DP13049 Factors that Fit the Time Series and Cross-Section of Stock Returns

Author(s): Martin Lettau, Markus Pelger
Publication Date: July 2018
Date Revised: July 2018
Keyword(s): Anomalies, Cross Section of Returns, expected returns, high-dimensional data, Latent Factors, PCA, Weak Factors
JEL(s): C14, C52, C58, G12
Programme Areas: Financial Economics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=13049

We propose a new method for estimating latent asset pricing factors that fit the timeseries 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.