I estimate the market's valuation of the net benefits to leverage using panel data from 1994 to 2004, identified from market values and betas of a company's debt and equity. The median firm captures net benefits of up to 5.5% of firm value. Small and profitable firms have high optimal leverage ratios, as predicted by theory, but in contrast to existing empirical evidence. Companies are on average slightly underlevered relative to the optimal leverage ratio at refinancing. This result is mainly due to zero leverage firms. I also look at implications for financial policy. Copyright (c) 2010 the American Finance Association.

This paper uses a randomized field experiment to identify which start-up characteristics are most important to investors in early-stage firms. The experiment randomizes investors' information sets of fund-raising start-ups. The average investor responds strongly to information about the founding team, but not to firm traction or existing lead investors. We provide evidence that the team is not merely a signal of quality, and that investing based on team information is a rational strategy. Together, our results indicate that information about human assets is causally important for the funding of early-stage firms and hence for entrepreneurial success.

This paper finds statistically and economically significant out-of-sample portfolio benefits for an investor who uses models of return predictability when forming optimal portfolios. The key is that investors must incorporate an ensemble of important features into their optimal portfolio problem including time-varying volatility and time-varying expected returns driven by improved predictors such as measures of yield that include shares repurchase and issuance in addition to cash payouts. In addition, investors need to account for estimation risk when forming optimal portfolios. Prior research document a lack of benefits to return predictability, and our results suggest this was largely due to omitting time-varying volatility and estimation risk. We also study the learning problem of investors, documenting the sequential process of learning about parameters, state variables, and models as new data arrives.

This appendix presents a number of supporting results, extensions, and robustness checks to supplement the analysis in the main paper. Section A provides a detailed derivation of the results for the log-normal model referred to in the introduction of the main paper. Section B discusses the relationship between our method and tests with linearized versions of SDFs that are common in the literature. Section C conducts Monte Carlo experiments to study the size of the tests we use to evaluate VC fund and start-up company payoffs. Section D looks at how the estimation of SDF parameters affects the size and power of tests. Section E provides additional detail on the data sets. Section F presents robustness checks where we consider further subsample results, including a sample of liquidated funds, and we explore the sensitivity of our findings to the assumptions about the unobserved acquisition returns of start-up company investments. In Section G, we compare our approach to earlier methods in the literature that are based on a log-normal model of returns. We show results for expected returns for the special case of jointly log-normal market returns and asset returns R i,t+1 for the general SDF specification M t+1 = exp(a − br m,t+1).

Abstract:Valuations of entrepreneurial companies are only observed occasionally, albeit more frequently for well-performing companies. Consequently, estimators of risk and return must correct for sample selection to obtain consistent estimates. We develop a general model of dynamic sample selection and estimate it using data from venture capital investments in entrepreneurial companies. Our selection correction leads to markedly lower intercepts and higher estimates of risks compared to previous studies. † Stanford University, GSB (korteweg@stanford.edu) and Columbia Business School and NBER (ms3814@columbia.edu). We are particularly grateful for our discussions with John Cochrane that helped shape our understanding of the underlying problems, and we thank