Why do entrepreneurs switch lead venture capitalists?
"A venture capital firm should deliver and focus on its core competency and move on. Just like startups change CEOs as they mature, shouldn't companies change VCs as they mature ? If there is a good startup CEO, shouldn't there also be good startup VCs? Some people can take a company from startup idea to billion-dollar business, but most need to be replaced along the way--this is true for both management teams and board members. (Hoffman, 2009).
There exists substantial uncertainty in venture capital investments, especially in the early stages. It is well established that there exists a positive sorting mechanism in the venture capital market in which more reputable venture capitalists (VCs) invest in better companies (Hsu, 2004; Knill, 2009; Sorensen, 2007). More recently, Bengtsson and Hsu (2010) show that the personal characteristics of VCs and founders also help explain the matching between VCs and entrepreneurial firms. Yavuz, Marquez, and Nanda (2010) show that fund managers may voluntarily limit fund size in order to match with high-quality entrepreneurial firms and consistently deliver superior returns. Given the importance of the matching in private equity investments, how the relationship between VCs and entrepreneurs evolve, however, is more or less neglected. Venture capital-backed companies typically take 2-7 years before they come to fruition in an exit, such as an initial public offering (IPO) or an acquisition. As both VCs and entrepreneurs learn about the potential of a start-up venture over time, it is natural to expect the two-sided matching between VCs, in particular, lead VCs, and entrepreneurs to dynamically adjust.
In this article, we explore the switching dynamic between entrepreneurs and their lead VCs and the underlying motivations. We define switching as when the lead VC firm in previous rounds is not the lead VC in the current or later rounds any more, where lead VC is defined as the one that had invested the largest cumulative amount of capital by the time of a specific round. (1) Switching lead VCs is more common than one might expect. We show that during the period from 1991 to 2002, 23% of the follow-on rounds of financing have lead VCs that are different from those of previous rounds. Further, we examine the consequence of switching lead VCs, for instance, the amount of capital raised and the valuation in the new round. Among the various factors examined, our key interest includes the impact of VC reputation and new information learned about the perceived quality of entrepreneurial firms on switching decisions, and whether companies switch to more reputable VCs regardless of less favorable financial terms, such as valuations, as Hsu (2004) finds these are important determinants of matching when the entrepreneur-VC relationship was initiated.
Theoretically, the switching process can be initiated by VCs or entrepreneurs. We conjecture that entrepreneurs will initiate switching if they view that the benefits of having a more reputable VC rather than the existing VC are greater than the cost of switching. This hypothesis predicts that ventures have a higher probability of success if entrepreneurs are successful in obtaining subsequent funding from more reputable outside VCs. To get the certification of more reputable VCs, entrepreneurs are likely to accept less favorable financial terms (e.g., valuation) than what they might have been able to bargain with the existing VCs. On the other hand, switching can also happen when existing VCs are not satisfied with the performance or the exit potential of the entrepreneurial firms. As outside VCs will interpret the failure to reinvest by exiting VCs as a negative signal, if entrepreneurs are able to obtain any subsequent funding from other VCs at all, then the new VCs are likely to be less reputable than the existing ones. The new VCs are also likely to charge a higher discount rate, which indicates a lower valuation.
Our empirical analysis starts with analyzing the determinants of switching. We correct for the potential survivorship bias by using the Heckman selection model. Then we apply the Heckman two-stage framework to control for the endogeneity of switching and examine the impact of switching on subsequent round financial terms and searching cost.
Three major findings emerge from our analysis. First, we find that entrepreneurial firms with upwardly revised perceived quality are more likely to switch lead VCs. This trend suggests that the learning of new information regarding the potential of the company by VCs and entrepreneurs drives the adjustment of the matching to reach the positive sorting equilibrium. Second, we find that entrepreneurial firms with upwardly revised perceived quality are more likely to switch to more reputable new lead VCs, while firms with downwardly revised perceived quality are more likely to switch to less reputable new lead VCs. Third, we document that in general, switchers obtain a larger investment and a higher valuation in follow-on rounds than non-switchers, but accept a smaller investment size and lower valuation if they switch to more reputable VCs. Furthermore, we show it typically takes more time for companies that switch to new VCs to obtain the subsequent round of financing. This is particularly true for switchers with downwardly revised perceived quality.
Our empirical findings suggest that there is considerable re-balancing of the VC-company matches. The pattern of rebalancing can at least be partly explained by the "graduation" arguments. This has implications about the "liquidity" and the magnitude of search costs in the VC market. Furthermore, we show that switching between VCs and entrepreneurs occurs often and in a systematic fashion. Researchers should be careful when aggregating the financing history of a VC-backed company. The fact that companies frequently switch lead VCs has implications for our understanding of staging, contract negotiations, and economic role of initial company-VC matches. The duration between staged financing rounds does not necessarily measure VC monitoring and governance as previously suggested (Gompers, 1995; Gompers & Lerner, 2004, Chapter 8) if entrepreneurs switch their investors frequently. Contract negotiations are much more heavily influenced by entrepreneurs' outside options than that which has previously been considered in the literature. Initial company-VC matches are less important if entrepreneurs frequently switch VCs. Other implications of our results are considered herein. Our findings suggest many avenues for future research.
The remainder of the article is organized as follows. The second section develops the theory and describes empirically testable hypotheses. The third section describes the data and reports the summary statistics. The fourth section describes the empirical methods and presents the empirical results. The final section concludes the article and discusses the implications of our findings.
Our article relates to the theoretical and empirical literature on how entrepreneurs select/switch their investors. In the venture capital literature, Sorensen (2007) suggests that there is a positive sorting between VCs and entrepreneurs. Hsu (2004) documents that entrepreneurs favor more reputable VCs and are willing to turn down the best financial terms in order to be associated with reputable VCs. Our article contributes to this body of literature by analyzing the dynamics of a positive sorting in the VC industry due to the two-sided selection between entrepreneurs and VCs as new information is learned by both parties. A significant body of work shows the quality of venture capital funds and their investment networks influences the spread, structure, and outcomes in venture capital and entrepreneurship (see, e.g., Ahlstrom & Bruton, 2006; Bruton, Fried, & Manigart, 2005; Carolis, Litzky, & Eddleston, 2009; Guler & Guillen, 2010; Jaaskelainen, Maula, & Seppa, 2006; Keil, Maula, & Wilson, 2010; Makela & Maula, 2006; Manigart et al., 2006; Meuleman & Wright, 2011; Mosey & Wright, 2007; Sorenson & Stuart, 2000, 2008; Walske & Zacharakis, 2009; Wright & Lockett, 2003). Herein we consider the roles of VC reputation and firms' perceived quality in the dynamic matching process.
Switching after an Upward Revision of Venture Quality
It is widely recognized that problems of information asymmetry and agency costs are pronounced in VC capital deals (Gompers & Lerner, 2004). Entrepreneurs know more about the technology and their own abilities than outside investors such as VCs. VCs are unable to perfectly monitor entrepreneurs, and contractual arrangements at best mitigate agency problems and do not eliminate agency problems (Cumming & Johan, 2009). Information asymmetries imply that there is imperfect matching between entrepreneurs and VCs in their first-round deals, thereby creating incentives for both entrepreneurs and VCs to switch in subsequent staged financing rounds. Agency problems may arise between different VCs whereby the original VC encourages a new VC to take on a venture after the perceived quality has been revised downward in a subsequent financing round (Cestone, Lerner, & White, 2006; Cumming, 2005, 2006; Gompers & Lerner, Chapter 11; Kunze, 1990; Lerner, 1994b). That is, an original VC may encourage a new VC to finance a negative net present value (NPV) project (especially in cases where the original VC has priority over the new VC), or encourage the VC to pay too much per share, or contribute too much capital relative to the needs of the entrepreneurial firm (see the Appendix in Cumming, 2005). Alternatively, agency problems may arise when a new reputable VC encourages an entrepreneur with upwardly revised perceived quality to leave an existing relationship with a prior less reputable VCs.
The economic benefits to an entrepreneurial firm of associating itself with reputable VCs appear to be well established, and therefore, the scope for potential agency problems via switching is pronounced. Lerner (1994a) reports that seasoned VCs appear to be particularly proficient at taking companies public near market peaks. Gompers and Lerner (2004, Chapter 16) find that younger VC firms strive for quick formation of their reputation in the VC industry by taking their portfolio companies public sooner, and under-pricing them. Hochberg, Ljungqvist, and Lu (2007) document that VCs with a broader network increase the likelihood of successful exits for their portfolio companies. Sorensen (2007) shows that companies funded by more experienced VCs are more likely to go public primarily due to their ability to source better investments. Nahata (2008) finds that companies backed by more reputable VCs are more likely to exit successfully, access public markets faster, and have higher asset productivity at IPOs. Krishnan, Masulis, Ivanov, and Singh (2011) find that companies backed by more reputable VCs have better post-issuance performance and better-quality corporate governance. Studies have also shown that entrepreneurs are aware of the benefits associated with VC reputation. According to Hsu (2004), entrepreneurs select offers among competing VC investors not only based on the financial terms, but more often by considering the reputation of the VC investors. That is, Hsu shows that entrepreneurs with multiple financing offers are more willing to turn down the best financial terms from less reputable VCs and accept harsh financial terms from more reputable VCs to be affiliated with the more reputable VCs. This finding is consistent with related theoretical modeling of VC-investor matching (Sorensen), as well as empirical studies of entrepreneurs being financed by banks (Gopalan, Udell, & Yerramilli, 2009)
Furthermore, incumbent lead VCs may also find it attractive to bring in a more reputable VC even with stringent financial terms due to the following reasons. For instance, the new reputable VC, presumably, is capable of adding more value and increases the probability of successful exit and exit valuation. Furthermore, VCs' reputation, their track record of taking portfolio companies to successful exits, and their network with other VCs are important determinants for raising follow-on funds and accessing high-quality deal opportunities (see e.g., Gompers, 1995; Nahata, 2008; Hochberg et al., 2007). By bringing in a more reputable VC, incumbent VCs build up their reputational capital and networks. These benefits may well exceed the cost associated with switching (e.g., accepting a lower valuation) to incumbent lead VCs.
If entrepreneurs and incumbent VCs view the benefits associated with a more reputable lead VC (relative to the existing VCs) as greater than the cost of switching (such as contracting and/or legal costs), then we expect the switching to happen. This type of switching is more likely to happen among entrepreneurs who have upwardly revised the perceived probability of success. Based on this reasoning, we predict ventures have higher probabilities of success if entrepreneurs are successful in obtaining subsequent funding from more reputable outside VCs. Furthermore, in order to get the certification of more reputable VCs and the perceived benefits of such affiliation, entrepreneurs and incumbent VCs are likely to accept less favorable financial terms.
Hypothesis 1: Entrepreneurial firms with the upwardly revised perceived quality are more likely to switch lead VCs.
Hypothesis 2a: New lead VCs are more reputable than incumbent lead VCs for entrepreneurial firms with upwardly revised perceived quality.
Hypothesis 2b: Entrepreneurial firms that switch to more reputable lead VCs are likely to accept less favorable contract terms (e.g., valuation) than those that do not switch.
Switching After a Downward Revision of Venture Quality
Staged financing is a commonly used technique in the venture capital industry. The literature typically regards staged financing as a control or monitoring mechanism (Metrick, 2006), because an entrepreneur has to come back to VCs for funding at several points. Thus, it allows VCs to monitor the firm and to shut it down (i.e., not fund it) if the probabilities of success are poor. On the other hand, VCs that invest in earlier rounds of financing acquire soft information regarding management skills and the quality of the entrepreneurial firm, which are typically not available to outside VCs. Thus, the asymmetric evolution of information between inside VCs and outside VCs allows the existing VCs to hold up the entrepreneurs when the firm needs the next round of financing, because outside VCs will interpret the failure to reinvest as a negative signal regardless of the true quality of the entrepreneurial firm. As a consequence, entrepreneurs face high costs to switch VCs in subsequent rounds of financing.
The implication is that entrepreneurs with downwardly revised perceived quality lack incentives to voluntarily switch VCs. Switching happens only when incumbent VCs are not satisfied with the performance or the potential of the entrepreneurial firm. In practice, for example, it has been noted by Kunze (1990) that if a VC thinks a venture can make it to acquisition (as a second best payoff) rather than an IPO, then the VC will attempt to bring other VCs into the syndicate (to use their money for a lower payoff). A less reputable outside VC may be interested in such deals as they often have a lower cost of capital since they do not charge as high a management fee and carry as the reputable ones. This condition is more likely to be satisfied if the reputable incumbent VC is willing to accept a lower valuation. From the perspective of the incumbent VC, accepting a lower valuation could be still a better proposition than making additional investments that are unlikely to earn the cost of capital. Further, the less reputable outside VC may benefit in other aspects. For instance, the incumbent lead VC will typically not be able to exit completely because that would be too negative a signal to the market. Thus the less reputable VC will benefit from being associated with the more reputable incumbent VC. In addition, the less reputable VCs will be doing the more reputable VC a favor and may call in the chits later.
Based on these reasoning, we conjecture that if entrepreneurs are able to obtain any subsequent funding at all, the new lead VCs are less reputable than the existing lead VCs. Furthermore, the new lead VCs charge a higher discount rate, indicating a lower valuation. In summary, if the information hold-up behavior of VCs exists commonly, we expect the following:
Hypothesis 3a: New lead VCs are less reputable than existing lead VCs for entrepreneurial firms with downwardly revised perceived quality.
Hypothesis 3b: Firms switching to less reputable lead VCs receive less favorable contract terms (e.g., valuation) than firms that do not switch.
Other Alternative Explanations
The earlier-discussed hypotheses are not necessarily mutually exclusive; rather, they reflect various motivations for different groups (for instance, firms of different quality) seeking switching lead VCs. Switching could happen due to other reasons. For instance, VCs often specialize in a stage of investment. Thus, VCs with a focus on early stage ventures might exit when the venture develops. Similarly, the venture's demand for capital at different stages may also have an influence on the switching. Ventures at later stage may demand more capital and thus need a VC with deeper pockets. We address these possible alternative explanations with appropriate controls in our empirical analysis. Further, we discuss other possible alternative explanations after the presentation of the empirical tests later.
Data and Sample
The data on venture investments, including the valuation data, come from the VentureXpert database provided by the Thomson Financial Corporation. We start with all VC investments made in the United States from 1991 to 2002. (2) We exclude entrepreneurial firms whose first-round financing was before 1991. Then, we exclude investments by corporate VCs, VCs affiliated with financial institutions, pension funds, university foundations, and government-sponsored funds. (3) Financings that are labeled as bridge financing are also excluded. Also, note that financings that show up on proximate dates within a few weeks are treated as part of the same round. To be included in the sample, the observation also has to have the amount invested in each round, the size (commitment) of the lead VC fund, and the zip codes of the lead VC investor and the new venture. The lead VC investor is defined as the one that has invested the largest cumulative amount by the time of a specific round. These data filters leave us 21,385 rounds of VC investments in 8,357 entrepreneurial firms with relevant data. Among the 8,357 entrepreneurial firms, 5,323 firms have received more than one round of financing during the sample period. We investigate how these 5,323 entrepreneurial firms make their decisions on whether to switch their lead VCs in subsequent rounds of financing. However, we only observe switching behavior among firms that have successfully obtained multiple rounds of financing, which itself can be endogenous. Without appropriately controlling for this selection effect, our results can be impacted by the potential survivorship bias. Therefore, in the analysis that follows, we apply the Heckman two-stage framework to correct for this survivorship bias. Our final sample consists of 21,385 rounds, which include 3,034 observations of firms that only raised one round and 13,028 follow-on rounds of firms that have obtained multiple rounds.
We define switching as when the lead VC firm in the early rounds of financing is not the lead VC firm in the current or later rounds, where the lead VC firm is the one that had invested the largest cumulative amount of capital by the time of a new round. If the lead VC skips a round or is just temporarily surpassed by another VC in terms of cumulative invested capital, this is not treated as switching.
To test to what extent that earlier-mentioned measure of lead VC captures the ownership stake by VCs, as a robustness check, using the sample of investments with valuation data, we calculate cumulative ownership obtained by each VC. Based on our tabulation, in about 90% of the cases, the lead VC based on cumulative investment amount overlaps with the lead VC based on cumulative ownership stake. Ideally, we would have used this robustness measure to define lead VC. However, when we restrict our sample to observations with valuation data, a lot of observations have to be dropped due to missing values, which may cause serious selection bias. Further, to define switching, we need the information of two continuous rounds of the same company. These restrictions further reduce sample size. After considering trade-offs of these two measures, we decided to report the definition based on cumulative investment amount as the primary results.
As shown in Table 1, during the period from 1991 to 2002, 23% of the follow-on rounds of financing have lead VCs different from those of previous rounds, suggesting that switching lead is not uncommon in the venture capital investments.
Panel A of Table 2 summarizes the characteristics of entrepreneurial firms that switched their lead VC in comparison with those who did not. Specifically, we consider the industry, stage, location, and final exit of the entrepreneurial firms. We show that technology firms, firms at early stages, and firms located in California are more likely to switch. The table also indicates that switchers are more likely to go public.
Panel B of Table 2 compares the characteristics between existing lead VCs of switchers and those of non-switchers. We consider three aspects of VC investors: VC firm reputation, which is measured as the VC firm's aggregate IPO market share following Nahata (2008), VC fund size, and the distance between the lead VC and the entrepreneurial firm. To calculate the VC firm's aggregate IPO market share, following the method proposed by Nahata, we cumulate the dollar market value of all companies taken public by the VC firm since 1987 until a given calendar year and normalize it by the aggregate market value of all VC-backed companies that went public until the same calendar year. VC fund size is measured by the total capital commitment to the VC fund, in millions of 2006 dollars. To measure the geographic distance between the lead VC and the entrepreneurial firm, we obtain the latitude and longitude data for the center of each zip code from the U.S. Census Bureau's Gazetteer and estimate the distance between the centers of the two zip codes by using the following equation:
[d.sub.ij] = 3963 x arcos[sin([lat.sub.i])sin([lat.sub.i]) + cos([lat.sub.i])cos([lat.sub.j])cos([absolute value of [long.sub.i] - [long.sub.j]])] (1)
where latitude (lat) and longitude (long) are measured in radians.
Panel B of Table 2 suggests that the existing lead VCs of switchers are significantly less reputable and smaller than those of non-switchers.
In Panel C of Table 2, we further consider how the characteristics of lead VCs change with switching. We show that among the switchers, about 50.1% of entrepreneurial firms switched to more reputable VCs, 46.6% switched to larger VCs, and 42.0% switched to less distant VCs.
Existing VC Reputation, Entrepreneurial Firm Quality, and the Decision to Switch
In this section, we examine how the decision to switch is impacted by the various characteristics of the existing lead VCs and the entrepreneurial firms.
Empirical Methods. As we mentioned earlier, we only observe switching behavior when firms successfully obtain more than one round of financing, which is endogenous itself. To make sure this selection effect does not introduce systematic bias (survivorship bias) in our results, we apply the Heckman sample selection approach to address this concern. Specifically, we run a first-stage probit regression in which the dependent variable is a dummy that is equal to one if the firm has successfully obtained more than one round of financing during our sample period, and zero otherwise. The independent variables for this analysis include the stage of the firm (seed, early, or later stage), the industry of the firm (computer-related, communication, semiconductor, biotechnology, medical, or non-technology), the location of the firm (California, Massachusetts, or other areas), a syndicate dummy that is equal to one if more than one VC invests in a round, and round size measured as the natural logarithm of round investment. We also include the year dummies in the specification. Rho (the correlation coefficient between error terms of the two equations) is estimated. A significant Rho indicates that the selection effect is important and that the Heckman technique should be used to correct for the potential bias.
In our second-stage regressions, we start with analyzing the determinants of switching with the correction of selection bias. Then we examine the determinants of switching to a more reputable lead VC. In the first specification, the dependent variable is the probability of switching lead VCs. In the second specification, the dependent variable is the probability of switching to a more reputable VC. The independent variables of our key interest are venture quality at the time of financing, reputation of the existing lead VC, and the interaction effect between the two.
Measuring company performance/quality at a specific round of financing is made difficult by the fact that VentureXpert does not disclose accounting information like profit margin, return of assets (ROA), etc. To come up with a proxy for the entrepreneurial firm's perceived quality at the start of a new round, we run a probit regression in which the dependent variable is equal to one if the firm exited as an IPO or a mergers and acquisitions (M&A) by the end of 2007, and zero otherwise. (4) The independent variables are entrepreneurial firms stage, location, industry, the size of the investment, the number of rounds company received previously, and the size of syndicate. These variables are often used as (indirect) indicators of venture quality in studies that use VentureXpert data. For instance, companies at later stage are in general of higher quality. Existing literature also established that companies based in California are more likely to be of higher quality relative to those based on other states. Many studies find that investment size, syndicate size, and the number of rounds received previously have important implications for company performance. Our specification confirms that all these variables are significantly correlated to the probability of successful exits. The predicted probability of a successful exit then is estimated from the earlier-mentioned probit regression and used as a proxy for the firm's perceived quality. We estimate the probability of a successful exit for each firm at the start of each round, assuming new information is available in the new round and thus changes the probability of exit. In particular, the probability of a successful exit is conditional on company stage, round number, round size, and syndicate size. This information changes between rounds and more or less reflects the new information acquired about the company's potential to succeed. Therefore, our measure of perceived quality is not static. Our findings are robust to a wide range of control variables in this regression to proxy entrepreneurial firm quality; alternative specifications are available on request. Our specification correctly predicts exit for 67.2% of the full sample.
To examine the role of VC reputation in the switching decision, we include previous round lead VC's IPO capitalization share as a measure for VC reputation (and our results presented later are robust to other measures of reputation, as shown in earlier drafts of this work; these results are available on request). As shown in Nahata (2008), VCs' IPO capitalization share effectively captures both VC screening and monitoring expertise and has consistent performance implications for entrepreneurial firms. If the majority of the cases of switching are those where entrepreneurial firms try to be affiliated with a more reputable lead VC as they become more confident with the potential of the firm, we expect a negative coefficient on VC IPO capitalization share. If the opposite is true, in other words, VCs abandon investments with low potential of successful exits, we expect a positive coefficient on VC IPO capitalization share.
We further include an interaction term between VC reputation and firms' perceived quality. A negative coefficient on the interaction term suggests that ventures with upwardly revised perceived quality and those associated with less reputable VCs are more likely to switch lead VCs to rematch themselves with appropriate counterparties.
Other control variables include a measure of the geographic distance between the lead VC and the entrepreneurial firm, a dummy variable that is equal to 1 if the stage of the entrepreneurial firm is not consistent with the VC's stated stage specialization, and a measure of VC fund size relative to the next round size. As shown in Cumming and Dai (2010), VCs prefer to invest in geographically proximate ventures to reduce potential frictions (information asymmetry and monitoring cost) associated with distance. They also find proximate ventures outperform distant ones controlling for venture quality. Anecdotal evidence shows that some entrepreneurs even relocate their firms to be close to the VC. Thus, distance could be an alternative reason that switching occurs. VCs typically specialize in investments at a certain stage. As the venture develops, for instance, the venture may have to switch from a lead VC specializing in the seed stage to one that focuses on the expansion stage. Our mismatching in stage preference dummy captures this effect. Further, if the size of the next round is beyond the capability of the existing lead VC, the venture has no choice but to switch. By including the ratio of lead VC fund size to the size of the next round, we control for this possibility. We also include venture location dummies and year dummies.
Empirical Results. We discuss our empirical findings in details in this section. The first specification in Panel B of Table 3 is a probit regression on the probability of switching VCs. We find Rho is statistically significant, indicating a selection bias. Thus Heckman two-stage framework is the appropriate model to control for this selection bias. Both firms' perceived quality and the lead VC IPO capitalization share are significantly and positively associated with the likelihood of switching lead VCs. In terms of the economic significance, the data indicate that a 10% higher perceived probability of successful exit gives rise to a 12% chance of switching lead VCs. The coefficient of the interaction term between the two is significantly negative. The positive coefficient on firms' perceived quality indicates that ventures with upwardly revised perceived quality are more likely to switch lead VCs. The positive coefficient on VC IPO capitalization share suggests that if the venture is currently associated with a reputable lead VC, it is more likely to be abandoned as reputable VCs have higher expectations on the potential of successful exits. The negative coefficient on the interaction term suggests that switching is significantly more likely to occur to ventures with upwardly revised perceived quality but currently associated with less reputable VCs. These findings provide support to HI.
The results from the second specification, which is a probit regression on the probability of switching to a more reputable VC, reveal a significantly positive coefficient on firms' perceived quality, a positive but not significant coefficient on VC reputation and a negative coefficient on the interaction term. In terms of economic significance, the data indicate that a 10% higher perceived probability of successful exit gives rise to a 17% chance of switching to a more reputable lead VC. These findings provide further support to the notion that ventures with upwardly revised perceived quality are more likely to switch from less reputable lead VCs to more reputable ones. Similarly, ventures previously associated with reputable VCs switch to lower-ranked VCs if their exit potential does not meet the expectations of existing VCs. These results support both hypotheses 2a and 3a.
Among the control variables, we find that distance is positively associated with both the probability of switching and the probability of switching to a more reputable VC. In terms of the economic significance, a 10% reduction in distance gives rise to a 1% chance of switching lead VCs and a 1% chance of switching to a more reputable lead VC. Furthermore, we show that the mismatch between the current stage of the venture company and the investment expertise or focus of the previous lead VC is an important determinant of switching. We also find the ratio of lead VC fund size to the next round investment size significantly reduces the probability of switching. This finding indicates that the current lead VC's capacity to provide sufficient funding to the venture is also an important concern in the switching decision.
Note that we considered other variables in the regressions, but their inclusion/ exclusion was immaterial to the results of interest that are presented in the tables. For example, we included variables for the VC's stage preference as reported to the data vendor. We also ran the regressions for subsets of the data for different VCs based on stage preference, location, size, reputation, portfolio composition, among other things (see e.g., Knill, 2009). Overall, inferences drawn from results not explicitly reported were not materially different. These and other specifications are available on request.
In summary, we show that companies with upwardly revised perceived quality are more likely to switch to more reputable VCs than the previous ones, suggesting a strong graduation effect upon entrepreneurial firms switching lead VCs. We also show some evidence that reputable VCs are more likely to abandon ventures with downwardly revised probability of successful exits. VC's capacity of providing sufficient funding in the next round as well the match between the current stage of the venture and the VC's investment expertise also appear to be important concerns in switching.
Switching and Financing Deal Terms
Empirical Methods. In this section, we further analyze the consequence of switching conditional on ventures' perceived quality and the change in VC reputation. Hsu (2004) shows that companies care about the identity of the investor, and when faced with multiple offers, companies are often in favor of more reputable investors and turn down less reputable ones even when they offer the best financial terms. In this section, we analyze whether entrepreneurs switch to obtain better financial terms and whether they turn down better deal terms in order to switch to more reputable VCs. Specifically, we compare the size of investment (millions of 2006 dollars), and the pre-money valuation (millions of 2006 dollars) between switchers and non-switchers.
Our analysis in the previous section suggests that both switching and switching to more reputable VCs are endogenous. In other words, the decision to switch is impacted by VC and company characteristics, which presumably also determine the size of investment and valuation. To control for this self-selection issue, we use the classic Heckman two-stage regression framework. In the first stage, we run the probit regressions analyzing the decision to switch and switch to more reputable VCs, respectively (as shown in panel B of Table 3). Then inverse Mills ratios (IMRs) are estimated off the probit regressions and included in the second-stage regressions as independent variables. The coefficients of the IMRs imply the impact of self-selection (switching) on subsequent round investment size and valuation. The second-stage regression results are reported in Table 4. The first two specifications are the analysis of round size. The next two specifications are about the pre-money valuation. (5) Both dependent variables are in natural logarithm format. The independent variables, which include IMR, that adjust for the self-selection of switching, are: size of the previous round; pre-money valuation of the previous round; company characteristics such as stage, industry, and location; characteristics of the new round lead VC including VC IPO capitalization share, VC fund size; and the syndicate size. Finally, we include Ln (Flow) as a control for the "hotness" of the VC industry.
Empirical Results. As shown in Table 4, we find that switchers raise larger rounds and higher pre-money valuation than non-switchers, controlling for the endogeneity of switching. In terms of the economic significance, switching gives rise to a $1.62 million increase in round size (compared with average round size $9.33 m) and a $1.26 increase in valuation (compared with average valuation $49.9 m). When they switch to more reputable VCs, they raise smaller rounds and accept lower valuation. The second result is also consistent with the finding in Hsu (2004) that many entrepreneurial firms view the benefits of being associated with more reputable VCs greater than the trade-off in concrete financial terms.
Another cost associated with switching is the searching cost. Switchers have to shop around to find a new lead VC and the new lead VC needs to conduct its own due diligence. It potentially takes more time for the company to obtain the follow-on financing in comparison with directly getting the funding from its existing VC. In this section, we examine the significance of the searching cost, which is defined as the duration between two rounds, when switching takes place. Furthermore, we examine whether switching to more reputable VCs incurs higher searching costs. Similar to our analysis of investment size and valuation, we control for the endogeneity of switching and the direction of switching using the Heckman two-stage framework. The results of the second-stage regressions are reported in Table 5.
We find that switching significantly increases the time needed to finish the follow-on financing. Specifically, switching, on average, increases the duration by 1.2 months. On the other hand, when companies switch to more reputable VCs, we find the duration is not necessarily longer (the coefficient is negative but not significant). It might be that more reputable VCs are more efficient at due diligence, which helps speed up the financing process. Alternatively, because companies with upwardly revised perceived quality are more likely to switch to more reputable VCs, they might have more than one competitive offer on the table, which might also speed up the closing of the transaction. On the other hand, for switchers with downwardly revised perceived quality, it may take much longer time for them to secure the new round of financing.
Our analysis in this section shows that depending on the nature of switching, it has a significant impact on follow-on round investment size, valuation, and the duration between rounds. Overall, switching is associated with larger investment size, higher valuation, and longer duration. When firms switch to more reputable VCs, they get a smaller capital infusion and a lower valuation, suggesting there is a trade-off between VC reputation and financial terms.
We note that there are additional terms in VC contracts that might be affected by switching, just as there are terms which might affect the ability to switch or at least increase the transaction costs associated with switching. These specific contractual rights include, but are not limited to, anti-dilution, drag-along, and redemption rights. A VC that does not want the investee to switch and has strong control rights could enforce such rights to make switching more difficult, by forcing the entrepreneur and the new investor(s) to incur legal costs (Cumming & Johan, 2009). But this would not make switching impossible; rather, it would just limit the likelihood that switching would occur. In our analyses, since we do not have information on deal terms in the large sample data from Thomson Financial SDC, we implicitly assume that the costs of switching are equal for all firms. Other publicly available data sets such as that which is used in this article do not include such contractual terms, and the only data sets that do have such terms are limited to small hand-collected samples of typically 200 or so observations (Cumming & Johan). Further research could investigate the interplay between contractual terms and switching with more detailed hand-collected data.
Conclusion and Discussion
We study the dynamics of the relationship between entrepreneurs and their lead VCs from their very first round of financing until the entrepreneurial firm exits. We show that during the period from 1991 to 2002, 23% of the follow-on rounds of financing have lead VCs different from those of previous rounds. This finding raises several questions. For instance, why do entrepreneurial firms want to switch lead VCs; to be associated with more reputable VCs, or simply because existing VCs decided to abandon the investment? As far as we know, this is the first study addressing the earlier-mentioned questions.
Our empirical findings show that entrepreneurial firms with upwardly revised perceived quality are more likely to switch lead VCs. Furthermore, we show that these entrepreneurial firms are more likely to switch to more reputable VCs if they are currently associated with less reputable VCs. In addition, we document that switchers, in general, obtain a larger capital infusion and a higher valuation in follow-on rounds than non-switchers, which provides further support to the "graduation" hypothesis. Moreover, we show that firms are willing to accept a smaller capital infusion and a lower valuation when they switch to more reputable VCs. Finally, it typically takes more time for switchers to obtain the subsequent round of financing. This is particularly true for switchers with downwardly revised perceived quality.
Our findings provide some new insights on the relationship between entrepreneurial firms and VCs. Sorensen (2007) suggests that there exists a positive sorting in the venture capital market, in that higher-quality companies are associated with more reputable VCs. Assuming both VCs and entrepreneurs learn about the quality of the firm as new information is disclosed, this learning process for both sides determines that the matching between VCs and entrepreneurs has to be dynamic. For instance, if reputable VCs acquire negative new information regarding the potential of the company, they will stop investing in the company. Alternatively, while entrepreneurs learn positive new information regarding the quality of the company, they might pursue more reputable VCs. We find strong evidence for the latter and some evidence for the former.
Our findings also have implications for the relative importance of VC reputation and financial terms during the matching between VCs and entrepreneurial firms. Hsu (2004) suggests that entrepreneurs value the reputation of VCs and are often willing to accept less favorable financial terms in order to be associated with the more reputable VCs. We confirm that VC reputation is an important factor that determines the dynamic matching between entrepreneurs and VCs by showing that entrepreneurial firms with upwardly revised perceived quality try to switch to more reputable VCs if they were affiliated with less reputable VCs in early rounds: switching entrepreneurial firms obtain a smaller capital infusion and often accept a lower pre-money valuation.
Our analyses were derived from information asymmetry and agency costs explanations of switching. There could be other explanations for our results, which form the basis for future research. Resource-based theory, transaction costs economics, prospect theory, and behavioral theories may all offer insights into switching. With new data sets on the topic, further empirical tests could be constructed and matched to testing such theories. We hope this new first-ever look at switching in this article inspires further work on the topic.
DOI: 10.1111/j. 1540-6520.2012.00525.x
Appendix: Definitions of Variables Variables Definition Switching Switching l is when the lead VC of the previous round VC reputation is not the lead VC in the subsequent round. Each VC firm's cumulative market capitalization of IPOs from 1987 until a given calendar year and normalized by the overall aggregate market capitalization of VC-backed 1POs until the same calendar year Ln (VC fund Natural logarithm of fund commitment in millions of size) 2006 dollars Ln (fund Natural logarithm of the ratio of lead VC fund size size/round and the size of the next round size) Ln (distance) Natural logarithm of the geographic distance in miles between the VC fund and the venture Syndicate A dummy variable that is set to one if the VC fund forms a syndicate with other VC investors to invest in the financing round, zero otherwise Syndicate The natural logarithm of the number of VCs size participating the same round Firm quality The predicted probability of successful exits (IPO or M&A) Tech A dummy variable that is equal to I if the venture is in information technology, communication, semiconductor, biotechnology, medical sectors, and 0 otherwise CA A dummy variable that is equal to 1 if the venture is located in California, and 0 otherwise MA A dummy variable that is equal to 1 if the venture is located in Massachusetts, and 0 otherwise Expansion A dummy variable that is equal to I if the venture is stage at the expansion stage, and 0 otherwise Later stage A dummy variable that is equal to I if the venture is at the later stage, and 0 otherwise Mismatching A dummy variable that is equal to I if the stage of in stage the venture is not consistent with the stage preference specialization of the lead VC Ln (flow) Natural logarithm of fund flows to the VC industry in the previous year, measured in millions of 2006 dollars Ln (round Natural logarithm of investment amount of a specific size) round, measured in millions of 2006 dollars Ln (pre- Natural logarithm of an entrepreneurial firm's money pre-money valuation in a specific round, measured in valuation) millions of 2006 dollars Duration Natural logarithm of the duration between two rounds, between measured in months rounds
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(1.) As a robustness check, we also consider lead VC as the one that has the largest cumulative ownership stake by the time of a specific round using a more restricted sample with valuation data. Based on our tabulation, lead VCs defined based on the cumulative amount of capital is the same as the lead VCs defined based on the cumulative ownership stake in about 90% of the cases.
(2.) We give a minimum 5-year window from the investment to observe whether the venture has exited as an IPO or an acquisition (by the end of 2007), which requires excluding the financing rounds after 2002.
(3.) We do not include these VC investors to avoid the potential impact of organizational forms of VC investors on the switching decision.
(4.) Our findings are robust to just considering IPO as a successful exit. Also, we checked for M&A exits that possibly disguised write-offs (liquidation sales) based on the exit valuations.
(5.) Due to the constraint of the VentureXpert data, we are not able to use standard normalizer for valuation such as profitability or size. We address this issue in two approaches. First, in our specification, we included the lag value of valuation as a control variable, which presumably should mitigate the problem mentioned earlier. Further, we tried our best to include control variables that may be related to the value drivers, for instance, the stage of the venture, the investment size, the reputation of VC, the syndicate size, which are all found to be related to the value of the entrepreneurial firms in many existing studies.
Douglas Cumming is a professor and the Ontario Research Chair at Schulich School of Business, York University.
Na Dai is an assistant professor at School of Business, SUNY-Albany.
We owe thanks to the seminar participants at ESADE, Cambridge, SUNY at Buffalo, Florida State University, College of William and Mary, York University, SUNY at Albany, and University of Toronto. The project is partly funded by SUNY at Albany FRAP A grant and by a Social Sciences and Humanities Research Council of Canada (SSHRC) grant.
Please send correspondence to: Douglas Cumming, e-mail: firstname.lastname@example.org, to Na Dai, tel.: 518-442-4962; e-mail: email@example.com.
Table 1 Frequency of Switching by Years Total number N of follow-on Frequency of Round year of investments rounds switching (%) 1991 264 65 21 (32) 1992 572 276 63 (23) 1993 593 354 81 (23) 1994 755 478 79 (17) 1995 1,132 621 166 (27) 1996 1,600 966 222 (23) 1997 1,875 1,127 226 (20) 1998 2,004 1,222 289 (24) 1999 3,242 1,776 514 (29) 2000 4,610 2,651 755 (28) 2001 2,659 1,960 326 (17) 2002 2,079 1,532 226 (15) 1991-2002 21,385 13,028 2,968 (23) This table summarizes the total number of investments, the number of follow-on rounds, the frequency of switching, and its percentage out of the number of follow-on rounds using a sample of U.S. venture capitalist investments from 1991 to 2002. Table 2 Summary Statistics Switcher Non-switcher Panel A: entrepreneurial firm characteristics Industry Tech 92.5% *** 89.2% *** Non-tech 7.5% *** 10.8% *** Stage Seed/early 42.4% *** 21.3% *** Expansion 45.5% *** 52.2% *** Later stage 12.1% *** 16.5% *** Location CA 46.1% *** 40.3% *** MA 4.5% ** 5.4% ** Elsewhere 49.4% *** 54.3% *** Exit IPO 9.9% *** 7.6% *** M&A 28.0% 27.6% Successful exits 37.9% *** 35.2% *** N 2,968 10,060 Panel B: existing lead VC characteristics Existing lead VC 1.46 *** (0.68) * 1.83 *** (0.7797) reputation (IPO market share) Existing lead VC fund 147.2 *** (78.5) *** 243.5 *** (101) *** size ($M) Existing lead VC 671.0 *** (105) 742.6 *** (195.7) distance (miles) N 2,968 10,060 Frequency Panel C: characteristics of new lead VCs % switch to more 50.1% reputable VCs % switch to larger VCs 46.6% % switch to closer VCs 39.5% N 2.968 Panel A summarizes the characteristics of entrepreneurial firms that switched lead VCs vs. those that did not. Panel B describes the characteristics of existing lead VCs of firms that switched in comparison to those that did not. Medians are reported in parentheses. Panel C provides frequency of ventures that switch to more reputable, larger, and closer VCs, respectively. The ***, **, and * denote significance in differences across groups at the l, 5, and 10% levels, respectively. CA, California; MA, Massachusetts; M&A, mergers and acquisitions; IPO, initial public offering; VC, venture capitalist. Table 3 Why Do Entrepreneurs Switch VCs? Coefficients p-value Panel A: first-stage probit regression Intercept 0.756 *** 0.000 Seed stage -0.420 *** 0.000 Early stage -0.450 *** 0.000 Computer-related 0.354 *** 0.000 Communication 0.447 *** 0.000 Semiconductor 0.310 *** 0.000 Biotechnology 0.466 *** 0.000 Medical 0.497 *** 0.000 Syndicate 0.606 *** 0.000 Ln (investment) -0.029 *** 0.000 CA 0.199 *** 0.000 MA 0.197 *** 0.003 Year dummies Yes N 21,385 Pseudo [R.sup.2] (%) 8.00 Switch to more Switching reputable VC Panel B: second-stage probit regressions Intercept -1.648 *** (0.000) -1.989 *** (0.000) Firm quality: 1.166 *** (0.000) 1.696 *** (0.000) Predicted probability of IPO/Acq Previous VC 0.158 *** (0.000) 0.042 (0.130) reputation VC reputation * -0.482 *** (0.000) -0.209 *** (0.004) firm quality Previous VC distance 0.092 *** (0.000) 0.095 *** (0.000) Mismatch in stage 0.204 *** (0.000) 0.199 *** (0.000) preference Ln (fund Size/round -2.149 *** (0.000) 0.178 *** (0.000) size) CA 0.081 *** (0.004) 0.043 * (0.085) MA -0.036 (0.387) -0.037 (0.327) Year dummies Yes Yes Rho -0.534 -0.093 p-value of LR test 0.005 0.231 (Rho =0) N 21,385 21,385 Log likelihood -15,501.67 -16,299.46 wald [chi square] 600.79 719.77 Prob > [chi square] 0.000 0.000 This table reports the Heckman two-stage regression analysis of why entrepreneurs switch VCs. Panel A reports our first- stage probit regression in which the dependent variable is whether the ventures had received more than one round during our sample periods. The first regression in Panel B reports the second stage probit regression in which the dependent variable is the switching dummy after a control for the selection problem. The second regression in Panel B reports the second-stage probit regression in which the dependent variable is a dummy which is equal to 1 if the venture switched to a more reputable VC. The *** **, and * denote significance at the 1, 5, and 10% levels, respectively. CA, California; MA, Massachusetts; IPO, initial public offering; VC, venture capitalist. Table 4 Heckman Two-Stage Regression Analysis on the Impact of Switching on Investment Size and Valuation Round size Round size Intercept -1.276 *** (0.000) 1.508 *** (0.000) Switching 7.449 *** (0.000) Switching to more -0.123 (0.225) reputable VC Size of previous round 0.308 *** (0.000) 0.219 *** (0.000) Valuation of previous round Expansion stage 0.030 (0.395) 0.159 *** (0.004) Later stage -0.068 (0.395) 0.238 *** (0.001) Tech -0.104 (0.270) -0.029 (0.778) CA -0.151 ** (0.030) 0.073 (0.156) MA 0.030 (0.765) 0.045 (0.572) Syndicate size 1.024 *** (0.000) 1.153 *** (0.000) VC reputation 0.065 *** (0.000) -0.002 (0.904) Ln (VC fund size) 0.191 *** (0.000) 0.145 *** (0.000) Ln (flow) 0.331 *** (0.000) 0.282 *** (0.000) Inverse Mills ratio -3.998 *** (0.000) 0.047 (0.542) N 7,263 1,090 Wald [chi square] 2,328.78 1,648.99 p > [chi square] 0.000 0.000 Valuation Valuation Intercept -0,798 *** (0.007) -0.733 (0.110) Switching 2.196 *** (0.000) Switching to more -0.258 ** (0.038) reputable VC Size of previous round Valuation of previous 0.631 *** (0.000) 0.568 *** (0.000) round Expansion stage 0.239 *** (0.000) 0.426 *** (0.000) Later stage 0.201 *** (0.000) 0.421 *** (0.000) Tech 0.018 (0.814) 0.119 (0.497) CA -0.018 (0.694) 0.038 (0.595) MA 0.015 (0.820) 0.118 (0.245) Syndicate size 0.333 *** (0.000) 0.476 *** (0.000) VC reputation 0.032 *** (0.001) -0.033 * (0.081) Ln (VC fund size) 0.095 *** (0.000) 0.148 *** (0.000) Ln (flow) 0.071 *** (0.008) 0.086 ** (0.050) Inverse Mills ratio -1.155 *** (0.000) 0.200 ** (0.032) N 2,336 427 Wald [chi square] 2,708.40 863.39 p > [chi square] 0.000 0.000 The first two regressions are the Heckman two-stage regression analysis on the impact of switching and switching to more reputable VCs on round size. The first-stage regression is the first specification in Panel B of Table 3. The next two regressions are the Heckman two-stage regression analysis on the impact of switching and switching to more reputable VCs on valuation. The first-stage regression is the second specification in Panel B of Table 3. Inverse Mills ratios are estimated based on the first- stage regressions and are included in the second-stage regressions as explanatory variables to control for the self-selection of switching or switching to more reputable VCs. The ***, **, and * denote whether the differences are significantly different from zero at the 1, 5, and 10% confidence level, respectively. CA, California: MA, Massachusetts; VC, venture capitalist. Table 5 Heckman Two-Stage Regression Analysis on the Impact of Switching on the Duration Between Rounds Switching Switch to more reputable VCs Intercept 1.889 *** (0.000) 1.657 *** (0.000) Switching 0.801 *** (0.000) Switching to more -0.140 (0.323) reputable VC Size of new round -0.003 (0.790) 0.074 * (0.065) Expansion stage 0.401 *** (0.000) 0.728 *** (0.000) Later stage 0.506 *** (0.000) 0.937 *** (0.000) Tech 0.095 ** (0.029) -0.001 (0.997) CA 0.033 (0.246) 0.073 (0.315) MA 0.069 * (0.091) -0.005 (0.967) Syndicate size -0.335 *** (0.000) -0.359 *** (0.000) VC reputation -0.033 *** (0.000) -0.041 * (0.074) Ln (VC fund size) 0.088 *** (0.000) 0.140 *** (0.000) Ln(flow) -0.033 ** (0.041) -0.081 * (0.059) Inverse Mills ratio -0.393 *** (0.001) 0.063 (0.556) N 7,263 1,090 Wald [chi square] 468.42 189.48 p> > [chi square] 0.000 0.000 We conduct the Heckman two-stage regression analysis on the impact of switching and switching to more reputable VC on the duration between rounds. The first-stage regressions are probit regressions as reported in Table 4. Inverse Mills ratios are estimated based on the first-stage regressions and are included in the second stage regressions as explanatory variables to control for the self selection of switching. The dependent variable of the second-stage regressions is the natural logarithm of the duration between two rounds in months. The *** **, and * denote whether the difference is significantly different from zero at the 1, 5, and 10% confidence level, respectively. CA, California; MA, Massachusetts; VC, venture capitalist.
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|Author:||Cumming, Douglas; Dai, Na|
|Publication:||Entrepreneurship: Theory and Practice|
|Date:||Sep 1, 2013|
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