Arbeitspapier
Option return predictability with machine learning and big data
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: CFR Working Paper ; No. 21-08
- Klassifikation
-
Wirtschaft
General Financial Markets: General (includes Measurement and Data)
Asset Pricing; Trading Volume; Bond Interest Rates
Contingent Pricing; Futures Pricing; option pricing
Information and Market Efficiency; Event Studies; Insider Trading
- Thema
-
Machine learning
big data
option return predictability
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Bali, Turan G.
Beckmeyer, Heiner
Moerke, Mathis
Weigert, Florian
- Ereignis
-
Veröffentlichung
- (wer)
-
University of Cologne, Centre for Financial Research (CFR)
- (wo)
-
Cologne
- (wann)
-
2021
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Bali, Turan G.
- Beckmeyer, Heiner
- Moerke, Mathis
- Weigert, Florian
- University of Cologne, Centre for Financial Research (CFR)
Entstanden
- 2021
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