Publication Type
Journal Article
Version
submittedVersion
Publication Date
9-2023
Abstract
We propose a reduced-rank approach (RRA) to reduce a large number of factors to a few parsimonious ones. In contrast to PCA and PLS, the RRA factors are designed to explain the cross section of stock returns, not to maximize factor variations or factor covariances with returns. Out of 70 factor proxies, we find that five RRA factors outperform the Fama-French (2015) five factors for pricing target portfolios, but performs similarly for pricing individual stocks. Our results suggest that existing factor proxies do not provide enough new information at the stock level beyond the Fama-French (2015) five factors.
Keywords
reduced rank, PCA, PLS, factors, factor model, cross section
Discipline
Corporate Finance | Finance and Financial Management
Research Areas
Finance
Publication
Management Science
Volume
69
Issue
9
First Page
5501
Last Page
5522
ISSN
0025-1909
Identifier
10.1287/mnsc.2022.4563
Publisher
INFORMS
Citation
HE, Ai; HUANG, Dashan; LI, Jiaen; and ZHOU, Guofu.
Shrinking factor dimension: A reduced-rank approach. (2023). Management Science. 69, (9), 5501-5522.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/5924
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1287/mnsc.2022.4563