Alpha Go Everywhere: Machine Learning and International Stock Returns

We apply machine learning techniques to predict international stock returns using firm characteristics. Market-specific features are 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 foreign variables constructed from U.S. firm characteristics further enhances the return predictability of market-specific NNs, consistent with the notion that the markets are partially integrated.

Keywords: International Asset Pricing, Cross-section of Stock Returns, Market Integration, Neural Networks, Regression Trees

JEL Classification: C52, G10, G12, G15

Suggested Citation: Suggested Citation

Choi, Darwin and Jiang, Wenxi and Zhang, Chao, Alpha Go Everywhere: Machine Learning and International Stock Returns (March 22, 2022). Available at SSRN: https://ssrn.com/abstract=3489679 or http://dx.doi.org/10.2139/ssrn.3489679