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

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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.
Original languageEnglish
Place of PublicationTilburg
Number of pages62
Publication statusPublished - 13 Nov 2019



  • Asset Pricing
  • Equity Indices
  • Return Forecasting
  • Machine Learning
  • Neural Networks
  • Macroeconomic predictability

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