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Alpha Go Everywhere: Machine Learning and International Stock Returns

Darwin Choi et al.

Review of Asset Pricing Studies2025https://doi.org/10.1093/rapstu/raaf005article
AJG 3ABDC A*
Weight
0.52

Abstract

We apply machine learning techniques to predict international stock returns using firm characteristics. Market-specific training is important, as neural network models (NNs) achieve stronger results when they are trained in each market separately than in a global model trained with U.S. data. NNs outperform linear models in predicting stock return rankings and forming profitable portfolios. In contrast, regression trees underperform linear models when the number of observations is low. We also show that adding variables constructed from U.S. firm characteristics, which may contain information beyond the characteristics of international stocks, further enhances the return predictability of market-specific NNs. (JEL C52, G10, G12, G15)

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https://doi.org/https://doi.org/10.1093/rapstu/raaf005

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@article{darwin2025,
  title        = {{Alpha Go Everywhere: Machine Learning and International Stock Returns}},
  author       = {Darwin Choi et al.},
  journal      = {Review of Asset Pricing Studies},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1093/rapstu/raaf005},
}

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Alpha Go Everywhere: Machine Learning and International Stock Returns

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Evidence weight

0.52

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

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.65 × 0.15 = 0.10
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.