Diverging Roads: Theory-Based vs. Machine Learning-Implied Stock Risk Premia

80 Pages Posted: 24 Feb 2020 Last revised: 27 Oct 2022

See all articles by Joachim Grammig

Joachim Grammig

University of Tübingen

Constantin Hanenberg

University of Tuebingen - Faculty of Economics and Social Sciences

Christian Schlag

Goethe University Frankfurt; Leibniz Institute for Financial Research SAFE

Jantje Sönksen

University of Tübingen

Date Written: October 21, 2022

Abstract

We assess financial theory-based and machine learning methods to quantify stock risk premia and investigate the potential of hybrid strategies. The results indicate that at the one-month investment horizon, a theory-based approach using option prices is preferable, especially if risk premium estimates get updated at high frequencies. At the one-year horizon, a random forest with sufficiently long training delivers a better performance than option-based models. The integration of machine learning procedures to address the approximation errors of a theory-based approach is identified as a novel and promising hybrid strategy.

Keywords: stock risk premia, option prices, machine learning

JEL Classification: C53, C58, G12, G17

Suggested Citation

Grammig, Joachim and Hanenberg, Constantin and Schlag, Christian and Sönksen, Jantje, Diverging Roads: Theory-Based vs. Machine Learning-Implied Stock Risk Premia (October 21, 2022). Available at SSRN: https://ssrn.com/abstract=3536835 or http://dx.doi.org/10.2139/ssrn.3536835

Joachim Grammig

University of Tübingen ( email )

Mohlstrasse 36
72074 Tübingen, Baden Wuerttemberg 72074
Germany

Constantin Hanenberg

University of Tuebingen - Faculty of Economics and Social Sciences ( email )

Mohlstrasse 36
Tuebingen, Baden-Wuerttemberg 72074
Germany

Christian Schlag

Goethe University Frankfurt ( email )

Faculty of Economics and Business
Theodor-W.-Adorno-Platz 3
Frankfurt am Main, Hessen 60323
Germany

Leibniz Institute for Financial Research SAFE ( email )

(http://www.safe-frankfurt.de)
Theodor-W.-Adorno-Platz 3
Frankfurt am Main, 60323
Germany

Jantje Sönksen (Contact Author)

University of Tübingen ( email )

Sigwartstr. 18
Tübingen, Baden-Wuerttemberg 72076
Germany

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
729
Abstract Views
2,734
Rank
59,262
PlumX Metrics