Diverging Roads: Theory-Based vs. Machine Learning-Implied Stock Risk Premia
63 Pages Posted: 24 Feb 2020 Last revised: 11 Jun 2021
Date Written: February 12, 2020
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: Suggested Citation