Stock Option Predictability for the Cross-Section
46 Pages Posted: 8 Mar 2021 Last revised: 12 Apr 2021
Date Written: March 1, 2021
We provide the first comprehensive analysis of the information content from options markets for predicting the cross-section of stock returns. We jointly examine an extensive set of firm characteristics and an exhaustive set of option predictors, filling the void between two largely disjoint literatures. Using both portfolio sorts and machine learning methods, we find that options have strong predictive power for the cross-section of returns after controlling for firm characteristics. A structural analysis shows that the strongest predictors are associated with tail risk premia and leverage. Our findings imply that these risks are estimated more accurately from options data, providing annualized Sharpe ratios in excess 1.5.
Keywords: Asset Pricing, Factor Models, High-dimensional Methods, Option-implied Risk
JEL Classification: C13, C14, G11, G12, G14
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