Knowledge that Transforms

We examine how venture capitalists (VCs) adapt their financing strategies when investing in startups that compete against deep-pocketed incumbents. Employing textual analysis to identify a startup’s potential competitors, we show that when competitors are cash-rich, VCs deploy a financing strategy characterized by less conditionality, as observed through larger, less frequent funding rounds that are less contingent on short-term performance. This strategy requires VCs to rely more on continuous monitoring and liquidation protection, and is restricted to larger funds with specialized experience. Our results highlight that product market competition plays an important role in explaining VC financial contracting choices.

We document widespread use of personal financial advice among retail investors. Individuals seek competent and trusted sources for financial advice among their family and friends. Investors who provide advice to family and friends are positively selected and emphasize the reputational costs of giving risky financial advice. While previous studies have shown that advice shared on social media promotes active trading, we show that personal financial advice encourages investing in funds over single stocks. Our evidence complements the existing literature on financial advice in online social networks by highlighting differences in incentives and outcomes of advice to close personal connections.

We model the feedback loop between the sales of trading advantages (e.g., data or co-location services) and traders’ endogenous participation in financial markets. Whereas a trader’s benefit from purchasing trading advantages increases with aggregate market participation, the benefit from participating decreases with other traders’ purchases of trading advantages that impose negative externalities on counterparties. In equilibrium, sellers of trading advantages (e.g., data providers or securities exchanges) may maximize their profits by prompting inefficiently low market participation and liquidity. We study the consequences of altering the market structure and show that the resulting policy prescriptions contrast sharply with standard models.

In the Australian real estate market, about a third of properties are sold at auction. Properties that fail auctions sell later for a 1.3 percent discount. Consistent with a causal channel, the effect holds with property-level fixed effects and when auction failure is instrumented by adverse weather or seller overvaluation. Prices cluster below round numbers, and the discount fades over time, inconsistent with our effects reflecting unobserved property characteristics. The evidence suggests that there are behavioral factors affecting the valuations of buyers and sellers.

Many initial public offerings (IPOs) are priced at exactly the high-end of the pricing range. Investing in IPOs priced at the high-end leads to first-day returns that are substantially higher than investing in IPOs that are priced just below or above the high-end. We argue that these results are in line with issuing firms settling for positively perceived salient pricing points in negotiations with underwriters.

A theoretical model of the borrower–lender relationship predicts that increased competitive threats lead to a reduction in loan covenant restrictiveness that is stronger for groups of borrowers who face constraints to their ability to raise external financing or compete in the product market. These predictions arise because competition impacts the dynamics of borrower performance so that lenders must trade off the benefit of controlling agency problems against a heightened cost of lost product market opportunities for the borrower, ultimately lowering the optimal use of covenants. We find strong empirical support for these predictions, highlighting an important role of competition for optimal financial contracting rooted in underlying agency problems.

We combine high-frequency stock returns with risk-neutralization to extract the daily common component of tail risks perceived by investors in the cross-section of firms. Our tail risk measure significantly predicts the equity premium and variance risk premium at short horizons. Furthermore, a long–short portfolio built by sorting stocks on their recent exposure to tail risk generates abnormal returns with respect to standard factor models. Incorporating investors’ preferences via risk-neutralization is fundamental to our findings: the predictive power of the physical tail risk is weaker and generally subsumed by its risk-neutral counterpart.