Shrinking Factor Dimension: A Reduced-Rank Approach
62 Pages Posted: 23 Jul 2018 Last revised: 17 Jun 2021
Date Written: December 17, 2019
We apply a reduced-rank approach to reduce a large number of observable factors to a few parsimonious ones. Out of 70 factor proxies, we find that the best five combinations seem adequate and outperform the Fama-French (2015) five factors for pricing industry portfolios as expected. However, they do not improve much for pricing individual stocks. Our results suggest that new factors are wanted to reduce the pricing errors at the firm level.
Keywords: reduced rank, PCA, PLS, factors, factor model, cross section
JEL Classification: G1, G11, G12, G17
Suggested Citation: Suggested Citation