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.

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 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.

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 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, venture capitalists (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 the expense of entrepreneurs. The results show that the selection of agents into deals is a first-order factor to take into account in studies of contracting.

We use new hand-collected data from corporate filings to study the drivers of corporate capital structure adjustment. Classifying firms by their adjustment frequencies, we reveal previously unknown patterns in their reasons for financing and the financial instruments used. Some are consistent with existing theory, whereas others are understudied. Many leverage changes are outside of the firm’s control (e.g., executive option exercise) or incur negligible adjustment costs (e.g., credit-line usage). This implies a lower frequency of proactive leverage adjustments than indicated by prior research using accounting data, suggesting that costs of adjustment are higher, or the benefits lower, than previously thought.