Option return predictability with machine learning and big data
Turan G. Bali,
Mathis Moerke and
No 21-08, CFR Working Papers from University of Cologne, Centre for Financial Research (CFR)
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.
Keywords: Machine learning; big data; option return predictability (search for similar items in EconPapers)
JEL-codes: G10 G12 G13 G14 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa and nep-fmk
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfrwps:2108
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