Option Return Predictability with Machine Learning and Big Data
121 Pages Posted: 18 Aug 2021 Last revised: 11 Jan 2022
Date Written: July 29, 2021
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. Besides statistical significance, the nonlinear machine learning models generate economically sizeable 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.
Keywords: Machine learning, big data, option return predictability
JEL Classification: G10, G12, G13, G14
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