Shrinking Factor Dimension: A Reduced-Rank Approach

Ai He (), Dashan Huang (), Jiaen Li () and Guofu Zhou ()
Additional contact information
Ai He: Darla Moore School of Business, University of South Carolina, Columbia, South Carolina 29208
Dashan Huang: Lee Kong Chian School of Business, Singapore Management University, Singapore 178899
Jiaen Li: Olin School of Business, Washington University in St. Louis, St. Louis, Missouri 63130
Guofu Zhou: Olin School of Business, Washington University in St. Louis, St. Louis, Missouri 63130

Management Science, 2023, vol. 69, issue 9, 5501-5522

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 principal component analysis, partial least squares, and least absolute shrinkage and selection operator 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; LASSO; dimension reduction (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2022.4563 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:69:y:2023:i:9:p:5501-5522

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().