We analyze how corporate direct investments in fintech startups affect startup performance and that of investing firms. Corporate investment in fintech startups is associated with a greater likelihood of successful exit, more and higher-quality innovation, and a greater inflow of high-quality inventors. A stacked difference-in-differences analysis shows that direct investments enhance the operating performance and equity-market valuation of corporate investors in the financial services sector, but not those in the nonfinancial sector. We establish two channels that drive fintech startups’ performance improvements: strategic alliance formation between investors and startups, and enhanced startup monitoring by corporate investors.

Product market advertising, while containing little new information, triggers spikes in investor attention. Using weekly advertising data, we find that sell‐side analysts issue optimistic earnings forecasts in response to heavier advertising in the prior week. This effect is not driven by confounding earnings or product news. It is more pronounced for experienced analysts and analysts affiliated with brokerages that rely solely on trading revenues. The optimistic forecast bias intensifies the impact of advertising on investor trades of the underlying stock during the following week, especially on retail buying. Overall, analysts appear to issue optimistic forecasts to exploit retail investor attention spikes induced by advertising.

We investigate whether meeting Wall Street’s expectations affects rank-and-file employees’ satisfaction. Controlling for firms’ underlying financial performance, we find that those currently working for firms that meet or marginally beat analysts’ forecasts experience increased job satisfaction. This positive effect is concentrated among employees who are less transient, receive more nonexecutive stock options, or are more unionized. Furthermore, the positive effect exists only when employees do not incur higher costs associated with reaching the threshold because they overwork, suffer from labor law violations, or experience layoffs. Lastly, more senior or highly skilled employees respond more strongly when their employer meets Wall Street’s expectations. These results suggest that the effect of meeting earnings targets on employee satisfaction is significant when employees’ incentives align more with those of their employer or when employees are not unduly pressured. Data Availability: Data are commercially available.

This paper presents a framework to study how investors’ social concerns affect technology choices. Consequentialist preferences (disutility from aggregate harm) influence outcomes only if investors coordinate, unless internalized harm is independent of an investor’s mass. Non-consequentialist preferences (disutility from stockholdings) affect outcomes regardless of coordination. Both preferences have stronger impact when risk-sharing consequences of technology supply are small (e.g., highly correlated returns), and their effects cannot be inferred from cost-of-capital differences. When harm is stochastic, polluting firms may appear less risky to social investors. Depending on type and strength of social preferences, this can support or hinder the green transition.

This paper studies economies where firms acquire capital in primary markets and then, after idiosyncratic productivity shocks, retrade it in secondary markets that incorporate bilateral trade with search, bargaining, and liquidity frictions. We distinguish between full or partial sales (one firm gets all or some of the other’s capital) and document several long- and short-run empirical patterns between these variables and the cost of liquidity, as measured by inflation. Quantitatively, the model can match these patterns plus the standard business cycle facts. We also investigate the impact of search frictions, monetary and fiscal policy, persistence in shocks, and returns to scale.

We present a novel methodology for the out-of-sample forecast of the equity premium. Our predictive slope coefficient is a conservative constant that has a lower bias than the zero slope employed by the historical average, but has the same variance. We demonstrate that, theoretically and empirically, our method dominates the historical average in forecast performance. Our methodology establishes a simple yet powerful paradigm for exploiting the real-time equity premium predictability derived from a predictor. Applications of our method reveal that many predictors can forecast the equity premium, and that parameter estimates in previous studies add value to out-of-sample forecasts.

There is causal evidence that mortgage credit expansions increase house prices. Does an expansion of margin lending increase stock prices? Because unconstrained arbitrageurs are more important for pricing stocks than homes, the impact is not obvious. Tests are limited because sizable shocks to margin lending are rare. We examine a major Chinese margin-lending expansion between 2010 and 2015. Institutional holding, regression discontinuity, and event study evidence—exploiting the rollout of margin lending across stocks—shows that arbitrageurs anticipated and bought in advance of a significant causal effect of credit. We develop a model to rationalize our findings. Our estimates suggest that margin debt contributes to stock market fluctuations.

This research proposes a novel firm-based model for pricing cyber insurance. Our model considers two types of cyber risk: virus attacks and data breaches. Virus attacks deliver adverse shocks to the firm’s productivity, while data breaches cause premium customer departures that worsen the prospect of the firm’s product demand. We derive the endogenous structural form of cyber losses in firms and utilize it to solve the formula for cyber insurance premiums. Our quantitative results show that the consensus prediction about a strictly positive premium-risk nexus is no longer valid. Asymmetries in the sub-premium’s sensitivity to cyber risks from different sources and the premium customer loss rates jointly shape the complexity of the relation between cyber insurance premiums and cyber risks. Improvements in the product demand conditions enhance firms’ incentives to hedge cyber losses and push premiums higher. Lastly, we discuss the influence of product price competition on premiums.

Previous research has revealed that return spreads between stocks with high and low characteristics-based factor beta remain insignificant. This study investigates the time variation in the pricing of various characteristics-based factors, uncovering a notable two-regime pattern: high-beta portfolios yield higher returns than low-beta portfolios after high-sentiment periods, while the opposite occurs after low-sentiment periods. Remarkably, this two-regime pattern is completely reversed for macro factors. Mutual fund and hedge fund returns corroborate these findings. Our results suggest that exposure to characteristics-based factors likely represents mispricing levels, particularly during high-sentiment periods, whereas exposure to macro factors likely represents risk, particularly during low-sentiment periods.