Factors That Fit the Time Series and Cross-Section of Stock Returns

61 Pages Posted: 1 Aug 2018 Last revised: 30 Jan 2020

See all articles by Martin Lettau

Martin Lettau

University of California - Haas School of Business; Centre for Economic Policy Research (CEPR); National Bureau of Economic Research (NBER)

Markus Pelger

Stanford University - Management Science & Engineering

Multiple version iconThere are 3 versions of this paper

Date Written: November 19, 2019

Abstract

We propose a new method for estimating latent asset pricing factors that fit the time-series and cross-section of expected returns. Our estimator generalizes Principal Component Analysis (PCA) by including a penalty on the pricing error in expected returns. We show that our estimator strongly dominates PCA and finds weak factors with high Sharpe-ratios that PCA cannot detect. Studying a large number of characteristic sorted portfolios we find that five latent factors with economic meaning explain well the cross-section and time-series of returns. We show that out-of-sample the maximum Sharpe-ratio of our five factors is more than twice as large as with PCA with significantly smaller pricing errors. Our factors are based on only a subset of the stock characteristics implying that a significant amount of characteristic information is redundant.

Keywords: Cross Section Of Returns, Anomalies, Expected Returns, High-Dimensional Data, Latent Factors, Weak Factors, PCA

JEL Classification: C14, C52, C58, G12

Suggested Citation

Lettau, Martin and Pelger, Markus, Factors That Fit the Time Series and Cross-Section of Stock Returns (November 19, 2019). Available at SSRN: https://ssrn.com/abstract=3211106 or http://dx.doi.org/10.2139/ssrn.3211106

Martin Lettau

University of California - Haas School of Business ( email )

Haas School of Business
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Berkeley, CA 94720
United States
5106436349 (Phone)

HOME PAGE: http://faculty.haas.berkeley.edu/lettau/

Centre for Economic Policy Research (CEPR)

London
United Kingdom

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Markus Pelger (Contact Author)

Stanford University - Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

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