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.
- Externe Organisation(en)
-
Goethe-Universität Frankfurt am Main
Eberhard Karls Universität Tübingen
Leibniz-Institut für Finanzmarktforschung SAFE
Universität zu Köln
- Typ
- Arbeitspapier/Diskussionspapier
- Anzahl der Seiten
- 90
- Publikationsdatum
- 24.02.2020
- Publikationsstatus
- Elektronisch veröffentlicht (E-Pub)
- Elektronische Version(en)
-
https://doi.org/10.2139/ssrn.3536835 (Zugang:
Offen)
https://doi.org/10.15496/publikation-39286 (Zugang: Offen)
