This study examines battles between short sellers and firms. Firms use a variety of methods to impede short selling, including legal threats, investigations, lawsuits, and various technical actions intended to create a short squeeze. These actions create short sale constraints. Consistent with the hypothesis that short sale constraints allow stocks to be overpriced, firms taking anti-shorting actions have in the subsequent year very low abnormal returns of about −2% per month.

We analyze mutual fund industry selectivity—the performance of a fund’s industry allocation relative to the market. We find that industry selection accounts for a full third of fund performance based on two-digit standard industrial classification (SIC) codes, with the remaining attributable to the performance of individual stocks relative to their own industries. More importantly, we find that industry-selection skill drives persistence in relative performance. Unlike stock-selection ability, industry selectivity is not eroded by increasing fund assets. Our results suggest that accounting for a manager’s ability to pick outperforming industries provides information beyond standard performance measures that can enhance a fund investor’s future performance.

What distribution best characterizes the time series and cross-section of individual stock returns? To answer this question, we estimate the degree of cross-sectional return skewness relative to a benchmark that nests many models considered in the literature. We find that cross-sectional skewness in monthly returns far exceeds what this benchmark model predicts. However, cross-sectional skewness in long-run returns in the data is substantially below what the model predicts. We show that fat-tailed idiosyncratic events appear to be necessary to explain skewness in the data. (JEL, G10, G11, G12, G13, G14).

We reconsider the excess volatility puzzle through the lens of a model in which agents believe they can predict dividend growth when in fact they cannot. Besides excess volatility in the time series, the model explains the value premium, and the explanatory power of the value factor. In support of the model, we show that analysts’ earnings forecasts align with market valuation and that analysts are far more optimistic about growth stocks than they are about value stocks. Using both survey and price data, we show that the same mechanism can explain the excess returns earned by investing in high-interest rate currencies.

Do stocks bear a crash risk premium? We examine the empirical performance of the tail index measure from Kelly and Jiang (2014). We find that the tail index explains the cross-section of the discount rate component of returns, but not the cash-flow component. Moreover, in the time series the tail index is uncorrelated with theoretically motivated measures of aggregate uncertainty and systemic risk. In contrast, the tail index Granger causes and is Granger caused by the level of the term structure, and the slope of the term structure Granger causes tail risk. Received June 22, 2016; editorial decision December 23, 2017 by Editor Raman Uppal.

We show there is a much stronger negative, dynamic relation between changes in economic uncertainty and Treasury yields over weaker economic times since at least 1990. We document this economic-state variation in uncertainty-yield dynamics for weekly and monthly change horizons, for nominal yields and real-yield proxies, for multiple economic-state identification methods, and for different economic uncertainty metrics. We present additional findings that suggest short-term fluctuations in precautionary-savings and consumption-smoothing forces are more impactful on interest rate dynamics during weaker economic times, especially relying on surveys of expected economic growth and inflation. Received February 8, 2019; editorial decision August 24, 2020 by Editor Nikolai Roussanov.

We evaluate the performance of various methods for estimating factor returns in an approximate factor model. Differences across estimators are most pronounced when there is cross-sectional heteroscedasticity or when cross-sectional sample sizes, n, have fewer than 4,000 assets. Estimators incorporating either cross-sectional or time-series heteroscedasticity outperform the other estimators when those types of heteroscedasticity are present. The differences are most pronounced when the cross-sectional sample is small. Received December 2, 2015; editorial decision May 16, 2017 by Editor Jeffrey Pontiff.