ABSTRACT Short selling efficiency (SSE), measured each month by the slope coefficient of cross-sectionally regressing abnormal short interest on a mispricing score, significantly and negatively predicts stock market returns both in-sample and out-of-sample, suggesting that mispricing gets corrected after short sales are executed on the right stocks. We show conceptually and empirically that SSE has favorable predictive ability over aggregate short interest, as SSE reduces the effect of noises in short interest and better captures the amount of aggregate short selling capital devoted to overpricing. The predictive power is stronger during the periods of recession, high volatility, and low public information. In addition, low SSE precedes the months when the CAPM performs well and signals an efficient market. Overall, our evidence highlights the importance of the disposition of short sales in stock markets.

We examine net arbitrage trading (NAT) measured by the difference between quarterly abnormal hedge fund holdings and abnormal short interest. NAT strongly predicts stock returns in the cross-section. Across ten well-known stock anomalies, abnormal returns are realized only among stocks experiencing large NAT. Exploiting Regulation SHO, which facilitated short selling for a random group of stocks, we present causal evidence that NAT has stronger return predictability among stocks facing greater limits to arbitrage. We also find large returns for anomalies that arbitrageurs chose to exploit despite capital constraints during the 2007–09 financial crisis. We confirm our findings using daily data. Received September 1, 2016; editorial decision May 28, 2018 by Editor Andrew Karolyi. 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.

Using novel data from a crowdsourcing platform for ranking stocks, we investigate how investors form expectations about stock returns over the next week. We find that investors extrapolate from stocks’ recent past returns, with more weight on more recent returns, especially when recent returns are negative, salient, or from a dispersed cross-section. Such extrapolative beliefs are stronger among nonprofessionals and large stocks. Moreover, consensus rankings negatively predict returns over the next week, more so among stocks with low institutional ownership and a high degree of extrapolation. A trading strategy that sorts stocks on investor beliefs generates an economically significant profit.