By flexibly weighting the information contained in past realized returns, we construct a momentum strategy that outperforms and subsumes the performance of traditional stock momentum. The strategy performs well in crises and continues to work in the most recent decades. We show that the way past returns are weighted is in line with the strategy exploiting an underreaction to the information contained in realized returns, but also investigate alternative behavioral and risk-based explanations. We find that the response to earnings announcements, market-wide jumps and large individual returns realized in the formation period are most informative about future stock returns.

We study the dynamics of the short-duration premium around pre-scheduled news announcements. For macroeconomic news, long-duration stocks earn higher returns than short-duration stocks. On the flip side, returns for short-duration stocks are significantly elevated on earnings announcement days. Focusing on earnings announcement as a laboratory for the pricing of firm-specific news, we differentiate between four competing explanations. We find strong support for the idea that investors are overly optimistic about long-term cash-flows, leading to an overvaluation of long-duration stocks. This overvaluation is in part corrected at earnings announcements, explaining the lower return response of long- compared to short-duration stocks. We also present empirical evidence against the three competing explanations, and show that the effect is not present in the corporate bond market.

Abstract Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing. 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.