Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/242849 
Year of Publication: 
2021
Series/Report no.: 
CFR Working Paper No. 21-08
Publisher: 
University of Cologne, Centre for Financial Research (CFR), Cologne
Abstract: 
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, costly arbitrage, and option mispricing.
Subjects: 
Machine learning
big data
option return predictability
JEL: 
G10
G12
G13
G14
Document Type: 
Working Paper

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