The Macroeconomy and the Cross-Section of International Equity Index Returns: A Machine Learning Approach

The paper evaluates the out-of-sample predictive ability of machine learning methods in the cross-section of international equity index returns using both firm fundamentals and macroeconomic predictors. The study performs a horserace between classical forecasting methods and the machine learning repertoire, including principal component analysis, partial least squares, and neural networks. Macroeconomic signals seem to substantially improve out-of-sample performance, especially when non-linear features are incorporated via neural networks.