We introduce aggregate transmission shocks to an epidemic model and link firm valuations to infections via an asset pricing framework with vaccines. Infections lower earnings growth but firms can mitigate damages. We estimate a large reproduction number $\mathcal R_0$ and transmission volatility for COVID-19. Using these estimates, we quantify the bias of deterministic approximations based on $\mathcal R_0$. Our model generates predictions consistent with the data: unexpected infection resurgence, nonmonotonic mitigation policies, and higher price-to-earnings ratios during a pandemic. Valuations would be significantly lower absent mitigation and a high vaccine arrival rate.

Corporate donations to charities affiliated with the board’s independent directors (affiliated donations) are large and mostly undetected due to lack of formal disclosure. Affiliated donations may impair independent directors’ monitoring incentives. CEO compensation is on average 9.4% higher at firms making affiliated donations than at other firms, and it is much higher when the compensation committee chair or a large fraction of compensation committee members are involved. We find suggestive evidence that CEOs are unlikely to be replaced for poor performance when firms donate to charities affiliated with a large fraction of the board or when they donate large amounts.

We use proprietary brokerage data to study trading patterns within a well-known financial market bubble: the Chinese warrants bubble. Persistently successful investors trade very actively and exhibit characteristics of de facto market makers. Unskilled investors unprofitably trend-chase and increase holdings in out-of-the-money warrants near expiration, whereas sophisticated investors do the reverse. We find that regulators did not properly forecast trading frenzies, as the prespecified price limits often exclude the fundamental values of warrants.

We analyze a model in which traders have different trading opportunities and learn information from prices. The difference in trading opportunities implies that different traders may have different trading motives when trading in the same market—some trade for speculation and others for hedging—and thus they may respond to the same information in opposite directions. This implies that adding more informed traders may reduce price informativeness and therefore provides a source for learning complementarities leading to multiple equilibria and price jumps. Our model is relevant to various realistic settings and helps to understand a variety of modern financial markets.

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

We argue that cross-ownership increases the amount of private information in stock prices, enhancing the ability of stock prices to provide feedback to managers. Consistent with this argument, we find greater cross-ownership heightens a firm’s investment- q sensitivity. This effect is stronger for firms with a lower propensity for voluntary disclosure and for firms whose managers hold less private information. Furthermore, we find that cross-ownership is negatively associated with the sensitivity of a firm’s investment to its peers’ stock prices. Additionally, cross-ownership has a stronger impact on the investment- q sensitivity when measured among investors who trade more actively in the firm’s shares. By using financial institution mergers as an identification strategy, we strengthen the causal inference. Overall, our results suggest that cross-ownership helps increase revelatory price efficiency (RPE), potentially leading to more efficient corporate decisions.

We introduce and test a firm-level innovation-efficiency measure new to the finance literature. The measure, termed the research quotient (RQ), defined as the firm-specific output elasticity of research and development (R&D), was first developed in the management literature. RQ has a low correlation with existing innovation input, output, and efficiency measures. We test RQ in a number of innovation tests common to the finance literature and find that RQ is robust in all tests of firm value, even after controlling for previous innovation measures. The results suggest that RQ may serve as a relevant complementary measure of a company’s innovation.

Recently, exchanges have been directly selling market data. We analyze how this practice affects price discovery, the cost of capital, return volatility, market liquidity, information production, and trader welfare. We show that selling price data increases the cost of capital and volatility, worsens market efficiency and liquidity, and discourages the production of fundamental information relative to a world in which all traders observe prices. Generally, allowing exchanges to sell price information benefits exchanges and harms liquidity traders. Overall, our results suggest that regulations on selling market data can play an important role in improving market quality and trader welfare.

Recent scholarship has suggested that artificial intelligence (AI) technology and autocratic regimes may be mutually reinforcing. We test for a mutually reinforcing relationship in the context of facial-recognition AI in China. To do so, we gather comprehensive data on AI firms and government procurement contracts, as well as on social unrest across China since the early 2010s. We first show that autocrats benefit from AI: local unrest leads to greater government procurement of facial-recognition AI as a new technology of political control, and increased AI procurement indeed suppresses subsequent unrest. We show that AI innovation benefits from autocrats’ suppression of unrest: the contracted AI firms innovate more both for the government and commercial markets and are more likely to export their products; noncontracted AI firms do not experience detectable negative spillovers. Taken together, these results suggest the possibility of sustained AI innovation under the Chinese regime: AI innovation entrenches the regime, and the regime’s investment in AI for political control stimulates further frontier innovation.