Option Return Predictability with Machine Learning and Big Data

Drawing upon more than 12 million observations over the period from 1996 to 2020,
we  nd that allowing for nonlinearities signi cantly 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 sizeable
pro ts 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 o er 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.