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and Wharton. We thank Mete Karakaya for sharing option return data. This paper is based in on Kelly's doctoral thesis and was previously circulated under the title "Risk Premia and the Conditional Tails of Stock Returns." The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

We propose a new measure of time-varying tail risk that is directly estimable from the cross section of returns. We exploit firm-level price crashes every month to identify common fluctuations in tail risk across stocks. Our tail measure is significantly correlated with tail risk measures extracted from S&P 500 index options, but is available for a longer sample since it is calculated from equity data. We show that tail risk has strong predictive power for aggregate market returns: A one standard deviation increase in tail risk forecasts an increase in excess market returns of 4.5% over the following year. Cross-sectionally, stocks with high loadings on past tail risk earn an annual three-factor alpha 5.4% higher than stocks with low tail risk loadings. These findings are consistent with asset pricing theories that relate equity risk premia to rare disasters or other forms of tail risk.

No abstract

and Wharton. We thank Mete Karakaya for sharing option return data. This paper is based in on Kelly's doctoral thesis and was previously circulated under the title "Risk Premia and the Conditional Tails of Stock Returns." The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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We uncover a negative relation between herding behavior and skill in the mutual fund industry.Our new, dynamic measure of fund-level herding captures the tendency of fund managers to follow the trades of the institutional crowd. We …nd that herding funds underperform their antiherding peers by over 2% per year. Di¤erences in skill drive this performance gap: antiherding funds make superior investment decisions even on stocks not heavily traded by institutions, and can anticipate the trades of the crowd; furthermore, the herding-antiherding performance gap is persistent, wider when skill is more valuable, and larger among managers with stronger career concerns.Keywords: Herding, mutual funds, performance, ability, imitation, alpha.J.E.L. codes: G11, G20, G23.Hao Jiang is at Michigan State University. Michela Verardo is at the London School of Economics. Corresponding author: Michela Verardo (m.verardo@lse.ac.uk). We thank two anonymous referees, an anonymous Associate Editor, and the Editor, Kenneth Singleton, for many insightful comments and suggestions. We are grateful for comments from Amil Dasgupta, Dong Lou, Christopher Polk, × ukasz Pomorski, René Stulz, Sheridan Titman, Dimitri Vayanos, Paul Woolley, and Kathy Yuan, as well as from conference participants at the 2011 IDC Summer Conference, 2012 WFA, and 2015 CFA UK Society and seminar participants at the Universities of Aarhus, Amsterdam, Arizona, BI Oslo, Cattolica Milan, Cologne, Drexel, Erasmus Rotterdam, ESSEC, IESE Barcelona, Kent, London School of Economics, Lugano, Mannheim, Michigan State, Nottingham, Reading, and Vienna. Verardo gratefully acknowledges …nancial support from the Paul Woolley Centre at the London School of Economics. The authors have no con ‡icts of interest to disclose.Theories of herding behavior predict that people tend to "follow the crowd"for a variety of reasons, for instance, to appear as talented as others or to learn from others. 1 One important yet underexplored feature of these models is the idea that less skilled individuals may herd on the decisions of their predecessors, while those with superior ability may be more likely to deviate from past actions-to the point of exhibiting antiherding behavior. Despite the rich implications of this intuition, however, there is little empirical evidence on the relationship between skill and the tendency to follow the crowd.In this paper we investigate the link between herding behavior and skill in the context of the mutual fund industry, which is an ideal setting to study the relation between herding and skill for two reasons. First, ample evidence shows that mutual funds and other institutional investors tend to herd in their buying and selling decisions. 2 Second, an extensive empirical literature on mutual fund performance analyzes the returns and investment decisions of mutual fund managers in an attempt to measure unobservable skill. 3 To address the question of whether investors can identify skilled and unskilled mutual fund managers by observing their tendency t...