Alpha Go Everywhere: Machine Learning and International Stock Returns

作者：Darwin Choi, Wenxi Jiang, Chao Zhang · 发表于：The Review of Asset Pricing Studies · 年份：2025 · DOI：10.1093/rapstu/raaf005 · 被引用次数：10 · 研究领域：Market Dynamics and Volatility、Stock Market Forecasting Methods、Complex Systems and Time Series Analysis

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)