원문정보
초록
영어
A recent paper by Conrad, Kapadia, and Xing (2014) shows that stocks with high probability for extreme positive payoffs (jackpots) earn low returns subsequently. We find that stocks with high probability for extreme negative returns (crashes) earn abnormally low average returns, and that the cross-sectional return predictability of crash probability subsumes completely the jackpot effect. The most distinctive features of the crash effect we find are that the underperformance of stocks with high crash probability is clear regardless of the stocks’ institutional ownership, and it is not associated with variations in investor sentiment. We also find that institutional demand for stocks with high crash probability increases until their prices arrive at the peak of overvaluation. Our evidence contradicts the presumption that sophisticated investors are always willing to trade against mispricing, and suggests that the crash effect we find may arise partially from rational speculative bubbles, not entirely from sentiment-driven overpricing.
목차
1. Introduction
2. A Generalized Logit Model of Price Crashes
2.1 Data
2.2 Definition of price crashes
2.3 Prediction of price crashes with a generalized logit model
3. Probability of Price Crashes and the Cross-Section of Stock Returns
3.1 Portfolios sorted on the predicted probability of crashes
3.2 Crash probability and firm characteristics
3.3 Firm-level cross-sectional regressions
4. Probability of Price Crashes and Sources of Overpricing
4.1 Crash probability effects and limits-to-arbitrage
4.2 Crash probability effects across different sub-periods
4.3 Crash probability effects and changes in institutional holdings
5. Probability of Price Crashes and Other Cross-Sectional Anomalies
5.1 Crash probability effects and jackpot effects
5.2 Crash probability effects and distress puzzle
5.3 Crash probability effects and various cross-sectional anomalies
6. Conclusion
Appendix. Definitions of Variables