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

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, it is preferable to rely on a theory-based approach instead of engaging in computer-intensive hyperparameter 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.