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

Martin Lettau, Markus Pelger and Stijn Van Nieuwerburgh

The Review of Financial Studies, 2020, vol. 33, issue 5, 2274-2325

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. Our approach finds weak factors with high Sharpe ratios that PCA cannot detect. We discover five factors with economic meaning that explain well the cross-section and time series of characteristic-sorted portfolio returns. The out-of-sample maximum Sharpe ratio of our factors is twice as large as with PCA with substantially smaller pricing errors. Our factors imply that a significant amount of characteristic information is redundant.Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

JEL-codes: C14 C52 C58 G12 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (52)

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Working Paper: Factors that Fit the Time Series and Cross-Section of Stock Returns (2018)
Working Paper: Factors that Fit the Time Series and Cross-Section of Stock Returns (2018)
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