition, the results for EXF, EXF Time, and Time are similar to those reported in Table 9, suggesting that the results for risk allocation are robust to changes in the supply of startups. Apart from exit market signals, another explanation for the change in VCs risk allocations could be related to institutional changes that have occurred over time. For instance, if VC fund sizes have grown larger as a function of the overall growth in the industry, it may be less economical to make smaller investments that are more typical of early stage financing. 24 In our regressions, capital inflows is highly correlated with the average round size (correlation = 87%) and, therefore, should control for trends in fund size over time. Additionally, consistent with the growth in fund size, average round size increases over the sample. As a robustness check, if we replace capital inflows with the average round size in the regressions, the coefficients on the EXF, EXF Time, and Time remain similar to those reported in Table 9. This suggests that lower risk allocations are not explained by the growth in fund and investment size. 24 See, for example, Venture Capital Firms Are Too Big, and Lerner, Leamon, Hardymon (2011). 30

32 5. Performance of VC Investments In this section, we examine how VCs risk allocations at the time of investment initiation relate to the future exit returns of the investments. As formalized in Hypothesis 3, conditional on exit, we expect that exit returns should be positively related to the risk allocation of investments. In Table 11, we use a multivariate regression setting to explore the impact of VC risk allocation and other factors on exit return. The independent variables that capture market and industry conditions are measured at the time of the first VC investment in the company. Because public equity markets (NASDAQ), number of industry VCbacked IPO exits, industry capital inflows, and VC industry performance (Industry IRR) at the time of investment can also influence evaluation of an investment, we include these as control variables. Additionally, we use Years to exit to control for systematic differences in exit returns across companies that vary in the time taken to exit. The design of the regression allows us to investigate how the risk allocation of investments in an industry influences the returns on investments initiated shortly thereafter. The results in Table 11 show a significant positive relation between the future exit returns and the risk allocation of VCs at the time of investment initiation. Comparing the models in columns (1) and (2), including % Industry Early Stage Rounds to explain exit returns increases the explanatory power of the regression model by 21.8%. Supporting Hypothesis 3, the coefficient on % Industry Early Stage Rounds is significantly positive in all specifications. Therefore, a reduction in VCs risk allocation, all else equal, diminishes future exit returns. In addition, the coefficient on % Industry Early Stage Rounds appears substantially larger in model (5) (based on ) compared to model (4) (based on ), which is consistent with risk allocations having a more pronounced effect on exit returns in latter periods. The coefficients on EXF are also significantly positive in all specifications in Table 11, suggesting that exit returns are higher on average for investments made following periods of more favorable reward-to-risk. The coefficient on EXF also appears substantially larger in compared to , suggesting that the sensitivity of exit returns to the signal of reward-to-risk has increased over time. In addition, the coefficient on log (Capital Inflows) increases in model (5) compared to model 31

33 (4). This suggests there is a stronger marginal effect of changes in inflows on exit returns in the latter period or, stated differently, the pro-cyclical effects of capital inflows on returns are larger in this period. Figure 3 shows the trends in the proportion of early stage investments and capital inflows over our sample period. Figure 3a shows using aggregate data from the NVCA a decline in the percentage of early stage investment from 41% in 1990 to 25% in The values shown in Figure 3b are calendar year snapshots (versus the mean of the 12 month rolling averages in Table 7) that represent per industry averages of capital inflows and the proportion of rounds invested in early stage companies over the year. From the peak in 2000 to 2008, capital inflows per industry decline by 37.1% (=($23.7 million $14.9 million)/$23.7 million), and % Industry Early Stage Rounds decreases by 20.5% (=(35.1% 27.9%)/35.1%). Based on our regression estimates, the positive marginal effect of a 37% decrease in capital inflows on exit returns can be nullified by a 7% decrease in the proportion of early stage investments. Thus, the results suggest that the decreased risk allocations to early stage investments over are a strong force countering the positive effects of reduced capital inflows on exit returns. As noted earlier in Section 2, there are several important distinctions between fund and exit returns that caution against assuming that declining exit returns necessarily implies declining fund returns. First, the incidence of failures is a component of fund returns that we do not capture in exit returns. In unreported results, logistic regressions predicting the probability of failure using the explanatory variables in the Table11 regressions show that the probability of failure increases significantly with the proportion of early stage investments at the time of first investment. By increasing allocations to later stage companies, VCs can manage their portfolios to reduce the incidence of failure, which, all else equal, should improve fund returns. Further, 69% of the failures in had their origins in the Dotcom period. In the same way that the Dotcom period was characterized by an unusually high level of investment, the following period has been characterized by an unusual volume of failures. Freed of this, there could be a substantial increase in reward-to-risk, which could stimulate greater risk taking. Second, there are relatively fewer exits from which to estimate the effects of capital inflows and risk over Both the effect of inflows as well as risk allocation over are qualitatively 32

34 similar to the results based on the relatively more complete data from , so that the issue is the magnitude of the effect. Our regression results for the period only include the exits that receive their first round of VC funding after the Dotcom period and there exists a strong possibility is that these early exits are upward biased and later exit returns could be lower. Since 2008, with few exceptions, IPOs remain sparse and VCs continue to face a challenging exit market. Thus, the combination of lower risk and lower exit returns creates a strong head wind in maintaining fund returns that must be offset by VCs greater selection ability. Future work might usefully be directed at the portfolio level to understand the factors VCs can manage to improve their selection ability in the face of a difficult exit market. 6. Conclusion Our article analyzes how the reward-to-risk signaled by recent exits affects the risk allocations of venture capital investments, an area yet to be explored in the existing literature. To gauge the changes in the signal of reward-to-risk trade-off, we construct EXF, which is the ratio of the average dollars gained to dollars lost relative to breakeven from recent exits. In addition to IPO exits, EXF includes the outcomes of M&A exits and failures factors that have grown in importance over time. EXF is akin to failure coverage and provides a time-varying indicator of how many failures on average are covered by an exit, which, in turn, influences VCs ability to absorb losses on new investments. Our findings are based on the returns to 1,436 IPO exits, 1,222 M&A exits, and 4,468 failures of U.S. venture-backed companies over the period 1986 to We find for the overall sample of exits, IPO exits have two times higher returns on average and a substantially higher frequency of top 25% returns (i.e., positive skewness) compared to M&A exits. However, there is a marked increase in the number of M&A exits relative to IPO exits over the sample period. In addition, there has also been a large increase in the funds lost to investments that do not pay-off and result in 00% returns over time. The net effect of these is lower average net exit realizations and a sharply higher average cost of failure which point to an overall reduction in the reward-to-risk ratio of VC investments over time. 33

35 Because higher levels of EXF imply greater failure coverage and a less constrained investment environment for VCs, all else equal, EXF should be positively associated with the proportion of funding for higher risk investments, such as early stage or first round investments. With respect to risk allocation, we find that the proportion of funding for higher risk investments in an industry increases with EXF. VCs have responded to a dampened return distribution by reducing capital allocations to early stage investments over time. When we decompose EXF into its components of Net money-out and Losses in Failures, we find that losses have a significantly negative effect on risk allocation, and this effect significantly increases in magnitude from to By comparison, net money-out has a significantly positive effect on risk allocation but its effect attenuates from to The results show that downside risk as reflected in losses to failures has become more influential in determining VCs risk allocations over time. Finally, there is a significant time trend in the proportion of early stage financing that remains after controlling for signals of exit market strength. This time trend is positive in the early portion of the sample, but reverses and becomes negative in the latter portion of the sample. In finance, it should not be surprising to find that risk is positively related to returns. Yet this is precisely the point of concern in the VC industry recent exit returns are lower but we do not know to what extent VCs risk allocations have contributed to the decline in returns. Conditional on exit, we find that exit returns are positively related to risk allocation and negatively related to capital inflows at the time of the investment. Our findings show that the lower allocations to riskier investment over are a strong force countering the positive effects of reduced capital inflows, suggesting that risk allocation is an important factor in understanding the cyclical nature of the VC industry that also bears on the potential for recovery from its recently troubled state. 34

36 References E. Ball, Hsin Hui, C., and R. Smith, 2011, Can VCs Time the Market? An Analysis of Exit Choice for Venture-backed Firms, Review of Financial Studies 24, Bayar, O., and T. Chemmanur, 2009, Product Market Competition, IPOs versus Acquisitions, and the Valuation Premium Puzzle: A Theoretical Analysis, Boston College working paper. Bradley, D., J. Bradford, I. Roten, and H. Yi, 2001, Venture Capital and IPO Lock-up Expiration: An Empirical Analysis, Journal of Financial Research 24, Chaplinsky, S., and S. Mukherjee, 2009, The Rise of M&A Exits in Venture Capital: Implications for Capital Recovery, University of Virginia, Darden School of Business, working paper, August. Cochrane, J., 2005, The Risk and Return of Venture Capital, Journal of Financial Economics 75, Gompers, P., A. Kovner, J. Lerner, and D. Scharfstein, 2008, Venture Capital Investment Cycles: The Impact of Public Markets, Journal of Financial Economics 87, Gompers, P., and J. Lerner, 2000, Money Chasing Deals? The Impact of Fund Inflows on Private Equity Valuation, Journal of Financial Economics 51, Jones, C. and M. Rhodes-Kropf, 2004, The Price of Diversifiable Risk in Venture Capital and Private Equity. Mimeo. Harris, R., T. Jenkinson, and S. Kaplan, 2012, Private Equity Performance: What Do We Know?, NBER Working Paper 17874, Kaplan, S. and J. Lerner, 2010, It Ain t Broke: The Past, Present, and Future of Venture Capital, Journal of Applied Corporate Finance 22. Kaplan, S. and A. Schoar, 2005, Private Equity Performance: Returns, Persistence, and Capital Flows, Journal of Finance 60, Kedrosky, P., 2009, Right-Sizing the U.S. Venture Capital Industry, Ewing Marion Kauffman Foundation working paper, June 10. Korteweg, A. and M. Sorenson, 2010, Risk and Return Characteristics of Venture Capital-Backed Entrepreneurial Companies, Review of Financial Studies 10, Lerner, J., 994, Venture Capitalists and the Decision to Go Public, Journal of Financial Economics 35, Lerner, J., A. Leamon, and F. Hardymon, 2011, Private Equity, Venture Capital, and the Financing of Entrepreneurship: The Power of Active Investing, New York, Wiley. Ljungqvist, A. and M. Richardson, 2003, The Cash Flow, Return and Risk Characteristics of Private Equity, New York University working paper. 35

37 Nahata, R., 2008, Venture Capital Reputation and Investment Performance, Journal of Financial Economics 90, Ofek, E., 2000, The IPO Lock-up Period: Implications for Market Efficiency and Downward Sloping Demand Curves, working paper, New York University. Patricof, A., 2009, Another View: V.C. Investing Not Dead, Just Different, Dealbook blogs, New York Times, February 9. Phalippou, L. and O. Gottschalg, 2009, The Performance of Private Equity Funds, Review of Financial Studies 22, Poulsen, A., and M. Stegemoller, 2008, Moving from Private to Public Ownership: Selling Out to Public Firms vs. Initial Public Offerings, Financial Management 37, Ritter, J. and I. Welch, 2002, A Review of IPO Activity, Pricing and Allocations, Journal of Finance 67, Robinson, D., and B. Sensoy, 2011, Private Equity in the 21st Century: Cash Flows, Performance, and Contract Terms from , Ohio State University, Charles A. Dice Center Working Paper No Rodrigues, U. and M. Stegemoller, 2007, An Inconsistency in SEC Disclosure Requirements? The Case of the Insignificant Private Target, Journal of Corporate Finance 13, Ruhnka, J.C., Feldman, H.D., and T.J. Dean, 1992, The Living Dead Phenomena in Venture Capital Investments, Journal of Business Venturing 7, , Sahlman, W. A., 1990, Structure and Governance of Venture Capital Organizations, Journal of Financial Economics 27, Smith, R. and J. Smith, 2004, Entrepreneurial Finance, New York: John Wiley & Sons, second edition. Smith, R. and Pedace, R., and V. Sathe, 2012, The Relative Importance of IPO and M&A Exits for Venture Capital Fund Financial Performance, University of California-Riverside, working paper, February. Valliere, D. and R. Peterson, 2004, Inflating the Bubble: Examining Dot-com Investor Behavior, Venture Capital 6,

38 Table 1 Frequency of M&A and IPO Exits This table reports the number of M&A and IPO exits from U.S. venture-backed portfolio companies during the period The sample is restricted to exits with postmoney round valuation data from Venture Economics (VE), and transaction value data (for M&A) or offer price data (for IPOs) from either SDC or VE databases. Exit Year M&A IPO M&A/ (M&A + IPO) % % % % % % % % % % % % % % % % % % % % % % % Total 1,222 1,436 37

39 Table 2 Descriptive statistics: Sample of portfolio companies with exit outcomes The table reports descriptive statistics for U.S. venture-backed portfolio companies that had exit outcomes during Panel A reports statistics for the sample of M&A and IPO exits. Panel B reports statistics for the sample of failures defined as companies declared bankrupt, defunct, or living dead (i.e., companies without a round of financing for at least five years as of December 2008). Mean values are reported for the overall sample along with means across the six major industry classifications used by Venture Economics. No. of rounds is the number of rounds of financing received by the company prior to exit. No. firms invested in company is the number of venture capital (VC) firms that invested in the company. Round amount ($ million) is the average amount invested per round. Money-in ($ million) is the total known amount invested in the company by VCs. Years to exit is the number of years between the date of first VC investment in the company and the date of outcome (i.e., exit or failure). Overall Biotech Communications /Media Panel A: Sample of M&A and IPO exits Computer Related Medical/Health/ Life Sciences Non-High Tech Semiconductor/ Other Electrical No. of rounds No. firms invested in company Round amount ($million) Money-in ($ million) Years to exit (in years) No. of Companies 2, , Panel B: Sample of Failures Overall Biotech Communications /Media Computer Related Medical/Health/ Life Sciences Non-High Tech Semiconductor/ Other Electrical No. of rounds No. firms invested in company Round amount ($million) Money-in ($ million) Years to exit (in years) No. of Companies 4, , ,

40 Table 3 Exit returns from venture-backed M&A and IPO exits The table reports in Panel A the mean and median exit returns (Exit_Return). The pool of venture-backed M&A and IPO exit returns are sorted into groups based on the top 25% (high), middle 50% (medium) and bottom 25% (low) of returns. In Panel B, annualized returns (r) are derived from (1+Exit_Return) = (1+r) t, where Exit_Return is the company-level exit return and t is the number of years to exit. The data are winsorized by excluding the top and bottom 5% of the returns for each type of exit. ***, **, * indicates statistical significance at the 1%, 5%, and 10% level respectively. The p-values are reported based on a t- statistic (Pearson's chi-squared) test for the difference in means (medians). Panel A: Exit Returns Overall Low Medium High High Low All exits Mean Median N 2, , M&A Mean Median % of N 46.0% 75.9% 37.6% 32.7% IPO Mean Median % of N 54.0% 24.1% 62.4% 67.3% Difference in Mean (IPO - M&A) *** (p-value) (0.00) Difference in Median (IPO - M&A) *** (p-value) (0.00) Panel B: Annualized Exit Returns All exits Mean Median N 2, , M&A Mean Median % of N 46.0% 73.7% 39.9% 30.4% IPO Mean Median % of N 54.0% 26.3% 60.1% 69.6% Difference in Mean (IPO - M&A) 30.0 *** (p-value) (0.00) Difference in Median (IPO - M&A) 27.6 *** (p-value) (0.00) 39

41 Table 4 Returns to IPO and M&A exits over time The table reports mean and median exit returns. The pool of Exit_Return in the panel dataset is sorted into categories of the top 25% (High), middle 50% (Medium) and bottom 25% (Low) returns in the three time periods. The frequency of M&A and IPO exits is reported within each period (% of Exits). Years to exit is the average years between the first round of VC investment and date of exit. The data are winsorized by excluding the top and bottom 5% of the returns for each type of exit. % of Exits Overall Low Medium High High Low (in period) Pre-Dotcom: All exits 100% Mean Median N M&A 25.4% Mean Median Years to exit % of N 25.4% 43.4% 19.4% 19.2% IPO 74.6% Mean Median Years to exit % of N 74.6% 56.6% 80.6% 80.8% Dotcom: All exits 100% Mean Median N M&A 41.4% Mean Median Years to exit % of N 41.4% 64.5% 30.9% 39.2% IPO 58.6% Mean Median Years to exit % of N 58.6% 35.5% 69.1% 60.8% Post-Dotcom: All exits 100% Mean Median N 1, M&A 63.4% Mean Median Years to exit % of N 63.4% 95.1% 59.3% 39.8% IPO 36.6% Mean Median Years to exit % of N 36.6% 4.9% 40.7% 60.2% 40

42 Table 5 Public market conditions and quarterly returns from exits ( ) The table reports in Panel A mean and median equally weighted quarterly returns for M&A and IPO exits, and in Panel B the components of exit returns. Each quarter, returns are calculated for the two portfolios consisting of M&A or IPO exits. Hot, Normal, and Cold Markets are defined as quarters in which the sixmonth cumulative NASDAQ returns ending with the last month of the quarter are in the top 25%, middle 50%, and bottom 25% of the all quarters in the sample period. The significance test for the difference of means is based on t-statistics for a two-sample means test, with p-values reported in parentheses. The significance test for the difference of medians is based on chi-squared statistics with continuity correction for a nonparametric K-sample test, with p-values reported in parentheses. ***, **, * indicate statistical significance at the 1%, 5% and 10% level, respectively. Panel A: Quarterly Returns , excluding 1999 and 2000 Cold Normal Hot Cold Normal Hot M&A Returns (%) Mean Median IPO Returns (%) Mean Median Difference in Mean (IPO M&A) ** 15.0 *** *** 45.9 *** 59.9 (p-value) (0.05) (0.01) (0.11) (0.00) (0.00) (0.11) Difference in Median (IPO M&A) *** 92.4 *** *** 98.7 *** 34.6 (p-value) (0.00) (0.00) (0.23) (0.00) (0.00) (0.41) Panel B: Components of Returns VC stakes (%) M&A Mean IPO Mean Money-in ($MM) M&A Mean IPO Mean Money-out ($MM) M&A Mean IPO Mean No. Exits 605 1, , % M&A Exits 62.5% 42.8% 39.3% 68.3% 42.7% 43.8% % IPO Exits 37.5% 57.2% 60.7% 31.7% 57.3% 56.2% 41

43 Table 6 Regressions explaining capital inflows The dependent variable is the log (Capital Inflows) in month m, the natural logarithm of the sum of capital invested in an industry (in $ millions) over a 12 month period starting in m+1. NASDAQ (%) is the cumulative monthly return on the NASDAQ index over a 12 month period ending with m-1. log (No. IPO Exits) is the natural logarithm of the number of IPO exits in the industry over a 12 month period ending with m-1, plus one. Industry IRR is the average percentage return for each of the six VE industries in the prior quarter to which the dependent variable is measured. EXF is the reward-to-risk ratio for the industry over a 12 month period ending with m-1, measured as the ratio of the average net money-out to the average dollars lost in failures over the 12 month period. Time is a trend variable that takes on a value of one for each industry starting in January of the first year of the sample period and is augmented by one each successive month through the end of The regressions include industry fixed effects. The t- statistics reported in parentheses below coefficient estimates are based on robust standard errors and adjust for clustering of observations over three calendar month periods. ***, **, * indicate statistical significance at the 1%, 5% and 10% level, respectively. log (Capital Inflows) All All All All All All Variable (1) (2) (3) (4) (5) (6) NASDAQ (%) *** (1.23) (0.27) (2.88) log (No. of Past IPO Exits) *** *** *** (8.94) (9.36) (5.01) Industry IRR (0.73) (0.15) (1.05) EXF *** ** *** (3.45) (2.43) (4.46) Time EXF *** (3.21) Time *** (9.92) Industry Dummies Yes Yes Yes Yes Yes Yes R-square No. of obs. 1,203 1,203 1,203 1,203 1,203 1,203 For reference, a specification with industry dummies alone has an R-square of

44 Table 7 Descriptive statistics of industry risk allocations The measures of risk allocation are as follows: % Industry Early Stage Rounds in month m is the percentage of VC financing rounds in an industry raised by rounds in early stage companies computed for a rolling 12 month period beginning with m+1. % Industry First Round in month m is the percentage of VC financing rounds in an industry raised by first rounds in companies computed for a rolling 12 month period beginning with m+1. % Industry Early Stage Round All % Industry First Rounds % Industry Early Stage Round % Industry First Rounds % Industry Early Stage Round % Industry First Rounds % Industry Early Stage Round % Industry First Rounds All Industries Biotech Communications/Media Computer Related Medical/Health/Life Sciences Non-High Tech Semiconductors/Other Elect

45 Table 8 Regressions examining risk allocation of VC investments The sample consists of venture-backed companies with known outcomes (i.e., IPO, M&A, failures) during The dependent variable in Panel A is % Industry Early Stage Rounds, measured in month m as the percentage of rounds in an industry over a 12 month period starting in m+1 that were raised by early stage companies; and in Panel B is % Industry First Rounds, measured in month m as the percentage of rounds in an industry over a 12 month period starting in m+1 that were raised by first rounds. log (Capital Inflows) is the natural logarithm of the sum of capital invested in an industry (in $ millions) over a 12 month period m 12 to m 1. Other variables are as defined in Table 6. The t-statistics reported in parentheses below coefficient estimates are based on robust standard errors and adjust for clustering of observations over three calendar months periods. ***, **, * indicate statistical significance at the 1%, 5% and 10% level, respectively. Panel A: %Industry Early Stage Round All All All All All All All, Ex- High Tech Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) NASDAQ (%) *** ** *** *** (5.21) (2.01) (1.51) (-2.80) (3.49) log (No. of Past IPO Exits) *** *** *** ** (4.05) (4.65) (5.76) (2.68) (-1.24) log (Capital Inflows) ** *** *** (-0.42) (-2.41) (-3.76) (1.22) (5.85) Industry IRR *** ** *** ** (4.26) (2.10) (3.10) (2.65) (-0.00) EXF *** *** *** *** *** (7.56) (6.63) (3.89) (4.22) (3.80) Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes R-square No. of obs. 1,203 1,203 1,203 1,204 1,203 1, For reference, a specification with industry dummies alone has an R-square of

46 Table 8 (Continued) Regressions examining risk allocation of VC investments Panel B: %Industry First Round All All All All All All All, Ex- High Tech Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) NASDAQ (%) *** *** *** (5.24) (3.04) (1.28) (0.34) (3.20) log (No. of Past IPO Exits) *** *** *** *** *** (8.78) (8.43) (10.61) (4.64) (2.75) log (Capital Inflows) * *** *** (1.89) (0.12) (-0.94) (3.48) (-4.10) Industry IRR *** *** *** ** (5.11) (3.54) (5.56) (2.43) (1.18) EXF *** *** *** *** *** (6.98) (3.52) (2.94) (3.32) (3.19) Industry Dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes R-square No. of obs. 1,203 1,203 1,203 1,203 1,203 1, For reference, a specification with industry dummies alone has an R-square of

47 Table 9 Regressions examining time dependency of VC risk allocations The sample consists of venture-backed companies with known outcomes (i.e., IPO, M&A, failures) during The dependent variable is % Industry Early Stage Rounds, measured in month m as the percentage of rounds in an industry over a 12 month period starting in m+1 that were raised by early stage companies. Time is a trend variable that takes on a value of one for each industry starting in January of the first year of the sample period (e.g., All=1990) and is augmented by one each successive month through the end of the respective period. The t-statistics reported in parentheses below coefficient estimates are based on robust standard errors and adjust for clustering of observations in three calendar month periods. ***, **, * indicate statistical significance at the 1%, 5% and 10% level, respectively. %Industry Early Stage Round All All All All, Ex-High tech Variable (1) (2) (3) (4) (5) (6) EXF *** ** *** ** *** (3.23) (2.41) (3.10) (2.42) (2.98) EXF x Time ** ** ** ** ** (2.31) (2.18) (2.22) (2.54) (2.31) Time *** *** *** ** * *** (-3.27) (-3.42) (-5.13) (-2.51) (1.94) (-2.87) NASDAQ (%) *** *** (1.04) (0.03) (-4.02) (3.15) log (No. of Past IPO Exits) *** *** ** (4.10) (4.73) (0.06) (-2.69) log (Capital Inflows) 1.239** *** (2.21) (-0.66) (0.27) (5.83) Industry IRR ** *** *** (2.48) (3.26) (2.83) (-0.12) Industry Dummies Yes Yes Yes Yes Yes Yes R-square No. of obs. 1,203 1,203 1,

48 Table 10 Regressions examining sensitivity of VC risk allocation to upside potential and downside risk The sample consists of venture-backed companies with known outcomes (i.e., IPO, M&A, failures) during The dependent variable is % Industry Early Stage Rounds measured in month m as the percentage of rounds in an industry over a 12 month period starting in m+1 that were raised by early stage companies. Net Money-out is the average net money-out from exits in the industry over a 12 month period ending in m-1. Losses in Failures is the average loss of capital invested in companies declared bankrupt, defunct, or not having received a new round of investment for at least five years as of 2008 in the industry over a 12 month period ending in m-1. Other variables are as defined in Table 6 and Table 8. The t-statistics reported in parentheses below coefficient estimates are based on robust standard errors and adjust for clustering of three calendar months of observations. ***, **, * indicate statistical significance at the 1%, 5% and 10% level, respectively. % Industry Early Stage Financing All All All All, Ex- High Tech Variable (1) (2) (3) (4) (5) (6) Net Money-out ** *** * ** *** (2.10) (6.31) (1.93) (2.23) (5.10) (0.61) Losses in Failures *** *** ** ** ** (-3.24) (-2.78) (-2.39) (-2.28) (0.78) (-2.57) Time x Net Money-out *** ** ** *** (-2.69) (-2.36) (2.20) (-4.63) (-1.55) Time x Losses in Failures ** ** ** *** (-2.34) (-2.52) (-2.33) (-0.27) (2.85) Time ** ** ** ** ** (-2.23) (-2.53) (-2.51) (2.30) (-2.34) NASDAQ (%) ** (1.27) (0.75) (-0.18) (2.15) log (No. IPO Exits) *** *** (4.29) (5.86) (-0.17) (-0.69) log (Industry Capital Inflow) ** *** (2.23) (1.64) (0.91) (4.35) Industry IRR ** *** ** (2.25) (3.66) (2.21) (-0.28) Industry Dummies Yes Yes Yes Yes Yes Yes R-square No. of obs. 1,203 1,203 1,

49 Table 11 Regressions explaining exit returns The sample consists of venture-backed companies with IPO or M&A exits during The dependent variable is the exit return (Exit_Return) for an exit of a portfolio company which raised the first round of VC financing in month m, defined as the return accruing to the group of VCs holdings stakes in the company at the time of exit. % Industry Early Stage Round is the percentage of rounds in the industry in which the exit occurs that were raised by early stage companies in the 12 month period ending with month m-1. log (Years to exit) is the natural logarithm of the number of years between the exit date and the first date of VC financing received in month m. Other variables are as defined in Table 6 and Table 8. The and subsamples include exits with first round dates that fall during 1990 to 1997 and 2001 to 2008, respectively. The t-statistics reported in parentheses below coefficient estimates are based on robust standard errors and adjust for clustering of observations over three calendar month periods. ***, **, * indicate statistical significance at the 1%, 5% and 10% level, respectively. Exit_Return All All All Variable (1) (2) (3) (4) (5) % Industry Early Stage Round *** *** ** *** (4.55) (3.57) (2.24) (2.93) EXF *** 0.316** *** (4.72) (2.23) (3.30) NASDAQ (%) ** ** ** (-2.32) (-2.33) (-2.02) (0.71) (-0.04) log (No. of IPO Exits) (-1.59) (-1.55) (-1.47) (0.61) (1.26) log (Capital Inflows) ** *** ** ** (-2.09) (-3.14) (-2.41) (-0.76) (-2.38) Industry IRR ** ** ** (-2.21) (-2.22) (-2.34) (-1.34) (0.17) log (Years to exit) *** *** *** *** (-6.13) (-6.12) (-6.09) (-3.18) (-1.44) Industry Dummies Yes Yes Yes Yes Yes R-square No. of obs. 2,215 2,215 2,215 1,

50 Gross Realizations from IPO and M&A Exits ($ in billions) Average Losses in Failures ($ in millions) IPOs M&A Avg Losses in Failures 0.0 Figure 1. Venture-backed Exits and Losses in Failures over The figure plots the natural logarithm of gross realizations from IPO and M&A exits (in $ billions), and the average losses in failures ($ in millions) of companies declared bankrupt, defunct, or living dead (i.e. without any rounds of financing for at least five years as of December 2008) over the period The sample is obtained from Venture Economics and includes IPO and M&A exits for which postmoney round valuation data and transaction (for M&A) or offering (for IPOs) data is available. Failures are restricted to portfolio companies that have at least two rounds of investment. 49

51 Exit-to-Failure Ratio NASDAQ 14 5, ,500 4,000 3, ,000 2, , ,500 1, EXF NASDAQ 0 Figure 2. Average Annual Exit-to-Failure Ratio over The figure plots the average Exit-to-Failure Ratio (EXF) across industries against the quarter-end levels of the NASDAQ market index. The values of EXF are three month averages corresponding to calendar quarters of the EXF ratio for all VC investments across the six VE industry categories. 50

52 Figure 3a. Aggregate Risk Allocations and Capital Inflows over The figure plots on the left hand axis the annual average percentage per industry of rounds invested in seed, startup, and early stage companies) to total venture capital investment rounds, and on the right hand axis the annual total capital inflows into venture capital (dollars in billions). The data are from NVCA Annual Yearbooks, VC investment by stage. Figure 3b. Average Risk Allocations and Capital Inflows per industry over The figure plots on the left hand axis the annual average percentage per industry of rounds invested in seed, startup, and early stage companies) to total venture capital investment rounds, and on the right hand axis the annual total capital inflows into venture capital per industry (dollars in billions). The industries are the six major VE industry categories. 51