Nearly all the research detailed below relies on the generous access to the VentureOne database provided by VentureSource and Correlation Ventures.
Working Papers
A New Model of Venture Capital Risk and Return [Job market paper]
I formulate a model and estimator of venture capital (VC) returns motivated by the entrepreneurial firm life-cycle and the extreme return outcomes of typical venture capital investments. The model incor- porates tail events and the estimator corrects for sample selection bias and endogenous investment holding periods. I find that an asymmetric three-state mixture distribution is a better characterization of returns than the standard single-state model. Mixture states mimic typical VC outcomes: “winners,” “break- even” and “failures.” Imposing normality on venture capital investment returns understates downside risk and kurtosis. In contrast to earlier studies, the mixture model reveals a leptokurtic, negatively-skewed returns distribution. Two new implications follow from the results. First, volatility as an estimate of risk underestimates the frequency and magnitude of large, negative VC returns. Investors in venture capital may need to incorporate additional moments or semivariance into their allocation decisions. Second, a microcap index benchmark previously shown to mimic the means and CAPM alphas of VC returns lacks the downside risk or fat tails of the VC mixture distribution. Thus, VC investments offer some risk and return features unavailable in publicly traded equities.
Spinoffs in the Venture Capital Industry (distributable paper coming March 2010)
I study new firm formation in the venture capital industry through employee spinoffs. I show that the creation of such firms is more likely to occur when a parent firm’s growth has slowed. This pattern illustrates the risks of losing top employees for large, older venture capital funds. Spinoffs and de novo firms differ in important ways that support the hypothesis that networks and experience explain cross-sectional differences in VC firm performance. De novo firm start with smaller capital stocks and exit the market sooner than spinoff firms. However, the two types of new firms have the same basic investment performance.
Estimating Statistical and Preference-based Racial Discrimination in the US Apartment Rental Market (with Bryan Tomlin and Choon Wang)
We test for statistical discrimination in the apartment rental market by responding via email to landlords postings their vacancies on craigslist.org. in 35 U.S. cities. By manipulating the level of positive or negative information included in over 14,000 fictitious inquiries with randomly assigned black or white sounding applicant names we are able to evaluate the effect of information on differential treatment by race. We find evidence of preferential treatment of whites when no additional information is provided in an inquiry, both in terms of the likelihood of receiving a response and the likelihood of receiving a positive response. The gap in differential treatment is unchanged when positive information is added, though the gap diminishes in the presence of negative information for males and increases in the presence of negative information for females. These results suggest that landlords have a race-based prior expectation of applicant quality that is (dis-)confirmed by the presence of negative information, and curiously uncorrelated with positive information. Our findings challenge the theory of statistical discrimination and suggest that preference based discrimination is likely to play a strong role in this market.
Works in Progress
These papers are in various stages of development and ordered by the likelihood of completion with an interesting result.
Encouraging Cooperation through Network Punishment: The Case of VC Follow-on Investments
Venture capital syndicates rely on a set of both formal and informal agreements for effective capital allocation and monitoring decisions. One important informal rule determines whether syndicate members should continue to invest in subsequent financing rounds. I study if and how venture capitalists punish deviations from this rule. Preliminary results suggest that investors who fail to follow past investments exhibit below-average growth in deals post-deviation. I hypothesize that punishment through restricted deal flow is used by both syndicate members and their network connections. Discovery of such relationships would show how information about deviations spreads through social networks in order to enforce informal rules.
Social Capital in Venture Capital Syndicates
I use person-level data to test how social networks in early-stage venture capital syndicates impact performance. The structure of the database allows me to control for important VC firm fixed effects and thus reduce endogeneity problems. The model posits that the internal network of the syndicate and its “external reach” are directly correlated with important outcomes like IPOs and bankruptcies.
Disentangling Performance Persistence in VC Fund Returns with Partner-level Performance Measures