Institutional Affiliation: University of Southern California
|Venture Capital Contracts|
with , : w26115
We estimate the impact of venture capital (VC) contract terms on startup outcomes and the split of value between the entrepreneur and investor, accounting for endogenous selection via a novel dynamic search and matching model. The estimation uses a new, large data set of first financing rounds of startup companies. Consistent with efficient contracting theories, there is an optimal equity split between agents, which maximizes the probability of success. However, VCs use their bargaining power to receive more investor-friendly terms compared to the contract that maximizes startup values. Better VCs still benefit the startup and the entrepreneur, due to their positive value creation. Counterfactuals show that reducing search frictions shifts the bargaining power to VCs and benefits them at t...
|Risk-Adjusting the Returns to Venture Capital|
with : w19347
Performance evaluation of venture-capital (VC) payoffs is challenging because payoffs are infrequent, skewed, realized over endogenously varying time horizons, and cross- sectionally dependent. We show that standard stochastic discount factor (SDF) methods can be adapted to handle these issues. Our approach generalizes the Public Market Equivalent (PME) measure commonly used in the private-equity literature. We find that the abnormal returns from both VC funds and VC start-up investments are robust to relaxing the strong distributional assumptions and implicit SDF restrictions from the prior literature: VC start-up investments earn substantial positive abnormal returns, and VC fund abnormal returns are close to zero. We further show that the systematic component of start-up company and VC ...
Published: “Risk-Adjusting the Returns to Venture Capital” (with Arthur Korteweg), Journal of Finance, Volume 71, Issue 3 June 2016 Pages 1437–1470 citation courtesy of
|Estimating Loan-to-Value and Foreclosure Behavior|
with : w17882
We develop and estimate a unified model of house prices, loan-to-value ratios (LTVs), and trade and foreclosure behavior. House prices are only observed for traded properties, and trades are endogenous, creating sample-selection problems for traditional estimators. We develop a Bayesian filtering procedure to recover the price path for each individual property and produce selection-corrected estimates of historical LTVs and foreclosure behavior, both showing large unprecedented changes since 2007. Our model reduces the index revision problem by nearly half, and has applications in economics and finance (e.g., pricing mortgage-backed securities).