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

Martin Lettau and Markus Pelger

No 24858, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C14 C38 C52 C58 G0 G12 (search for similar items in EconPapers)
Date: 2018-07
New Economics Papers: this item is included in nep-fmk and nep-ore
Note: AP
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Citations: View citations in EconPapers (6)

Published as Martin Lettau & Markus Pelger, 2020. "Factors That Fit the Time Series and Cross-Section of Stock Returns," The Review of Financial Studies, vol 33(5), pages 2274-2325.

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Related works:
Journal Article: Factors That Fit the Time Series and Cross-Section of Stock Returns (2020)
Working Paper: Factors that Fit the Time Series and Cross-Section of Stock Returns (2018)
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