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

63 Pages Posted: 24 Feb 2020 Last revised: 11 Jun 2021

See all articles by Joachim Grammig

Joachim Grammig

University of Tuebingen

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: February 12, 2020

Abstract

We assess financial theory-based and machine learning methods to quantify stock risk premia and investigate the potential of hybrid strategies by comparing the quality of the respective excess return forecasts. 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, 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.

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 (February 12, 2020). Available at SSRN: https://ssrn.com/abstract=3536835 or http://dx.doi.org/10.2139/ssrn.3536835

Joachim Grammig

University of Tuebingen ( email )

Wilhelmstr. 19
72074 Tuebingen, 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
+49 69 798 33699 (Phone)

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
413
Abstract Views
1,780
rank
91,078
PlumX Metrics