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

Joachim Grammig et al.

Journal of Financial Econometrics2025https://doi.org/10.1093/jjfinec/nbaf005article
AJG 3ABDC A*
Weight
0.37

Abstract

We compare the performance of theory-based and machine learning (ML) methods for quantifying equity risk premia and assess hybrid strategies that combine the two very different philosophies. The theory-based approach offers advantages at a one-month investment horizon, in particular, if daily frequency risk premium estimates (RPE) are needed. At the one-year horizon, ML has an edge, especially using theory-based RPE as additional feature variables. For a hybrid strategy called Theory with ML Assistance, we employ ML to account for the approximation errors of the theory-based approach. Employing random forests or an ensemble of ML models for theory support yields promising results.

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https://doi.org/https://doi.org/10.1093/jjfinec/nbaf005

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@article{joachim2025,
  title        = {{Diverging Roads: Theory-Based vs. Machine Learning-Implied Stock Risk Premia}},
  author       = {Joachim Grammig et al.},
  journal      = {Journal of Financial Econometrics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1093/jjfinec/nbaf005},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.