Diverging Roads: Theory-Based vs. Machine Learning-Implied Stock Risk Premia
69 Pages Posted: 24 Feb 2020 Last revised: 23 Mar 2020
Date Written: February 12, 2020
We assess financial theory-based and machine learning-implied measurements of stock risk premia by comparing the quality of their return forecasts. In the low signal-to-noise environment of a one month horizon, we find that it is preferable to rely on a theory-based approach instead of engaging in the computer-intensive hyper-parameter tuning of statistical models. The theory-based approach also delivers a solid performance at the one year horizon, at which only one machine learning methodology (random forest) performs substantially better. We also consider ways to combine the opposing modeling philosophies, and identify the use of random forests to account for the approximation residuals of the theory-based approach as a promising hybrid strategy. It combines the advantages of the two diverging paths in the finance world.
Keywords: stock risk premia, return forecasts, machine learning, theory-based return prediction
JEL Classification: C53, C58, G12, G17
Suggested Citation: Suggested Citation