Option return predictability with machine learning and big data

Turan G. Bali, Heiner Beckmeyer, Mathis Moerke and Florian Weigert

No 21-08, CFR Working Papers from University of Cologne, Centre for Financial Research (CFR)

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.

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)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa and nep-fmk
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfrwps:2108

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