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.

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. Investors must account for estimation risk, and incorporate an ensemble of important features, including time‐varying volatility, and time‐varying expected returns driven by payout yield measures that include share repurchase and issuance. Prior research documents a lack of benefits to return predictability, and our results suggest that this is largely due to omitting time‐varying volatility and estimation risk. We also document the sequential process of investors learning about parameters, state variables, and models as new data arrive.

We adapt stochastic discount factor (SDF) valuation methods for venture capital (VC) performance evaluation. Our approach generalizes the popular Public Market Equivalent (PME) method and allows statistical inference in the presence of cross‐sectionally dependent, skewed VC payoffs. We relax SDF restrictions implicit in the PME so that the SDF can accurately reflect risk‐free rates and returns of public equity markets during the sample period. This generalized PME yields substantially different abnormal performance estimates for VC funds and start‐up investments, especially in times of strongly rising public equity markets and for investments with betas far from one.