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Option Return Predictability with Machine Learning and Big Data

Author

Listed:
  • Turan G Bali
  • Heiner Beckmeyer
  • Mathis Mörke
  • Florian Weigert
  • Stefano Giglio

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. The nonlinear machine learning models generate statistically and economically sizable 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.Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Suggested Citation

  • Turan G Bali & Heiner Beckmeyer & Mathis Mörke & Florian Weigert & Stefano Giglio, 2023. "Option Return Predictability with Machine Learning and Big Data," The Review of Financial Studies, Society for Financial Studies, vol. 36(9), pages 3548-3602.
    • Handle: RePEc:oup:rfinst:v:36:y:2023:i:9:p:3548-3602.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhad017
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    More about this item

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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