Shrinking Factor Dimension: A Reduced-Rank Approach
48 Pages Posted: 23 Jul 2018 Last revised: 21 Mar 2022
Date Written: December 17, 2019
Abstract
We provide a reduced-rank approach (RRA) to extract a few factors from a large set of factor proxies, and apply the extracted factors to model the cross section of expected stock returns. Empirically, we find that the RRA five-factor model outperforms the well known Fama-French five-factor model as well as the corresponding PCA, PLS and LASSO models for pricing portfolios. However, at the stock level, our RRA factor model still has large pricing errors even after adding more factors, suggesting that the representative factor proxies of our study do not have sufficient information for pricing individual stocks.
Keywords: reduced rank, PCA, PLS, factors, factor model, cross section
JEL Classification: G1, G11, G12, G17
Suggested Citation: Suggested Citation