[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. The methodology is generally applicable to estimating risk and return in illiquid markets with endogenous trading.]

This paper shows the importance of correcting for sample selection when investing in illiquid assets that trade endogenously. Using a sample of 32,928 paintings that sold repeatedly between 1960 and 2013, we find an asymmetric V-shaped relation between sale probabilities and returns. Adjusting for the resulting selection bias reduces average annual index returns from 8.7% to 6.3%, lowers Sharpe ratios from 0.27 to 0.11, and materially impacts portfolio allocations. Investing in a broad portfolio of paintings is not attractive, but targeting specific styles or top-selling artists may add value. The methodology naturally extends to other asset classes.

VC: An American History, by Tom Nicholas, offers a compelling chronicle of the development of professional venture capital (VC) in the United States—from VC-like fore-bearers as diverse as eighteenth-century cotton manufacturing and nineteenth-century whaling up to the state of the modern VC market at the turn of the millennium. The book emphasizes America’s enduring advantage in VC as a consequence of these early developments and as a practical governance solution for investing in the long-tailed returns of risky new ventures. In this essay we discuss similar historical precedent and governance arrangements in the spice-trading voyages of the sixteenth- and seventeenth-century Dutch Republic, calling into question the uniqueness of early American VC ancestors. Moreover, far from being a distinguishing feature of early ventures, long-tailed returns exist even in public equities, suggesting that the VC governance structure is about more than the distribution of returns. We conclude that the reasons for American dominance of contemporary VC remain unclear. Picking up where the book leaves off, we summarize facts and trends in twenty-first-century VC. (JEL G24, M13, N20, O16, O31)

ABSTRACT 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.

Private equity (PE) performance is persistent, with PE firms consistently producing high (or low) net-of-fees returns. We use a new variance decomposition model to isolate three components of persistence. We find high long-term persistence: the spread in expected net-of-fee future returns between top and bottom quartile PE firms is 7–8 percentage points annually. This spread is estimated controlling for spurious persistence, which arises mechanically from the overlap of contemporaneous funds. Performance is noisy, however, making it difficult for investors to identify the PE funds with top quartile expected future performance and leaving little investable persistence.

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. The methodology is generally applicable to estimating risk and return in illiquid markets with endogenous trading.

Abstract This paper introduces a new metric, α, to benchmark the performance of individual private equity funds. Our metric is substantially less sensitive to noise in fund cash flows compared to the popular public market equivalent (PME) and its generalization (GPME), while having the same aggregate pricing implications as GPME. For a large data set of fund cash flows, α estimates have much lower standard deviation across funds than does (G)PME. For buyout funds, PME and α are close, but deviate in certain subsamples. Using α increases power in regressions involving fund performance and improves performance predictability of future funds.

ABSTRACT 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.

ABSTRACT 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.