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

67 Pages Posted: 13 Nov 2019 Last revised: 15 Mar 2020

Date Written: December 2019

Abstract

The paper evaluates the out-of-sample predictive potential of machine learning methods in the cross-section of international equity index returns using firm fundamentals and macroeconomic predictors. The relatively small number of equity indices in the cross-section compared to the multitude of predictive signals, makes this an ideal setting to examine return predictability using machine learning techniques. I find that macroeconomic signals seem to substantially improve out-of-sample performance, especially when non-linear features are incorporated via neural networks. The performance of a long-short country bet based on forecasted returns cannot be explained by standard definitions of risk.

Keywords: Asset Pricing, Equity Indices, Return Forecasting, Machine Learning, Neural Networks, Macroeconomic Predictability

JEL Classification: C21, C45, C58, C38, G12

Suggested Citation

Popescu, Andreea Victoria, The Macroeconomy and the Cross-Section of International Equity Index Returns: A Machine Learning Approach (December 2019). Available at SSRN: https://ssrn.com/abstract=3480042 or http://dx.doi.org/10.2139/ssrn.3480042

Andreea Victoria Popescu (Contact Author)

APG Asset Management ( email )

Gustav Mahlerplein 3
Amsterdam, 1082 MS
Netherlands

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