Working Paper: NBER ID: w24858
Authors: Martin Lettau; Markus Pelger
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: latent asset pricing factors; principal component analysis; risk premium; Sharpe ratio
JEL Codes: C14; C38; C52; C58; G0; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| RPPCA (R50) | PCA (C38) |
| RPPCA (R50) | weak factors (D91) |
| RPPCA (R50) | pricing information (D49) |
| RPPCA (R50) | variation and comovement (C32) |
| RPPCA (R50) | weak factors for stochastic discount factor (D15) |