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

By Martin Lettau, Markus Pelger. The Review of financial studies, 2018

212 citations recorded by the source corpus.

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.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e5f734d5307bc486fd1faf48b213fc7dbe7fc8b5

How researchers see this paper

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In Management Science and Operations Research

1. Ruoxuan Xiong
2. Markus Pelger
3. Jason Zhu
4. Svetlana Bryzgalova
5. Martin Lettau
6. Luyang Chen

Citations in the corpus

• Short- and Long-Horizon Behavioral Factors (2019, unknown): “A second approach is to extract factors using a statistical analysis of the returns to assets or characteristic-sorted portfolios, while potentially imposing structural constraints implied by rational asset pricing theory (Kelly, Pruitt, and Su, 2017; Freyberger, Neuhierl, and Weber, 2017; Kozak, Na”
• State-Varying Factor Models of Large Dimensions (2018, methodology): “They have been successfully used in finance, for example, Ross (1976), Chamberlain and Rothschild (1983), and Ludvigson and Ng (2009), and in macro-economics, for example, Stock and Watson (2002) and Jurado, Ludvigson, and Ng (2015).”
• Deep Learning in Asset Pricing (2019, background): “Lettau and Pelger (2020) extend this important insight to RP-PCA rotated factors.”
• Estimating latent asset-pricing factors (2018, methodology): “The same data is studied in more detail in our companion paper Lettau and Pelger (2018). Based on the universe of U.S. firms in CRSP, we consider 37 anomaly characteristics following standard definitions in Novy-Marx and Velikov (2016), McLean and Pontiff (2016) and Kogan and Tian (2015).”
• Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference (2019, methodology): “Li and Bell (2017) propose using the LASSO method to select control units and Carvalho et al.”
• Forest Through the Trees: Building Cross-Sections of Stock Returns (2020, methodology): “Lettau and Pelger (2018) extend principal component analysis to include a cross-sectional pricing restriction that helps to identify weak factors, and our paper is based on a similar intuition, relying on a no-arbitrage criterion to select the optimal tree portfolios. Kelly, Pruitt, and Su (2019) an”
• Understanding Systematic Risk: A High‐Frequency Approach (2020, unknown): “Lettau and Pelger (2020) generalize PCA by including a penalty on the pricing error in expected returns.”