The Joint Cross Section of Option and Stock Returns Predictability with Big Data and Machine Learning

66 Pages Posted: 2 Mar 2021 Last revised: 23 Mar 2022

See all articles by Ruslan Goyenko

Ruslan Goyenko

McGill University - Desautels Faculty of Management

Chengyu Zhang

McGill University - Desautels Faculty of Management

Date Written: December 11, 2020

Abstract

Which market has leading informational advantage: stocks or options? Using large set of stock
and option characteristics, and machine learning, we provide a comprehensive analysis of which
characteristics are the first order importance predictors of option and stock returns. First, we find
that option, rather than stock, characteristics are dominant predictors of option returns. Second, option, rather than stock, characteristics are also dominant predictors of stock returns. Consistent with the argument that an increase in trading activity in derivatives decreases information asymmetry about the underlying, option illiquidity is identified as the most important predictor of both stock and option
returns.

Keywords: Machine learning, Option pricing, Stock return predictability

JEL Classification: G10, G12, G13, G14

Suggested Citation

Goyenko, Ruslan and Zhang, Chengyu, The Joint Cross Section of Option and Stock Returns Predictability with Big Data and Machine Learning (December 11, 2020). Available at SSRN: https://ssrn.com/abstract=3747238 or http://dx.doi.org/10.2139/ssrn.3747238

Ruslan Goyenko (Contact Author)

McGill University - Desautels Faculty of Management ( email )

1001 Sherbrooke St. West
Montreal, Quebec H3A1G5 H3A 2M1
Canada

Chengyu Zhang

McGill University - Desautels Faculty of Management ( email )

1001 Sherbrooke St. West
Montreal, Quebec H3A1G5 H3A 2M1
Canada

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