Diverging Roads: Theory-Based vs. Machine Learning-Implied Stock Risk Premia
80 Pages Posted: 24 Feb 2020 Last revised: 27 Oct 2022
Date Written: October 21, 2022
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
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