Diverging Roads
Theory-Based vs. Machine Learning-Implied Stock Risk Premia
- verfasst von
- Joachim Grammig, Constantin Hanenberg, Christian Schlag, Jantje Sönksen
- Abstract
We assess financial theory-based and machine learning methods to quantify stock risk premia and investigate the potential of hybrid strategies. In the low signal-to-noise environment of a one-month investment horizon, we recommend to rely on a theory-based strategy that exploits the information in current option prices, especially if the risk premium estimate is to be updated at a high frequency. At the one-year horizon, the theory/option-based strategy and an ensemble of neural networks, two notably different methodologies, perform comparably well. A random forest can improve on the theory-based method, provided that a sufficiently long training period is used. In an effort to connect the opposing philosophies, we identify the use of a random forest to account for the approximation errors of the theory-based approach towards measuring stock risk premia as a promising hybrid strategy. It combines the advantages of two diverging roads in the finance world.
- Organisationseinheit(en)
-
Institut für Ökonometrie und Data Science
- Externe Organisation(en)
-
Eberhard Karls Universität Tübingen
Universität zu Köln
Goethe-Universität Frankfurt am Main
Leibniz-Institut für Finanzmarktforschung SAFE
- Typ
- Artikel
- Journal
- Journal of Financial Econometrics
- Band
- 23
- Seiten
- 1-55
- Anzahl der Seiten
- 90
- ISSN
- 1479-8409
- Publikationsdatum
- 10.03.2025
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Finanzwesen, Volkswirtschaftslehre und Ökonometrie
- Elektronische Version(en)
-
https://doi.org/10.1093/jjfinec/nbaf005 (Zugang:
Geschlossen)
https://doi.org/10.2139/ssrn.3536835 (Zugang: Offen)
https://doi.org/10.15496/publikation-39286 (Zugang: Offen)
