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The Lasso and the Factor Zoo-Predicting Expected Returns in the Cross-Section

Author

Listed:
  • Marcial Messmer

    (Department of Economics, School of Economics and Political Science, University of St. Gallen, Bodanstrasse 6, 9000 St. Gallen, Switzerland)

  • Francesco Audrino

    (Department of Economics, School of Economics and Political Science, University of St. Gallen, Bodanstrasse 6, 9000 St. Gallen, Switzerland)

Abstract

We investigate whether Lasso-type linear methods are able to improve the predictive accuracy of OLS in selecting relevant firm characteristics for forecasting the future cross-section of stock returns. Through extensive Monte Carlo simulations, we show that Lasso-type predictions are superior to OLS when type II errors are a concern. The results change if the aim is to minimize type I errors. Finally, we analyze the predictive performance of the competing methods on the US cross-section of stock returns between 1974 and 2020 and show that only small and micro-cap stocks are highly predictable throughout the entire sample.

Suggested Citation

  • Marcial Messmer & Francesco Audrino, 2022. "The Lasso and the Factor Zoo-Predicting Expected Returns in the Cross-Section," Forecasting, MDPI, vol. 4(4), pages 1-35, November.
    • Handle: RePEc:gam:jforec:v:4:y:2022:i:4:p:53-1003:d:984417
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    References listed on IDEAS

    as
    1. Ang, Andrew & Liu, Jun & Schwarz, Krista, 2020. "Using Stocks or Portfolios in Tests of Factor Models," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 55(3), pages 709-750, May.
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    Cited by:

    1. Marcos López de Prado & Joseph Simonian & Francesco A. Fabozzi & Frank J. Fabozzi, 2025. "Enhancing Markowitz's portfolio selection paradigm with machine learning," Annals of Operations Research, Springer, vol. 346(1), pages 319-340, March.

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