Shrinking Factor Dimension: A Reduced-Rank Approach

• 成果类型: Article
• 署名作者: He, Ai; Huang, Dashan; Li, Jiaen; Zhou, Guofu
• 署名单位: University of South Carolina System; University of South Carolina Columbia; Singapore Management University; Washington University (WUSTL)
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.4563
• 发表日期: 2023
• 页码: 5501-5522
• 关键词: reduced rank; PCA; PLS; Lasso; Dimension Reduction

摘要

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 wellknown 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.