Shrinking Factor Dimension: A Reduced-Rank Approach
提出降秩方法从大量因子代理中提取少数因子，用于建模股票预期收益的横截面。实证发现该五因子模型在投资组合定价上优于Fama-French五因子模型等，但在个股层面仍有较大定价误差。
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. This paper was accepted by Lukas Schmid, finance. Funding: D. Huang acknowledges that this study was partially funded at the Singapore Management University through a research [Grant MSS20B016] from the Ministry of Education Academic Research Fund Tier 1. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4563 .