Stock Option Predictability for the Cross-Section

46 Pages Posted: 8 Mar 2021 Last revised: 12 Apr 2021

See all articles by Andreas Neuhierl

Andreas Neuhierl

Washington University in St. Louis - John M. Olin Business School

Xiaoxiao Tang

University of Texas at Dallas - School of Management - Department of Finance & Managerial Economics

Rasmus Tangsgaard Varneskov

Copenhagen Business School - Department of Finance; Nordea Bank AB - Nordea Asset Management

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School

Date Written: March 1, 2021

Abstract

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

Suggested Citation

Neuhierl, Andreas and Tang, Xiaoxiao and Varneskov, Rasmus Tangsgaard and Zhou, Guofu, Stock Option Predictability for the Cross-Section (March 1, 2021). Available at SSRN: https://ssrn.com/abstract=3795486 or http://dx.doi.org/10.2139/ssrn.3795486

Andreas Neuhierl (Contact Author)

Washington University in St. Louis - John M. Olin Business School ( email )

St. Louis, MO
United States

Xiaoxiao Tang

University of Texas at Dallas - School of Management - Department of Finance & Managerial Economics ( email )

2601 North Floyd Road
P.O. Box 830688
Richardson, TX 75083
United States

Rasmus Tangsgaard Varneskov

Copenhagen Business School - Department of Finance ( email )

A4.17 Solbjerg Plads 3
Copenhagen, Frederiksberg 2000
Denmark

Nordea Bank AB - Nordea Asset Management ( email )

PO Box 850
Copenhagen, 0900
Denmark

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

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