ABSTRACT A mutual fund manager is more likely to buy (or sell) a particular stock in any quarter if other managers in the same city are buying (or selling) that same stock. This pattern shows up even when the fund manager and the stock in question are located far apart, so it is distinct from anything having to do with local preference. The evidence can be interpreted in terms of an epidemic model in which investors spread information about stocks to one another by word of mouth.

ABSTRACT We propose that stock‐market participation is influenced by social interaction. In our model, any given “social” investor finds the market more attractive when more of his peers participate. We test this theory using data from the Health and Retirement Study, and find that social households—those who interact with their neighbors, or attend church—are substantially more likely to invest in the market than non‐social households, controlling for wealth, race, education, and risk tolerance. Moreover, consistent with a peer‐effects story, the impact of sociability is stronger in states where stock‐market participation rates are higher.

This paper studies how market closures affect investors' trading policies and the resulting return‐generating process. It shows that closures generate rich patterns of time variation in trading and returns, including those consistent with empirical findings: (1) U‐shaped patterns in the mean and volatility of returns over trading periods, (2) higher trading activity around the close and open, (3) more volatile open‐to‐open returns than close‐to‐close returns, (4) higher returns over trading periods than over nontrading periods, (5) more volatile returns over trading periods than over nontrading periods. It also shows that closures can make prices more informative about future payoffs.

We develop a model to explore the asset pricing implications of firms being buyers of last resort for their own stocks. Those with more ability to repurchase shares when prices drop far below fundamental value (i.e., less financially constrained firms) should have lower short-horizon return variances (controlling for fundamental variance) than other firms. Using standard proxies for financing constraints such as past repurchases and firm age, we find strong support for this predicted relation. We also find that this relation is stronger in the U.S. after 1982 when regulatory reforms lowered the legal cost of conducting repurchases as well as in countries where share repurchases are legally easier to execute.

ABSTRACT We model the relationship between asset float (tradeable shares) and speculative bubbles. Investors with heterogeneous beliefs and short‐sales constraints trade a stock with limited float because of insider lockups. A bubble arises as price overweighs optimists' beliefs and investors anticipate the option to resell to those with even higher valuations. The bubble's size depends on float as investors anticipate an increase in float with lockup expirations and speculate over the degree of insider selling. Consistent with the internet experience, the bubble, turnover, and volatility decrease with float and prices drop on the lockup expiration date.

We model the welfare consequences of mandates that restrict investors to hold firms with net-zero carbon emissions. To qualify for these mandates, value-maximizing firms have to accumulate decarbonization capital. Qualification lowers a firm’s required return by its decarbonization investments divided by Tobin’s q, that is, the greenium or the dividend yield shareholders forgo to address the global-warming externality. The welfare-maximizing mandate approximates the first-best solution, yielding welfare gains compared to laissez-faire by mitigating the weather disaster risks resulting from carbon emissions. Our model generates optimal transition paths for decarbonization that we use to evaluate proposed net-zero targets. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online

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.

ABSTRACT We study the asset pricing implications of learning in an environment in which the true model of the world is a multivariate one, but agents update only over the class of simple univariate models. Thus, if a particular simple model does a poor job of forecasting over a period of time, it is discarded in favor of an alternative simple model. The theory yields a number of distinctive predictions for stock returns, generating forecastable variation in the magnitude of the value‐glamour return differential, in volatility, and in the skewness of returns. We validate several of these predictions empirically.

Emissions abatement alone cannot address the consequences of global warming for weather disasters. We model how society adapts to manage disaster risks to capital stock. Optimal adaptation—a mix of firm‐level efforts and public spending—varies as society learns about the adverse consequences of global warming for disaster arrivals. Taxes on capital are needed alongside those on carbon to achieve the first best. We apply our model to country‐level control of flooding from tropical cyclones. Learning rationalizes empirical findings, including the responses of Tobin's q , equity risk premium, and risk‐free rate to disaster arrivals. Adaptation is more valuable under learning than a counterfactual no‐learning environment. Learning alters social‐cost‐of‐carbon projections due to the interaction of uncertainty resolution and endogenous adaptive response.

We investigate the effect of scale on performance in the active money management industry. We first document that fund returns, both before and after fees and expenses, decline with lagged fund size, even after accounting for various performance benchmarks. We then explore a number of potential explanations for this relationship. This association is most pronounced among funds that have to invest in small and illiquid stocks, suggesting that these adverse scale effects are related to liquidity. Controlling for its size, a fund's return does not deteriorate with the size of the family that it belongs to, indicating that scale need not be bad for performance depending on how the fund is organized. Finally, using data on whether funds are solo-managed or team-managed and the composition of fund investments, we explore the idea that scale erodes fund performance because of the interaction of liquidity and organizational diseconomies.