When Reputation is Not Enough: Portfolio Manipulation and Fund-raising in Venture Capital

Transcription

1 When Reputation is Not Enough: Portfolio Manipulation and Fund-raising in Venture Capital Indraneel Chakraborty and Michael Ewens April 7, 2014 Abstract Using a novel dataset of venture capital (VC) investments, we find that reputation concerns are unable to eliminate agency problems between VCs and their investors. We find that many portfolio characteristics of VC funds respond to fund-raising activities and outcomes. VCs delay signaling negative information about their quality until after raising new funds. Once a VC raises a new fund, they write-off past investments more often and invest using different securities. The entrepreneurial firms that receive capital are relatively worse than those financed prior to the fund closing: they have both lower valuation and probability of successful exit. The average difference in returns for firms in which VCs invest after raising new funds is approximately 11% lower compared to firms financed by VCs before raising new funds. The result suggest that fund-raising incentives have real impacts on the timing and composition of the VC fund portfolio. JEL Classifications: G24, G32, G14 Keywords: Venture capital, reputation, financial intermediation, entrepreneurship. Cox School of Business, Southern Methodist University and Tepper School of Business, Carnegie Mellon University. Corresponding author contact information: Michael Ewens ([email protected]), Tepper School of Business, 5000 Forbes Ave. Pittsburgh, PA 15221, We thank Matthew Rhodes-Kropf for helpful comments. Ewens recognizes the financial support of the Kauffman Junior Faculty Fellowship. We are grateful to VentureSource and Correlation Ventures for access to the data.

2 Delegated management of capital often limits an investor s ability to observe effort and talent level of a money manager. Reputation built upon past performance can ameliorate this information asymmetry and help investors identify capable managers. A large literature shows across several industries that inflows of capital to managers responds strongly to performance. 1 The stakes are thus high for managers to signal that they are talented, as their future earnings potential depends on performance signals. This paper asks if and how information frictions incentivize managers to distort their reputation signal through portfolio strategy. Such agency problems are acute in venture capital and private equity (VC/PE) settings where managers (i.e. general partners or GPs) raise money to start a new fund every few years from limited partners (LPs). A salient feature of the relationship between GPs and LPs is the lack of control the latter has after the fund closes. An investor s control over the venture capitalist is at its highest when she approaches LPs to raise a new fund. 2 There is significant value to the VC in raising the new long-lived fund: a stream of cash flows through management fees (typically 2%) that has been shown to account on average for 15% of total capital committed. 3 The main post-fund raising connection between the GP and LP is the equity incentive via the standard 20% profit-sharing (i.e. carry). The change in the LP s control may lead to ex-post agency problems where GPs may choose suboptimal investments through changes in their portfolio. 1 Lim, Sensoy, and Weisbach (2013) find that indirect incentives for the average hedge fund are four times as large as direct incentives from incentive fees and returns to managers own investment in the fund. Chung, Sensoy, Stern, and Weisbach (2012) show that the current fund s performance affects the ability of private equity general partners (GPs) to raise capital for future funds. In the mutual fund industry, Sirri and Tufano (1998) and Chevalier and Ellison (1997) show that reputation of the mutual fund managers affects their ability to raise new capital. Specifically, Chevalier and Ellison (1997) show that mutual fund managers alter the riskiness of their portfolio at the end of the year to attract new funds. Recently, in banking literature, Lin and Paravisini (2012) show that banks find their capacity to externally fund syndicated loans reduced after a firm they monitor commits fraud. 2 The agency problem should be present in all fund management scenarios. However, the problem should be relatively less severe in cases where the amount of new capital raised every period is a small fraction of the total amount of fund under management. This is the case in mutual funds, hedge funds and banks for example. 3 Metrick and Yasuda (2010) study a sample of VC funds and their fee structures which can change over the life of a fund. Fees account for an average of 15% of capital committed by limited partners. 2

3 We investigate these agency problems with information on individual VC investment decisions and funding cycles. In contrast to earlier studies of VC fund-raising (e.g. Gompers (1996) and Brown, Gredil, and Kaplan (2013)), this paper focuses on how the shift in control rights around fund closing impacts non-valuation portfolio characteristics across VC funds. We first identify the actions a VC can take to manage reputation: investment size, security choice and investment timing. Under a simple null that agency problems have no bearing on these dimensions of portfolio strategy, there should be no relationship between them and fund-raising by the VC. To reject the null, our empirical strategy employs two steps. First, we compare VC financing decisions around VC funding cycle to test for changes in behavior. Second, we track the ultimate outcomes of entrepreneurial firms financed around fund closing to investigate whether investment quality differs. The data comprises venture capital financings and funds from 1992 to For the former, we observe entrepreneurial firm characteristics and the relationship between the firm and each of its venture capital investors. The data tracks the timing and size of investments along with the participation of the VC fund over the entrepreneurial firm lifecycle. The data also includes information about the funds raised by VCs (size, date closed) and the individuals investments. The main sample includes 564 VC firms and 1366 funds, with investments in 7267 entrepreneurial firms and 17,485 financing events. The empirical specification follows a VC firm s current fund s investment behavior around the next fund closing. The analysis compares investment characteristics and exit outcomes around the years immediately surrounding the VC firm s next fund closing. Multiple funds within a VC firm invite our preferred model: VC firm fixed effect comparing fund investment across funds and within-firm. Our main predictions posit an agency problem between the VC and investor (LP) driven by limited information about the VC s type. Effectively, the VC has control over portfolio strategy that can send plausibly positive signals about type. Several authors have stud- 3

4 ied such frictions in exit strategy (Gompers (1996) or portfolio valuation (Brown, Gredil, and Kaplan (2013) and Jenkinson, Sousa, and Stucke (2013)). This paper begins with the same frictions and investigates the rich detail of investment-by-investment fund activity. These differences demand a new set of hypotheses that were not testable in earlier work and databases. First, we ask if fund-raising predicts changes in investment intensity through new investments by the current fund and capital use. Next, and in contrast to the earlier studies of portfolio value inflation, we ask if VCs act to delay investment or writeoffs. Postponing negative signals about portfolio performance could artificially inflate beliefs about VC quality. Next, the predicted behavior of the VC selectively investing in high-quality firms in the portfolio provides an additional incentive to alter security choice. Thus, we ask whether the use of debt finance changes around fund-raising. Finally, an immediate implication of the above hypothesis is a heterogeneity in the quality and composition of entrepreneurial firms financed around a VC s next fund. We predict that those firms financed immediately after fund-raising will under-perform and debt financing will signal poor quality as well. We find that VCs postpone bad news about their past performance when they are close to raising capital for their next fund. After funding is secured, VCs writeoff past bad investments more often - the frequency of writeoff more than doubles in years subsequent to fund raising. Fund-raising activities and success also strongly correlates with behavior consistent with the VC fund selectively delaying reinvestment in a subset of their portfolio. VCs also significantly increase their use of debt financing after a fund is successfully raised. This behavior is consistent with VCs selectively avoiding riskier investments pre-fund closing and using debt post funding as a strategy to invest in inferior firms while limiting downside exposure. The differences in investment behavior and security choice manifests itself in entrepreneurial firm outcomes. The firms that VCs financed through debt have 21% lower successful probability of successful exit through initial public offerings and acquisitions. This negative relationship is largely driven by debt financing after fund closing. Again these 4

5 patterns are consistent with VCs choosing debt securities for the riskier part of their portfolio. The median difference in investment returns between firms in which VCs invest before their own funding and the firms in which they invest after fund closing is approximately 11% annually. The results above are robust to several alternative explanations. We impute next fund close dates for approximately 13% of the VC funds. These VC firms failed to raise a next fund, however, the ex-ante frictions due to fund-raising should still exist. An analysis that separates out the two sample of successful and unsuccessful fund-raisers results in no change in conclusions. A prediction of many reputation models is that more experienced agents have less incentive to manipulate their portfolio because the value is lower. Thus, one might predict that the results are driven by the younger, inexperienced funds as Gompers (1996) found for grandstanding. We separate VC funds into first time and all subsequent funds for VC firms that successfully raised at least two funds. In this sample, we find some evidence of the opposite: a VC firm s first fund is less likely to manipulate their portfolio. We argue this could be a consequence of LPs higher scrutiny for early funds. Finally, the results are not driven by the boom era of nor can the results be explained by VC funds raised in high-return equity markets and subsequent mean reversion. How do these patterns inform VC fund selection and VC performance evaluation? The results do not immediately imply that a large set of lemon funds are raising capital, tricking limited partners. Rather, the results suggest that non-valuation metrics could be a warranted additional to disclosures available to LPs. Of course, no amount of information disclosure can reveal the ideal set of entrepreneurial firms that require a writeoff nor the optimal timing. From an LP s perspective, this discretion on the part of the VC is possibly an accepted cost of delegated management. Nonetheless, the stark differences in performance around next fund closing raises questions about the current 2 and 20 model or lumpy fund-raising process. Pledge funds where the VCs returns to investor for each deal are an extreme solution to the 5

6 fund-raising process, with its own costs. In future drafts we hope to show if and how these changes to portfolio strategy benefit the VC or harm the LP. For example, it may be that VCs who delay writeoffs or use debt financing raise an otherwise larger fund. LPs are worse off because they over-invest in a VC fund. This paper contributes to the literature on VC reputation and performance. Gompers (1996) shows that young venture capitalists take companies public earlier to establish a reputation to facilitate raising capital for new funds. Jenkinson, Sousa, and Stucke (2013) find that valuations are conservative in general except before PE managers raise new funds. Brown, Gredil, and Kaplan (2013) investigate who inflates valuations, and find that investors are able to see through manipulating managers. Those GPs that inflate valuations are unable to raise new capital. Dass, Hsu, Nanda, and Wang (2012) show that young VC funds invest in too many projects and turn over the projects too quickly in order to find successful investment outcomes. Fund raising and fund age also changes the risk profile of investments as shown by Barrot (2014). Our paper provides a view of the investment-by-investment portfolio strategy and how post-fund-raising change in investor oversight impacts real portfolio outcomes. Furthermore, the literature has thus far focused on valuation inflation or aggression. The new strategies detailed here provide an alternative view of the tools available to the VC. Our work also contributes to the literature on contracting choices for entrepreneurial and private firms. Kaplan and Strömberg (2003) detail the contract choices of venture-backed firms, particularly the allocation of cash flow and control rights. 4 Our paper shows that the contract choice of a firm debt versus equity financing may be the result of the agency problem between the VC fund and the investors in the VC fund. Theoretical literature has suggested that the optimal contract between the principal and an agent in a variety of delegated management scenarios is financing with state contingent 4 Cumming (2005) looks at similar questions in a larger Canadian sample. Hellmann, Lindsey, and Puri (2008) study debt from outsiders and focus on how these capital providers build lending relationships with entrepreneurial firms. 6

7 claims (See Grossman and Hart (1986), Aghion and Bolton (1992) and Dewatripont and Tirole (1994)). However, in the case of discrete financing decisions as observed in VC/PE funds, ex-post contingency claims can be rare, leading to agency problems. Bank financing contracts already impose a large number of covenants on the borrowing firms to allocate control rights more effectively ex-post financing, which ultimately can increase value of managed assets (See Chava and Roberts (2008) and Nini, Smith, and Sufi (2012)). Such contracts may be useful even in case of money management industry where principal and agents share equity returns. 1 Hypothesis Development 1.1 VC fund portfolio strategy Before we detail a set of hypotheses around VC portfolio strategy, it is important to map the action space available to VCs. Several authors investigate the valuation choices of a VC or PE fund manager over the fund-raising cycle (e.g. Brown, Gredil, and Kaplan (2013)). Such changes can stem from new investments in portfolio companies or a mark of a portfolio to market. The agency conflicts that predict such manipulation should also manifest in other strategies. First, the VC fund can manage when to make investments in entrepreneurial firms and the level of any commitment. Conditional on taking an equity position in these firms, the VC also has some control on the re-investment timing. Most VCs purchased preferred equity and in turn receive significant control rights not available to traditional equity holders (see Kaplan and Strömberg (2003)). Gompers (1996) shows that these rights give the investor the ability to select an exit strategy and its timing. One exit thus far not studied is the choice of writing off or shutting down an entrepreneurial firm. VCs can exercise these rights through simple non-investment or even redemption rights. Finally, VCs can often choose from a rich set of contract features such as liquidation preference and debt versus equity. 7

8 This paper argues that each of these choices provide mechanisms through which the agency problems of fund-raising can manifest themselves. 1.2 Agency Problems Venture capitalists (General Partners) and their investors (Limited Partners) face a standard delegated management setup. Previous literature investigates the presence of agency problems and found mixed evidence. Brown, Gredil, and Kaplan (2013) ask if GPs try to inflate their quality signal before fund raising, and find that in fact, good GPs are more conservative before fund raising. LPs, who are sophisticated investors, are able to see through this behavior and bad GPs who inflate their quality signal are unable to raise new funds. In contrast, Jenkinson, Sousa, and Stucke (2013) show that funds held by CALPERs inflated valuations during fund-raising. Gompers (1996) finds that VCs grandstand and IPO firms quicker than they should so that they can signal their quality to the GPs. There may be additional agency problems that manifest themselves through different actions of the GPs. A delegated management setup can cause two types of agency problems - Ex-ante problems where LPs need to separate good and bad GPs, and ex-post problems where GPs may deviate from optimal investing strategy for LPs. Furthermore, even ex-ante, agency problems can exist in a larger action space than just signaling higher quality. 5 For example, VCs may not signal higher quality by inflation valuations of firm in their portfolio because LPs can identify such behavior as demonstrated by Brown, Gredil, and Kaplan (2013), but VCs may delay writing off bad firms to avoid negative signal of their quality. Additional actions are also available to the VC. VCs may want to look busy and want to show the LPs that their strategies demand more capital. That is, VCs have to show that they can put money to work. In this case, VCs should increase their investing activity in the current fund to signal that they can put money to work. 5 Even in terms of signaling higher quality, Gompers (1996) shows that young VCs may suffer from agency conflicts with their firms. 8

9 LPs exercise control on GPs through their allocation to new funds. At the time of raising a new fund, GPs approach LPs, where the latter provide capital to high quality VCs and refusing funding to underperforming VCs. This ability provides incentive for VCs to signal quality during fund-raising. However, once funding is provided, the immediate control of LPs on GPs is at the lowest point in the life cycle of VCs. Facing relatively higher temporal bargaining power, GPs may be susceptible to ex-post agency problems vis-a-vis their LPs. The problem is similar to asset substitution since GPs take advantage of limited control by LPs after funding to invest in risky firms. Furthermore, while LPs are able to see through inflated valuations of firms as shown by Brown, Gredil, and Kaplan (2013), this paper introduces other possible actions for the VC - such as delay in realization of losses. 1.3 Testable Hypotheses We first investigate if GPs change their investing behavior before raising new funds. This will help them delay negative signals about their quality. The change could be: (H1) Activity in investment: VCs have incentives to look busy. VCs would like to show that they indeed need new capital as they are able to find good firms and they are running out of old capital. It may be natural to expect cumulative investment from previous fund to reach closer to 100% before new fund raising. However if investment per period peaks before raising new funds, rather than stay the same or decline, then it may suggest that VCs are relatively more active before fund raising. (H2) Delay write-offs before funding: VCs delay writing off their portfolio of companies, to avoid sending negative signals of their quality. VCs may also delay investing in underperforming firms in their portfolio, which do not face a write-off decision. 6 6 Brown, Gredil, and Kaplan (2013) discuss the possibility of reverse causality in timing of fund launches and its impact of valuations before fund raising. VCs may sometimes be optimistic about their investment opportunities or skill level, which encourages them to raise a new fund, and hence valuations are biased higher 9

10 Next, we investigate if GPs change their behavior post funding. The change could be in type of firms the GPs invest in, or type of financing contracts they have with firms in their portfolio. If VCs engage in asset substitution, they may choose to finance firms with higher downside risk after obtaining their own funding. Such firms eventually should show themselves to be riskier through higher realized negative outcomes - such as they should face downward valuations more often or have lower probability of successful exit through an acquisition or an initial public offering. The type of financing depends on the type of firm, and hence it is possible that GPs financing composition changes along with the type of firms they finance. We investigate if the type of financing is different for firms ex-post VC funding. The presence of a different type of contract ex-post funding also provides corroboration that the VCs recognize the difference in the type of firms they are investing in. In general VCs provide equity financing to well performing firms and write-off firms that have under-performed expectations. Hence, we should expect that when VCs are attempting to take risk, then they will perhaps not shutdown the marginal firm, and provide it financing to continue in hopes that the firm may outperform in the future. However, should we expect that the financing contract to be straight equity as if the firm is well performing? This is not necessary. A VC may choose to finance a riskier firm, but at the same time may not want the valuation of the firm to be known by the investors based on (H2). A possible way to provide financing without revaluing the company downwards immediately is by providing debt financing to such firms. Thus a pecking order emerges. Among the risky firms, the VCs should be willing to offer equity to the relatively safer firms, and debt to the relatively riskier firms, and shut down the most risky firms. Hence, we should see a change in the composition of financings of firms by VCs after funding if there in an ex-post agency problem faced by the VCs. While we cannot before raising a fund. In our case, hypothesis (H2) is an especially strong test under such circumstances, because before raising a new fund, VCs generally have to update stale valuations. However, if during such a time when valuations have to be updated and there is general optimism, certain write-offs are postponed to just after fund raising, then it suggests that VCs are selectively updating valuations in order to try to avoid a negative signal. 10

11 distinguish between firms financed by equity that are best to those riskier firms financed by equity, we observe security choice in all financings. (H3) Change in financed firms: Ex-post funding, GPs are willing to accept reputation hits. They delay investment in riskier firms ex-ante and invest more in relatively risky firms ex-post. The financing contract offered to such firms is different as well. GPs provide bridge loans or straight debt to inferior firms ex-post funding. This allows them to invest in inferior firms while limiting their downside exposure in case realization is negative. Ultimately we ask if these agency frictions hurt LPs. If it is indeed the case that agency frictions lead VCs to change their behavior after raising a new fund, then quality and outcome of investments post funding should be different. Continuing with (H3), we should find that firms which are debt financed have a higher probability of poor outcomes through the exit type or exit valuation. Ultimately, such firms should also have a lower probability of successful exit through IPO or acquisition. Furthermore, if fund-raising introduces agency problem, then VCs will disproportionately invest in worse firms post-funding resulting in relatively lower returns. (H4) Negative outcome for financings after fund raising: Agency frictions should manifest themselves in terms of inferior outcomes for investments done after a VC has raised a new fund. Thus, (i) debt financed firms should result in lower valuations and lower probabilities of successful exit. (ii) Equity financed firms post funding should also underperform firms in which VCs invested before fund raising. 2 Data We use the venture capital financing database VentureSource provided by Dow Jones. The data has been supplemented with additional data from individual VCs, LPs and also merged 11

12 with the valuation data in Thompson s VentureXpert. 7 The financing data covers equity, debt and exit events for US-based VC-backed entrepreneurial firms from 1992 through For a subset of these financing events, we observe the VC fund that provided the capital (as opposed to simply knowing the VC firm.) The sample also excludes funds managed by other private equity firms, angel groups, non-us investors, buyout funds and corporations. The main sample of financings with a known US-based VC fund includes 7267 entrepreneurial firms in 17,485 financing or exit events. This sample represents 40% of all entrepreneurial firms and 33% of all financing events. These financings are merged to VC fund characteristics, for which we know the sequence of closings and total amount of committed capital. There are 1366 unique funds associated with 564 VCs, the latter of which constitutes 32% of all VCs in the database Dynamics of fund-raising Beyond the match of VC fund to entrepreneurial firm financing, we can track the within-vc dynamics of fund-raising. A VC fund closes when the partners and LPs agree that it can use some part of the committed capital to being investing. Such closings are often done over stages, possibly separated by three to nine months. We use the first closing date throughout the paper. A venture capitalist, when successful, raises multiple funds over time. However, many venture capitalists are unsuccessful in raising capital for a new fund when their past fund underperforms. The predictions of Section 1 simply require that a VC intends to raise a new fund, so inclusion of those that are unsuccessful is reasonable. The main challenge is the timing of the next fund discussed below. Figure 1 shows the empirical distribution between subsequent funds in a VC firm for 7 The authors thank Correlation Ventures, a quantitative VC fund for which Ewens is an advisor, for access to the data. 8 Compared to the full sample, the sample with VC fund raising data includes older entrepreneurial firms that have raised more capital in more financing rounds. The VCs in the sample have four times the investing experience (in terms of deals) as the investors without fund-raising data. 12

13 funds with at least 6 months and at most 8 years between them. 9 The dates are measured in years from fund N s first close date to the first close date of fund N + 1. The average years between fund closings is over 2.9 years, with the median 2.5. A VC fund s life is often split into two periods: the investment period (years 1 to 5) and the follow-on period (years 6-12). The investment period is one where we expect rapid investing of fund capital. One can track the capital invested by a fund over time to measure the dry powder, which is the difference between this sum and the total committed capital. This measure is difficult to estimate perfectly because we know which funds invest but often lack the individual amount contributed. Nonetheless, Figure 2 reports the pattern of dry powder across the final sample of funds. The slope is consistent with rapid investing early in a fund in the early years followed by a slowing. 10 We follow a similar methodology of Brown, Gredil, and Kaplan (2013) to include VC firms that fail to raise a next fund. The inclusion of these VC funds rests on the assumption that they attempted, but failed, to raise a next fund. The dry powder measure in Figure 2 helps with imputation of next fund close dates for funds that failed to have a follow-on. The median(mean) dry powder of funds that successfully raised a fund in the year window around closing is 71% (68%). 11 The number of new investments made by the fund peaks around this value of dry powder as well, which is often the time a VC begins to seek a new fund. For those funds that failed to raise a next fund, we impose the next fund close date to be the time when their current fund has invested 65% of the fund. 12 The resulting imputation accounts for 13% of the final sample of funds. 9 The graph reports all VC funds in the database rather than the sub-sample used in the analysis below. Funds closings less than six months appear to be sidecar or supplemental funds to already closed funds. 10 The figure does not show the full time window of years, so the average fraction does not fall to zero. 11 This number may seem high, however, most VCs reserve capital for the entrepreneurial firms in their capital which can nearly halve the dry powder. 12 The results are insensitive to using a value in the range [60, 75]. 13

14 2.2 Main variables There are several variables of interest tied to the hypotheses detailed in Section 1. The main independent variable is the time around a VC fund s next fund closing. That is, for every investment made by a VC firm out of fund N, we compare its date to the date of the first close of the firm s fund N + 1. For example, Fund N of the VC invests from 1998 to The firm s fund N + 1 has a first close in The variable Time to next close is in the range [ 3, 0] for all investments made out of fund N between 1998 and Similarly, the post-2001 financings have a range (0, 4]. The next set of variables consider the characteristics of the investments made by the VC. When making an equity or debt investment in an entrepreneurial firm, one of the first choices made is the size of the capital infusion. Given that VC funds are of a fixed size and duration, an increased use of capital signals higher activity of a fund. The variable Capital invested measures the VC fund s individual contribution to an entrepreneurial firm investment such that each fund s contribution sums to the total capital infusion. Hypothesis 1 above predicts that the rate of investment will peak before or near the VC s next fund closing. Beyond the capital invested, we also observe the type of security used in new financing events. The classification of debt versus equity financing part of Hypothesis 3 results in an additional variable Debt financing that is one if the security is a bridge loan, convertible note or straight debt. A common outcome for a VC-backed entrepreneurial firm is failure or shutdown. Such exits constitute 32% of entrepreneurial firm exits and return very little if any capital invested to investors. 13 The timing of writeoffs is the next variable we consider. The variable Writeoff is an indicator for these failure events which are dated in VentureSource using the date the firm s profile was last updated. Hypothesis 2 predicts that the fund-raising process will slow the write off probabilities in a VC fund s portfolio. 13 This percentage is almost surely an underestimate because many acquisitions are hidden failures (see Puri and Zarutskie (2012)). 14

15 The next portfolio strategy available to a VC investor is the timing of the re-investment in the entrepreneurial firm. The variable Years since last financing measures the time between a follow-on financing and the previous investment made by the VC investor. An entrepreneurial firm that has a delayed financing as predicted by Hypothesis 3 should have a larger Years since last financing all else equal. Of course, this variable is only defined conditional on the firm having a new financing event, so the writeoff variable above fills in the gap for non-financed firms. The last set of predictions in Section 1.3 concern the outcomes of entrepreneurial firms. We employ three measures of firm prospects. A popular measure of success for entrepreneurial firms is whether such firms have an IPO or are acquired the latter defined as cases in which reported exit values exceed twice the capital raised by the firm. As our first measure of firm prospects, we create an indicator variable that is one in case of either of these outcomes. 14 Our second measure is the total value created at exit after controlling for capital raised, which proxies return for investors (10% of total capital invested if failure). The final measure of firm success is the return earned by in a specific financing event. Log multiple captures the cash-on-cash return of an equity investment accounting for any dilutive effects of follow-on financings. Hypothesis 4 predicts that these firm and investment outcomes will differ around the fund closing event. 3 Empirical Evidence As discussed before, if VC actions are not influenced by agency problems, then VC funding cycle should not have any relationship with the actions of the VCs and the outcome of those actions in the current open fund. All major regressions below will estimate the following for a dependent variable Y ijkt where i is VC firm, j is fund, k is entrepreneurial firm and t 14 The results are robust to using simple acquisition; however, many acquisitions lacking exit valuations may be disguised failures. For clarity, we focus on the more accurate success measure. 15

16 represents time: 4 Y ijkt = β 0 + β 1 X it + β 2 Z kt + γ t + α i + ρ s τ s + ɛ ijkt. (1) s= 4,s 1 The main coefficients of interest are the ρ s which characterize the relationship between the time to the next fund close and Y ijkt. The other controls include VC firm time-varying variables X it, entrepreneurial firm characteristics Z kt, time fixed effects γ t measured at the financing events and the VC firm fixed effect α i. The inclusion of this last fixed effect is crucial for the interpretation of the estimates. These controls ensure that we compare the investing activity Y ijkt across a VC s multiple funds. Table 1 defines the controls variables used in all regressions. 3.1 Portfolio strategy Valuation around fund-raising Before investigating the portfolio strategies proposed above, we study the main object of interest in the existing literature: valuation. Several recent papers investigate whether VCs manipulate the value of their portfolio around fund-raising to signal their ability to generate returns. Jenkinson, Sousa, and Stucke (2013) and Brown, Gredil, and Kaplan (2013) show that the fund-raising incentive impacts how portfolio values are reported. These two papers both show some aggressive marking of investments around fund-raising, with Brown, Gredil, and Kaplan (2013) concluding that only the unsuccessful fund-raisers manipulate valuations. We revisit these questions using data on the individual investment valuations of a VC fund. The literature has thus far considered the quarterly valuations of the portfolio of such investments. Our approach differs in that any valuations occur only when an entrepreneurial firm receives capital, which is also an important choice variable that we study below. Table 2 presents the results of regressions of entrepreneurial firm pre-money valuation around next 16

17 fund closings. 15 Column 1 of Table 2 reports the coefficients ρ s of Equation 1 without any controls. The excluded dummy is the year prior to fund closing. The pattern of coefficients reinforces the conclusion that there is some conservatism on the part of VCs prior to fund-raising. Column 2 introduces a large set of controls importantly entrepreneurial firm size, age and industry resulting in weaker estimates for the years surrounding a fund closing. Inclusion of VC firm fixed effects in Column 3 provide no direct evidence for either conservatism or aggression in the valuation of entrepreneurial firms around fund-raising. Again, our sample requires a financing event, while the previous fund-level analyses provide a view of how a VC fund marks a portfolio after investment. Finally, column 4 focuses on the four year window around closing and finds no statistical difference in valuation. The result is not due to a lack of statistical power as the coefficient is economically small. We conclude that there is little evidence of VC fund manipulation of valuation around fund-raising through the investment events. 16 The remaining analyses focus on the portfolio strategies that are less visible to LPs as VC conduct fund-raising Investment We next test if and how VC investment activity changes around fund-raising activity. The simplest variable is the total capital invested in each financing event. As discussed in Section 2.2, we use the capital amount contributed by the specific fund, which is the disaggregated total capital invested in the syndicate. Figure 4 presents the univariate graph of the capital invested by the same fund around the next fund closing. Capital invested is scaled by the average capital invested in the same stage financings in the same year. The graph shows that capital invested rises dramatically in the year prior to the next fund close and is in close step with investments in new entrepreneurial firms. Capital invested peaks three months after 15 The pre-money is the valuation of the company before the capital infusion. 16 Some 85% of the funds successfully raise a fund, so these results are consistent with the result of Brown, Gredil, and Kaplan (2013). 17

18 the fund closing. Such a pattern is somewhat counter-intuitive because one would expect a VC fund to invest in more mature, later-stage deals as the fund ages. Table 3 reports the regression results for capital invested. Column 1 of Table 3 shows the OLS estimates of the log of capital invested around fund closing without any controls. As with all the remaining specifications, the excluded dummy is the year prior to fund-raising for time in [ 1, 0). Relative to this excluded group, the coefficient estimates suggest a peak of investment activity in the year prior to fund closing. Column 2 introduces controls for the VC firm, entrepreneurial firm and VC fund. The pattern of investing again shows a relative decrease after next fund closing. The final two columns introduce our preferred specification with VC fixed effects. Here we see that the coefficient flips sign around fund-closing, demonstrating a peak prior to next fund closing. There is a 15% drop in investing in the year after a fund closes. Note that all the specifications in columns 2-4 include controls for investment year, entrepreneurial firm stage and industry. Thus, any underlying trends in investment size that are captured by other investment characteristics cannot explain the results. Overall, the results in Figure 4 and Table 3 are the first evidences of the fund-raising process impacting the portfolio decisions of VCs. The patterns are consistent with a view that the average VC investor must signal that they can put capital to work and require a new fund to continue the investment strategy (H1). Of course, we have yet to show that the investment characteristics risk profile, stage, or quality changes around fund-raising. We conduct that analysis next Delay The next portfolio strategy we consider is in the class of delay of which we consider two types. The first is the propensity of the VC to write off an investment in their portfolio. In the presence of the fund raising process, one might predict that such signals be delayed until 18

19 post-fund raising. Here, the VC seeks to avoid sending any negative signals about portfolio performance. It is also possible that the VC can delay re-financing an entrepreneurial firm in their portfolio to avoid re-pricing. A new financing event is a mark to market and may not match the LP s expectations. We now consider these two types of delays and test hypothesis (H2). Figure 5 provides the first view of the relationship between fund-raising and write off probabilities. The graph reports the fraction of a VC fund s active portfolio that includes a write-off across the sample of funds. A write-off of a portfolio firm is dated in VentureSource and is distinct from a new financing event. On average, the rate of write-offs increase 40% in the two years after a fund closes (relative to the two years prior). Table 4 reports several regression specifications addressing the same question. Column 1 reports the simple OLS estimates for write-off probabilities around fund-raising. The dramatic shift in coefficient sign for One year after mimics the result of Figure 5. Of course, write-offs are more likely to occur in older entrepreneurial firms, which have raised capital and may have suffered some negative shock. Column 2 introduces the controls and the results remain. Finally, Columns 3 and 4 introduce VC firm fixed effects in a conditional logit specification. This specification addresses how a VC firm s propensity to have a portfolio firm shut down correlates across multiple fund closings. The results remain with fixed effects, while after condensing the data to the four year window around a fund closing in Column 4, the strong correlation with write-offs and fund-closings remains. Overall, the evidence is consistent with a story that VC firms delay the revelation of bad news in their current fund until after they raise the next fund. 17 Not all under-performing entrepreneurial firms in a VC fund s portfolio demand a writeoff. Instead, one can imagine a hierarchy of investment quality. The incentive to delay bad news could therefore occur in the timing of equity financing. How does such delay manifest 17 Note that the increase in writeoffs does not imply that the total portfolio of the VC is experiencing negative returns. The result simply implies that the timing of such exits depends on fund-closing. 19

20 itself in observables? Suppose a VC fund has subset of investments that it plans to delay re-financing. After the next fund closes, the concerns about negative signals to LPs fall and we should observe an up-tick in follow-on financings. Given the delay, the observed time between these financings should appear longer than predicted for firms financed immediately before fund-raising. Figure 3 presents the first view of the delay behavior. The graphs presents the average rate of new investments and follow-on investments. The former are the first investments made by a fund in an entrepreneurial firm, while the latter count the second, third and so on investments made by the same fund. The rate of follow-on investments increases dramatically after the next fund closes, which is consistent with the delay story. Figure 6 next shows a slight increase in the time between financings for completed investments. Table 5 provides a regression view of this behavior. Column 1 of Table 5 presents an OLS estimation of the relationship between the time between financings in entrepreneurial firms and fund-raising. Without controls, we see that the sign of the time dummies flips sign and become significant after fund closing. The lack of significance prior to fund-raising suggests that VC funds are not over-investing quickly in entrepreneurial firm prior to fund-raising. However, the positive and significant coefficient in the post-fund raising dummies is consistent with a delay of financing. VC funds appear to invest in entrepreneurial firms that took relatively longer than predicted immediately after their next fund closes. Column 2 introduces the standard controls with little change in conclusions. The last two columns introduce VC firm fixed effects and again show that even across within-firm funds, VCs who have recently closed a fund appear to invest in entrepreneurial firms that have waited relatively longer for capital. Firms financed after fund-closing take 8% longer (approximately one month) to raise capital all else equal. The collection of evidence points to delay behavior predicted by Hypothesis (H2). 20

21 3.1.4 Debt financing We have thus far shown that the VC delays the re-financing of some entrepreneurial firms in their portfolio. The hypothesis presented above suggest that these firms are lower quality because the VC aims to avoid marking parts of the portfolio during fund-raising. The next hypothesis predicts that the security choice for these post-fund closing firms will change as well. In particular, debt financing provides benefits to a VC with a recently closed fund and heterogenous quality of current fund investments. The additional benefits of debt finance stem from the ability to avoid re-marking a poor performing portfolio company and the senior position of debt in the capital structure. Hypothesis 3 predicts an increase in the incidence of debt financing after fund-raising. Figure 7 reports the average fraction of debt financings before and after a VC raises the next fund by quarter. The fraction of firms raising debt financing increases after the VC closes the next fund. Column 1 of Table 6 estimates the probability of debt financing around a VC s next funding. The dependent variable is one if the financing uses debt, where the specification is logit. After raising funds, VCs are more likely to finance firms with debt. The probability of a financing being debt financing increases approximately 17% a year after VC obtains funding. Column 2 adds industry, year and firm financing stage fixed effects and controls such as age of VC and firm, the amount of capital raised, whether the firm has revenues or if the firm is profitable and the size of the fund. The results remain robust to these controls. Column 3 performs a conditional logistic regression with VC firm fixed effects, and confirms the results of columns 1 and 2. Finally, column 4 compares the likelihood of debt financing just before and after the VC obtains new funding, and finds that there is significant increase in likelihood of debt financing post funding. These patterns are consistent with hypothesis (H3) and suggests that VCs use debt to limit their exposure to the below-average investments delayed during fund-raising. 21

22 3.2 Portfolio outcomes Now that we have shown fund-raising impacts portfolio strategy, we ask whether these changes matter for outcomes. Following the VC literature, we focus on the ultimate status of the entrepreneurial firm using the exit status (e.g. IPO), exit valuation and for a sub-sample of the data, realized returns. We propose that there exists a break in the quality of investments around fund-raising. Entrepreneurial firms for which the VC delayed capital investment or raised debt after fund-raising were argued to be the lower quality firms that could have sent negative signals to LPs. Following (H4), we predict that both investments around fund-raising and the choice of security will predict differences in entrepreneurial firm outcomes and investment returns Debt Finance and Cross-sectional Outcomes If debt financing is generally used for financing riskier firms, then their presence should correlate with less successful exits (Implication (H3)). We test this prediction with the firm outcomes detailed in Section 2.2: exit type (IPO or acquisition) and exit valuation. All regressions include the variable Raised debt which is equal to one if the entrepreneurial firm ever raised debt and includes controls for industry, year founded and total capital raised (i.e. size). The sample includes only entrepreneurial firms founded prior to 2006 to allow firms time for an exit event by the end of the sample period. 18 We further restrict the sample to entrepreneurial firms that had at least one financing event around the four-year window of a VC fund closing. Column 1 of Table 7 implies that entrepreneurial firms that borrow are 34% less likely to have a successful exit, while Column 3 shows that the exit valuations are 45% lower. The second and fourth column interact the debt indicator for whether the debt occurred after fund-raising. Thus, we are now asking whether the cross-section of debt financings in the 18 The results are qualitatively similar using the full sample as well. 22

23 [ 2, 2] window around fund-raising differ in their predictive power for exits. Hypothesis 4 (H4) predicts that VCs use debt for their riskier investments which they effectively avoided during fund-raising (i.e. [ 2, 0)). This higher risk should translate into higher failure rates, disproportionately when they occur after fund-raising. Column 2 demonstrates that debt in the post-fund-raising period explains the majority of the cross-sectional relationship between debt and outcomes. Similarly, the inclusion of the interaction in column 4 eliminates the significance of the debt control, while the size of the interaction coefficient is economically large. Overall, the evidence connecting debt and entrepreneurial outcomes is consistent with the predictions of the agency frictions in fund-raising. The evidence points to real differences in the types of firms financings by a VC fund before and after the next fund closes. We now investigate if such differences manifest themselves in investment returns Returns The final prediction concerns the quality differences in investments made around fund-raising. Quality of entrepreneurial firm is difficult to observe, so we rely on the final exit outcomes as a proxy. For a subset of the financings, we observe the final exit valuation of the entrepreneurial firm and the return earned by an investor in the financing. We predict that the set of investments made immediately prior to a fund-closing should out-perform those made immediately after. Ideally, we could create a continuously-traded long-short portfolio and ask whether such a strategy earns an alpha. Unfortunately, these investments are not marked to market except at non-periodic financings and eventual exit. The alternative test is in Table 8 where the specification includes VC firm fixed effects. The sample includes all investments made in the four year window around all successful and unsuccessful fund closings. The control 0-2 years after close is one if the investment occurred in the post-fund closing period. Column 1 presents the standard measure of success in the VC literature: whether an 23

24 entrepreneurial firm has an initial public offering or successful acquisition. Investments made in the post-period have an 19% lower probability on an IPO. This result is striking given that later investments in a fund are more developed and naturally have a higher success rate. We can also measure the final firm valuation for approximately 65% of the sample. Column 2 shows that post-fund closing investments exhibit an 21% lower exit valuation. Finally, these lower success rates and exit valuations manifest themselves in the returns regression of Column 3. Here, we take the log of the multiple which measures the cash-on-cash gross return earned by an investor in a financing (accounting for dilution). The log returns to post-fund closing investments are 11% lower than those made in the two years prior. All these patterns are consistent with the agency issues around fund-raising that results in VCs selectively reinvesting in entrepreneurial firms around fund-raising. 4 Robustness Tests 4.1 Fund raising outcomes The sample of VC funds includes both those that had a successful next fund closing and those that did not. The underlying assumption for pooling both types was that each VC firm/fund aims to raise a next fund (in the absence of partner retirement or shutdown of the firm). Many of the results documented above rely on the prediction that successful fund-raising lowers the control LPs have over a VC fund. Thus, we would expect different patterns of behavior between those VC funds that did and did not raise a next fund. Some 13% of the sample of funds fit in the latter category. Panel A of Table 9 repeats the four main regressions of investing, writeoffs, delay and debt. We introduce a dummy variable and interaction with Successfully closed that is one if the current fund was followed by a subsequent fund closing. The sample includes only financings done in the four year period around fund (or predicted) closing. 24

25 The coefficients on the interactions each mimic the sign of those found in the full sample of column 4 in Tables 3-6. In fact, the inclusion of the interaction dramatically lowers the significance of the simple dummy variable 0 2 years after close suggesting that the bulk of the post-closing relationship is driven by the sample of funds that successfully raised a next fund. For example, the coefficient on this dummy is approximately half as large for the first column as it is in Table 3. This result either implies that the ex-ante changes to the portfolio worked and readjusted or the successful fund-raising VCs could change behavior after LP control rights fell. In either situation, the results are consistent with our original predictions. 4.2 Young vs. old Several authors have shown that the incentive to manipulate a portfolio is highest when the VC firm is young and trying to raise a second fund. In first funds, the VC firm has little reputation and typically the portfolio has few realized outcomes to judge. Thus, the portfolio strategies studied here could differ by VC experience. Two other scenarios are possible. First, these strategies could be difficult to observe ex-ante by a LP, resulting in a VC firm exploiting them across all funds. Alternatively, younger VC firms may avoid the risk of the revelation of manipulation. Our hypotheses above are agnostic on the relationship between VC firm age and change in portfolio strategy. Panel B of Table 9 presents the results of the main specifications with a new variable First fund that is equal to one for the VC s first fund. The sample includes all VC firms with at least two VC funds and includes VC firm fixed effects. The first result that stands out in Panel B is the continued significance of the coefficient on the control 0 2 years after close. The signs are consistent with those found in the full sample regressions of Tables 3-6. The interaction term on Just after close X First fund lacks strong statistical significance, however, the sign of each coefficient is the opposite of 25

26 the main effect. The flipped sign is suggestive evidence that VCs are less prone to time their writeoffs, delay financing and use debt strategically around their second fund-raising. First-time funds may be more cautious with the discretion about portfolio characteristics. Overall, there is little strong evidence that the overall relationships are primarily driven by the age of the VC firm. 4.3 Additional robustness In unreported regressions, we rule out several alternative explanations. The results are not driven by investments made during the era as excluding funds raised in this period has no impact on the conclusions. Similarly, one concern is that fund-raising success and timing is correlated with public equity markets. If VCs find it easier to raise a fund during hot equity markets and such markets eventually mean revert, then many of our results could be driven by the timing rather than agency problems. The next robustness check separates VC funds by the characteristics of equity markets at the time of fund raising. Consider a simple break down of quarterly S&P 500 returns that labels quarters above the historical 90th percentile as hot. This dummy variable strongly correlates with the time to fund raising, as the probability of such markets is highest in the year prior to observed (imputed) fund closing. The concern that VC funds are raised at market peaks appears to be confirmed in the data. Table 10 presents two sub-sample analysis to address whether market timing is driving the main results. Panel A interacts the Just after close with a dummy equal to one if a VC fund (next) is raised during a hot equity market. If the market timing drives the results above, the inclusion of the interaction should reduce or eliminate the significance of the 0-2 years after close control. The results suggest that market timing is an important factor in the results, however, the loading on the main control is unchanged. Panel B considers the sub-sample of VC funds raised outside of hot markets using a VC firm FE specification. Only the delay column exhibits a lack of significance, 26

27 while the coefficient sign flip remains. Overall, we conclude that market timing does not explain the main conclusions. 5 Conclusion It is often said that a venture capitalist has two jobs: investing in entrepreneurial firms and raising their next fund. That next fund provides a steady stream of performance insensitive management fees for at least ten years. Performance evaluation by the capital providers to VCs (Limited partners) is thus an area ripe for manipulation. This paper asks whether the high information asymmetry between LPs and money managers (GPs) incentivizes portfolio manipulation. Our main contribution is to study a rich set of strategies available to VCs beyond the valuation and exit speed thus far studied in the literature. The empirical strategy compares a period of heightened control by the LP to the lowest point: fund-raising versus postfund closing. We test whether changes occur in terms of financing delay, strategic writeoffs, security choice and ultimately investment returns. The posited agency problems generated by the fund-raising incentive result in increase in writeoffs, debt financing and selective investment of quality portfolio companies during fund-raising. The long fund life and lumpy fund raising traditionally used in VC and other private equity setting provides real benefits to parties as the assets are illiquid and fund-raising is costly. Nonetheless, the behavior of the VCs in our sample invite study of alternative fund structure such as pledge funds or different disclosure regimes. In future drafts, we will study whether these behaviors result in better outcomes for GPs and worse outcomes for LPs. 27

28 References Aghion, P., and P. Bolton, 1992, An Incomplete Contracts Approach to Financial Contracting, The Review of Economic Studies, 59(3), Barrot, J.-N., 2014, Investor Horizon and Innovation: Evidence from Private Equity Funds, Massachusetts Institute of Technology Working Paper. Brown, G. W., O. Gredil, and S. N. Kaplan, 2013, Do Private Equity Funds Game Returns?, University of Chicago and University of North Carolina Working Paper. Chava, S., and M. R. Roberts, 2008, How Does Financing Impact Investment? The Role of Debt Covenants, Journal of Finance, 63(5), Chevalier, J., and G. Ellison, 1997, Risk Taking by Mutual Funds as a Response to Incentives, Journal of Political Economy, 105(6), pp Chung, J., B. A. Sensoy, L. Stern, and M. S. Weisbach, 2012, Pay for Performance from Future Fund Flows: The Case of Private Equity, Review of Financial Studies, 25(11), Cumming, D., 2005, Capital Structure in Venture Finance, Journal of Corporate Finance, 11, Dass, N., H.-C. Hsu, V. Nanda, and Q. Wang, 2012, Churning in Venture Capital Investments, Georgia Institute of Technology Working Paper. Dewatripont, M., and J. Tirole, 1994, A Theory of Debt and Equity: Diversity of Securities and Manager-Shareholder Congruence, The Quarterly Journal of Economics, 109(4), pp Ewens, M., C. M. Jones, and M. Rhodes-Kropf, 2013, The Price of Diversifiable Risk in Venture Capital and Private Equity, Review of Financial Studies, 26(8), Gompers, P., 1996, Grandstanding in the venture capital industry, Journal of Financial Economics, 42(1). Grossman, S. J., and O. D. Hart, 1986, The Costs and Benefits of Ownership: A Theory of Vertical and Lateral Integration, Journal of Political Economy, 94(4), pp

29 Hellmann, T., L. Lindsey, and M. Puri, 2008, Building Relationships Early: Banks in Venture Capital, Review of Financial Studies, 21(2), Jenkinson, T., M. Sousa, and R. Stucke, 2013, How Fair are the Valuations of Private Equity Funds?, University of Oxford Working Paper. Kaplan, S. N., and P. Strömberg, 2003, Financial Contracting Theory Meets the Real World: An Empirical Analysis of Venture Capital Contracts, Review of Economic Studies, 70(2). Lim, J., B. A. Sensoy, and M. S. Weisbach, 2013, Indirect Incentives of Hedge Fund Managers, Working Paper 18903, National Bureau of Economic Research. Lin, H., and D. Paravisini, 2012, Delegated Monitoring of Fraud: Contractual Incentives, Columbia Business School Working Paper. The Role of Non- Metrick, A., and A. Yasuda, 2010, The economics of private equity funds, Review of Financial Studies, 23(6), Nini, G., D. C. Smith, and A. Sufi, 2012, Creditor Control Rights, Corporate Governance, and Firm Value, Review of Financial Studies, 25(6), Puri, M., and R. Zarutskie, 2012, On the Life Cycle Dynamics of Venture-Capital-and Non-Venture-Capital-Financed Firms, The Journal of Finance, 67(6), Sirri, E. R., and P. Tufano, 1998, Costly Search and Mutual Fund Flows, The Journal of Finance, 53(5), pp

30 6 Figures and Tables Figure 1: Time between fund closes for successful follow-on funds Notes: Figure reports the distribution between each VCs fund closings for all funds that closed at least 6 months apart and less than 8 years apart. A fund close date is assigned at the first close date (there can be multiple closes over time). The solid line in the graph represents the mean time between fund closings. The dashed line reports the median years between funds. 30

31 Figure 2: Dry powder around fund-raising Notes: Figure reports the estimated fraction of capital remaining in the current fund around the closing of the next VC fund. Sample includes all funds that were followed by another fund closing. 31

32 Figure 3: New and follow-on investments within fund Notes: Figure reports the average number of two types of investments made by a fund. New investments are the first investment in an entrepreneurial firm made by a fund and Follow-on investments are all subsequent investments in the entrepreneurial firm made by the fund. 32

33 Figure 4: Normalized capital invested around fund-raising Notes: Figure reports the average capital invested in financings around fund closings. The capital invested is normalized by the average capital invested in the same stage financings (to address the overall trend in capital invested during fund life). Capital invested is the specific amount contributed by the specific fund rather than the total investment amount. 33

34 Figure 5: Write offs around fund-raising Notes: Figure reports the average fraction of write offs in a VC fund s portfolio around fundraising. A write-off is an instance where an entrepreneurial firm in the portfolio is listed as failed or had its assets acquired. 34

35 Figure 6: Delayed financing around fund-raising Notes: Figure reports the average time between entrepreneurial financing events financed by the same VC fund. The figure also reports the fraction of financings in each time period around financings that are follow-on. Follow-on financings are those where the VC fund already has an equity position in the entrepreneurial firm. 35

36 Figure 7: Use of debt financings around fund-raising Notes: Figure reports the average fraction of debt financings before and after a VC raises a next fund. 36

37 Figure 8: Writeoffs around fund-raising: VCs with and without next funds Notes: Figure reports the average fraction of writeoff financings before and after a VC raises a next fund. The Raised fund includes only those VC funds that were followed by a successful next fund and the Did not raise fund includes funds that failed to do so. 37

38 Figure 9: Debt financings around fund-raising: VCs with and without next funds Notes: Figure reports the average fraction of debt financings before and after a VC raises a next fund. The Raised fund includes only those VC funds that were followed by a successful next fund and the Did not raise fund includes funds that failed to do so. 38

39 Table 1: Variable description Notes: Definitions of the main variables used throughout the text. Variable Description N years prior A dummy for a VC fund s investment that occurred N (0 to 4) years (rounded to the quarter) to the next realized or predicted fund closing. N years after A dummy for a VC fund s investment that occurred N years (rounded to the quarter) after the first fund to close after the current fund. 0 2 years after close A dummy equal to one if the VC fund s investment occurred in the year of or year immediately after the next fund closing. The excluded group are the financings in the two year prior (samples with this control have a four year window around each fund closing). Log VC age The log of the number of years since the VC firm first invested. (years) Log total capital raised The log of the sum of capital raised (in millions) as of each entrepreneurial firm s financings. Has revenues A dummy equal to one if at the time of the entrepreneurial firm s financing the firm reported revenue. Profitable A dummy equal to one if at the time of the entrepreneurial firm s financing the firm reported profits. Log firm age The log of the age of the entrepreneurial firm as of each financing (yrs.) event. Log fund size The log of the total capital raised in the current fund (in millions). (m) Industry FE Fin. year FE Stage FE Log pre-money Successfully raised fund VC s first fund IPO Log return Categorical dummies for one of four major industry categories: Business/Consumer/Retail, Healthcare, Information Technology and Other. Dummies for the year of the entrepreneurial firm financing event. Fixed effects for the development stage of the entrepreneurial firm: early, middle and late. The log of the financing pre-money valuation which is the equity valuation of the entrepreneurial firm prior to the VC s capital infusion. Indicator equal to one if the current VC fund was followed (by 2013) by a new fund closing. Indicator equal to one for the first fund raised by a VC firm (for the sample of VCs with at least two funds). An indicator for whether the entrepreneurial firm had an IPO or a successful acquisition (greater than $100m valuation) as of the end of The log of the gross multiple for the return to investing in a financing event. The multiple captures the cash-on-cash return accounting for any dilution and assumes common equity. Many returns are missing due to missing valuations. Log valuation The log of the value of the entrepreneurial firm at exit, set to 10% of total capital raised if a failure. Some exit valuations are missing because the sale price is not reported. 39

40 Table 2: Finance valuation around fund-raising Notes: Table reports the estimates of both OLS and VC fixed effect estimates for a regression of the log pre-money valuation of the entrepreneurial firm for VC fund investments on a set of observables. The dependent variable may by missing, however, the fixed effect specifications require a VC firm to have at least two known valuations. The unit of observation is the entrepreneurial firm financing event paired with a VC fund (thus, the financing may be repeated if there are multiple funds investing). The sample includes all entrepreneurial firm financings between 1992 and 2013 for which a VC investor had a fund close before The main controls are dummy variables for the time around the VC funds subsequent fund raising. For example Four years prior is a dummy equal to one if the VC fund s investment in an entrepreneurial firm investment occurred four years (rounded) to the next fund closing. The excluded category is the year prior to the fund closing. Other control variables are defined in Table 1. Columns 1 and 2 are OLS estimates, while columns 3 and 4 use VC firm fixed effects. Column 4 narrows the sample to the four-year period around the next fund closing and introduces a dummy 0 2 years after close that is one for the post-closing period. Standard errors in parentheses, clustered at the VC firm level.,, represent significance at the 10%, 5% and 1% level respectively. Log pre-money valuation (1) (2) (3) (4) Four years prior (0.0538) (0.0311) (0.0342) Three years prior (0.0411) (0.0241) (0.0263) Two years prior (0.0342) (0.0203) (0.0218) One year after (0.0329) (0.0193) (0.0215) Two years after (0.0405) (0.0255) (0.0273) Three years after (0.0461) (0.0311) (0.0356) Four years after (0.0557) (0.0411) (0.0447) 0-2 years after close (0.0204) Log VC age (years) ( ) (0.0206) (0.0283) Log total capital raised (0.0154) (0.0138) (0.0160) Has revenues (0.0211) (0.0161) (0.0171) Profitable (0.0650) (0.0563) (0.0711) Log firm age (yrs.) ( ) ( ) ( ) Log fund size (m) ( ) (0.0158) (0.0207) Constant (0.0256) (0.102) (0.109) (0.133) Observations R Num. VCs Num funds Num. firms Specification OLS OLS FE FE Industry FE? N Y Y Y Fin. year FE? N Y Y Y Stage FE? N Y Y Y 40

41 Table 3: Investment amounts around fund-raising Notes: Table reports the estimates of both OLS and fixed effect estimates for a regression of the log of capital invested by a VC fund on a set of observables (in millions). The unit of observation is the entrepreneurial firm financing event paired with a VC fund (thus, the financing may be repeated if there are multiple funds investing). The sample includes all entrepreneurial firm financings between 1992 and 2013 for which a VC investor had a fund close before The main controls are dummy variables for the time around the VC funds subsequent fund raising. For example Four years prior is a dummy equal to one if the VC fund s investment in an entrepreneurial firm investment occurred four years (rounded) to the next fund closing. The excluded category is the year prior to the fund closing. Other control variables are defined in Table 1. Columns 1 and 2 are OLS estimates, while columns 3 and 4 use VC firm fixed effects. Column 4 narrows the sample to the four-year period around the next fund closing and introduces a dummy 0 2 years after close that is one for the post-closing period. Standard errors in parentheses, clustered at the VC firm level.,, represent significance at the 10%, 5% and 1% level respectively. Investment amount around fund-raising (1) (2) (3) (4) Four years prior (0.0393) (0.0312) (0.0412) Three years prior (0.0299) (0.0239) (0.0284) Two years prior (0.0246) (0.0203) (0.0209) One year after (0.0245) (0.0204) (0.0222) Two years after (0.0292) (0.0255) (0.0323) Three years after (0.0351) (0.0309) (0.0380) Four years after (0.0411) (0.0369) (0.0451) 0-2 years after close (0.0231) Log VC age (years) ( ) (0.0239) (0.0286) Log total capital raised (0.0121) (0.0125) (0.0151) Has revenues (0.0186) (0.0160) (0.0186) Profitable (0.0533) (0.0491) (0.0554) Log firm age (yrs.) ( ) ( ) ( ) Log fund size (m) ( ) (0.0332) (0.0350) Constant (0.0174) (0.0805) (0.136) (0.159) Observations Pseudo R 2 Num. VCs Num funds Num. firms Specification OLS OLS FE FE Industry FE? N Y Y Y Fin. year FE? N Y Y Y Stage FE? N Y Y Y 41

42 Table 4: Write off probability around fund-raising Notes: Table reports the estimates of both OLS and conditional logit fixed effect estimates for a regression of the entrepreneurial firm failure dummy on a set of observables. The dependent variable is equal to one if the entrepreneurial firm was written off by its investors at the date observed (zero for financing or other exit events). The unit of observation is the entrepreneurial firm financing event paired with a VC fund (thus, the financing may be repeated if there are multiple funds investing). The sample includes all entrepreneurial firm financings between 1992 and 2013 for which a VC investor had a fund close before The main controls are dummy variables for the time around the VC funds subsequent fund raising. For example Four years prior is a dummy equal to one if the VC fund s investment in an entrepreneurial firm investment occurred four years (rounded) to the next fund closing. The excluded category is the year prior to the fund closing. Other control variables are defined in Table 1. Columns 1 and 2 are OLS estimates, while columns 3 and 4 use VC firm fixed effects with conditional logit. Column 4 narrows the sample to the four-year period around the next fund closing and introduces a dummy 0 2 years after close that is one for the post-closing period. Standard errors in parentheses, clustered at the VC firm level.,, represent significance at the 10%, 5% and 1% level respectively. Write off company? (1) (2) (3) (4) Four years prior (0.0873) (0.0992) (0.199) Three years prior (0.0686) (0.0793) (0.170) Two years prior (0.0554) (0.0674) (0.145) One year after (0.0449) (0.0538) (0.114) Two years after (0.0561) (0.0635) (0.124) Three years after (0.0548) (0.0596) (0.121) Four years after (0.0603) (0.0654) (0.148) 0-2 years after close (0.0977) Log VC age (years) (0.0326) (0.191) (0.256) Log total capital raised (0.0217) (0.0440) (0.0536) Has revenues (0.0356) (0.0750) (0.105) Profitable (0.117) (0.229) (0.389) Log firm age (yrs.) ( ) (0.0141) (0.0203) Log fund size (m) (0.0277) (0.0868) (0.105) Constant (0.0403) (0.403) Observations Pseudo R Num. VCs Num funds Num. firms Specification OLS OLS C. Logit C. Logit Industry FE? N Y Y Y Fin. year FE? N Y Y Y Stage FE? N Y Y Y 42

43 Table 5: Financing delay around fund-raising Notes: Table reports the estimates of both OLS and VC fixed effect estimates for a regression of the years between two investments made by a VC fund in the same entrepreneurial firm on a set of observables. The dependent variable is the number of years between such events, which is dropped if no financing occurs. The unit of observation is the entrepreneurial firm financing event paired with a VC fund (thus, the financing may be repeated if there are multiple funds investing). The sample includes all entrepreneurial firm financings between 1992 and 2013 for which a VC investor had a fund close before The main controls are dummy variables for the time around the VC funds subsequent fund raising. For example Four years prior is a dummy equal to one if the VC fund s investment in an entrepreneurial firm investment occurred four years (rounded) to the next fund closing. The excluded category is the year prior to the fund closing. Other control variables are defined in Table 1. Columns 1 and 2 are OLS estimates, while columns 3 and 4 use VC firm fixed effects. Column 4 narrows the sample to the four-year period around the next fund closing and introduces a dummy 0 2 years after close that is one for the post-closing period. Standard errors in parentheses, clustered at the VC firm level.,, represent significance at the 10%, 5% and 1% level respectively. Years since last capital infusion (1) (2) (3) (4) Four years prior (0.0288) (0.0266) (0.0303) Three years prior (0.0239) (0.0215) (0.0266) Two years prior (0.0188) (0.0168) (0.0182) One year after (0.0194) (0.0176) (0.0176) Two years after (0.0205) (0.0202) (0.0238) Three years after (0.0265) (0.0264) (0.0303) Four years after (0.0311) (0.0323) (0.0380) 0-2 years after close (0.0174) Log VC age (years) ( ) (0.0164) (0.0209) Log total capital raised (0.0101) ( ) (0.0103) Has revenues (0.0197) (0.0167) (0.0184) Profitable (0.0685) (0.0636) (0.0729) Log firm age (yrs.) ( ) ( ) ( ) Log fund size (m) ( ) (0.0168) (0.0185) Constant (0.0127) (0.119) (0.113) (0.128) Observations R Num. VCs Num funds Num. firms Specification OLS OLS FE FE Industry FE? N Y Y Y Fin. year FE? N Y Y Y Stage FE? N Y Y Y 43

44 Table 6: Use of debt financings around fund-raising Notes: Table reports the estimates of both OLS and VC fixed effect estimates for a regression of a dummy for whether a financing event was debt on a set of observables. The dependent variable is equal to one if the financing is a bridge loan, credit line or straight debt. The unit of observation is the entrepreneurial firm financing event paired with a VC fund (thus, the financing may be repeated if there are multiple funds investing). The sample includes all entrepreneurial firm financings between 1992 and 2013 for which a VC investor had a fund close before The main controls are dummy variables for the time around the VC funds subsequent fund raising. For example Four years prior is a dummy equal to one if the VC fund s investment in an entrepreneurial firm investment occurred four years (rounded) to the next fund closing. The excluded category is the year prior to the fund closing. Other control variables are defined in Table 1. Columns 1 and 2 are OLS estimates, while columns 3 and 4 use VC firm fixed effects with conditional logit. Column 4 narrows the sample to the four-year period around the next fund closing and introduces a dummy 0 2 years after close that is one for the post-closing period. Standard errors in parentheses, clustered at the VC firm level.,, represent significance at the 10%, 5% and 1% level respectively. Debt financing? (1) (2) (3) (4) Four years prior (0.0865) (0.0828) (0.176) Three years prior (0.0686) (0.0657) (0.128) Two years prior (0.0543) (0.0561) (0.113) One year after (0.0485) (0.0481) (0.0958) Two years after (0.0578) (0.0584) (0.114) Three years after (0.0657) (0.0650) (0.122) Four years after (0.0722) (0.0709) (0.148) 0-2 years after close (0.0928) Log VC age (years) (0.0363) (0.116) (0.169) Log total capital raised (0.0217) (0.0421) (0.0474) Has revenues (0.0394) (0.0806) (0.104) Profitable (0.0932) (0.182) (0.244) Log firm age (yrs.) ( ) (0.0124) (0.0142) Log fund size (m) (0.0268) (0.0749) (0.123) Constant (0.0487) (0.331) Observations Pseudo R Num. VCs Num funds Num. firms Specification OLS OLS C. Logit C. Logit Industry FE? N Y Y Y Fin. year FE? N Y Y Y Stage FE? N Y Y Y 44

45 Table 7: Debt in the Cross-Section Notes: Cross-sectional regressions using the sample of entrepreneurial firms founded between 1992 and 2007 that are in the main debt regression in Table 6. The IPO/Acq. column reports logit regressions has a dependent variable is equal to one if the entrepreneurial firm exited via IPO or successful acquisition as of the end of log(exit value) is the log of the final sale price of the firm at IPO, acquisition (if reported) or at shutdown (set to 10% of total capital raised). An entrepreneurial firm is in the sample if they had at least one financing with a VC fund in the [ 2, 2] period around closing and was in the main sample of Tables 3-6. Raised debt is equal to one if the entrepreneurial firm ever issued debt. Raised debt X after fund closed is one if the debt financing occurred after one of the firm s VCs just raised a fund (two years since). Total capital raised is the sum of all capital raised by the entrepreneurial firm. Age at exit or end of sample assumes the first observed investment is the founding date of the entrepreneurial firm and defines age as the number of years to an exit or end of the sample (12/2013). Log total financings is the log total number of financings. All regressions include controls for year founding and industry. Standard errors in parentheses, cluster at the firm founding year.,, represent significance at the 10%, 5% and 1% level respectively. IPO/Acq. IPO/Acq. log(exit value) log(exit value) Raised debt (0.218) (0.233) (0.181) (0.210) Raised debt X after fund closed (0.134) (0.209) Log total financings (0.187) (0.186) (0.226) (0.222) Log capital raised (0.0648) (0.0651) (0.106) (0.106) Constant (0.476) (0.480) (0.436) (0.435) Observations Pseudo R R Num. firms Model Logit Logit OLS OLS Industry FE? Y Y Y Y Founding year FE? Y Y Y Y 45

46 Table 8: Entrepreneurial firm outcomes around fund-raising Notes: Table reports the VC firm fixed effect estimates of several entrepreneurial firm outcome measures on a set of observables. In column (1) the dependent variable is equal to one if the entrepreneurial firm went public by the end of the sample. Column (2) uses the log of the exit valuations (log(.01) if failed) for the firms that have a known value, while Column (3) uses the log of the gross multiple return of an investment accounting for dilution. The unit of observation is the entrepreneurial firm financing event paired with a VC fund (thus, the financing may be repeated if there are multiple funds investing). The sample includes all entrepreneurial firm financings between 1992 and 2013 for which a VC investor had a fund close before We consider the first investment made by a fund in the entrepreneurial firm in the four year window around fund-raising. The variable 0 2 years after close is a dummy for the post-closing investments. Other control variables are defined in Table 1. Columns 1 is a conditional logit with a VC firm FE, while columns 2 and 3 use standard VC firm fixed effects. Standard errors in parentheses, clustered at the VC firm level.,, represent significance at the 10%, 5% and 1% level respectively. Financing outcomes (1) (2) (3) IPO Log Valuation Log Return 0-2 years after close (0.0595) (0.0579) (0.0442) Log VC age (years) (0.0734) (0.0978) (0.0858) Mid-stage (0.0897) (0.105) (0.0853) Late stage (0.0922) (0.122) (0.0887) Log total capital raised (0.0354) (0.0417) (0.0361) Has revenues (0.0639) (0.0806) (0.0695) Profitable (0.152) (0.176) (0.186) Log firm age (yrs.) (0.0103) (0.0106) ( ) Log fund size (m) (0.0508) (0.0952) (0.0654) Years to exit (0.0146) Constant (0.430) (0.311) Observations Pseudo R R Num. VCs Num funds Num. firms Specification C. Logit FE FE VC FE? Y Y Y Industry FE? Y Y Y Fin. year FE? Y Y Y 46

47 Table 9: Robustness checks: successful fund-raising and first-time funds Notes: Table repeats the main regressions in Tables 3-6 introducing two interaction variables measured at the VC fund-level. Each column considers the sample of financings made by a VC fund in the [ 2, 2] year period around the next fund closing. Panel A has the dummy variable Successfully raised fund that is equal to one if the current fund was followed by a successful next fund raised. That dummy is interacted with the dummy for the post-close period [0, 2]. For funds that do not raise a next fund, we follow the methodology detailed in Section 2.1 using the capital dry powder proxy. Panel B considers the set of VC funds that had a successful next fund and interacts the post-close dummy variable 0 2 years after close with a dummy equal to one if the fund is the VC firm s first fund. All regressions include the controls and fixed effects as found in Column 4 of Tables 3-6 ( Controls and FE? ). Standard errors in parentheses, clustered at the VC firm level.,, represent significance at the 10%, 5% and 1% level respectively. Panel A Interaction: Successfully raised next fund Log raised Writeoff Delay Debt 0-2 years after close (0.0822) (0.269) (0.0509) (0.189) Just after X Successfully closed (0.0853) (0.291) (0.0527) (0.205) Successfully raised fund (0.0800) (0.426) (0.0776) (0.365) Observations R Pseudo R Num funds Controls and FE? Y Y Y Y Panel B Interaction: First fund vs. other funds Log raised Writeoff Delay Debt 0-2 years after close (0.0280) (0.153) (0.0196) (0.118) Just after close X First fund (0.0562) (0.656) (0.0389) (0.333) VC s first fund (0.0545) (0.444) (0.0368) (0.394) Observations R Pseudo R Num funds Controls and FE? Y Y Y Y 47

48 Table 10: Robustness checks: fund-raising in hot markets Notes: Table repeats the main regressions in Tables 3-6 introducing an interaction variables measured at the VC fund-level and a sub-sample of VC funds. Each column considers the sample of financings made by a VC fund in the [ 2, 2] year period around the next fund closing. Panel A has the dummy variable Hot market before that is equal to one if the new fund was raised during a hot equity market, defined as an S&P 500 quarterly return above the historical 90th percentile. That dummy is interacted with the dummy for the post-close period [0, 2]. Panel B considers the set of VC funds that were followed by a fund raised outside of these hot equity markets and repeats the specifications in column 3 of Tables 3-6. All regressions include the controls and fixed effects as found in Column 4 of Tables 3-6 ( Controls and FE? ). Standard errors in parentheses, clustered at the VC firm level.,, represent significance at the 10%, 5% and 1% level respectively. Panel A Interaction: Raised fund in hot market Log raised Writeoff Delay Debt 0-2 years after close (0.0259) (0.116) (0.0203) (0.117) Just after close X Hot market before (0.0468) (0.222) (0.0305) (0.200) Fund raised hot market (0.0485) (0.233) (0.0247) (0.170) Observations R Pseudo R Number funds Controls and FE? Y Y Y Y Panel B Funds raised outside of hot markets Log raised Writeoff Delay Debt Four years prior (0.0481) (0.226) (0.0391) (0.203) Three years prior (0.0366) (0.196) (0.0319) (0.142) Two years prior (0.0254) (0.159) (0.0232) (0.137) One year after (0.0261) (0.137) (0.0201) (0.120) Two years after (0.0381) (0.160) (0.0287) (0.140) Three years after (0.0422) (0.156) (0.0370) (0.137) Four years after (0.0496) (0.184) (0.0419) (0.177) Observations R Pseudo R Number funds Controls and FE? Y Y Y Y 48