The Macroeconomy and the Cross-Section of International Equity Index Returns: A Machine Learning Approach
62 Pages Posted: 13 Nov 2019
Date Written: October 2019
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.
Keywords: Asset Pricing, Equity Indices, Return Forecasting, Machine Learning, Neural Networks, Macroeconomic predictability
JEL Classification: C21, C45, C58, C38, G12
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