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

Marcial Messmer and Francesco Audrino
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Marcial Messmer: Department of Economics, School of Economics and Political Science, University of St. Gallen, Bodanstrasse 6, 9000 St. Gallen, Switzerland

Forecasting, 2022, vol. 4, issue 4, 1-35

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.

Keywords: factor models; cross-section of stock returns; lasso; simulation study (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2022
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