Alpha Go Everywhere: Machine Learning and International Stock Returns
该研究运用机器学习技术，基于公司特征预测国际股票收益，发现针对每个市场单独训练的神经网络模型比全球模型更有效，且加入美国公司特征变量能进一步提升预测能力。
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.