Shrinking factor dimension: A reduced-rank approach
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 prox...
Saved in:
| Main Authors: | , , , |
|---|---|
| 格式: | text |
| 語言: | English |
| 出版: |
Institutional Knowledge at Singapore Management University
2023
|
| 主題: | |
| 在線閱讀: | https://ink.library.smu.edu.sg/lkcsb_research/5924 https://ink.library.smu.edu.sg/context/lkcsb_research/article/6923/viewcontent/SSRN_id3205697.pdf |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 機構: | Singapore Management University |
| 語言: | English |
| 總結: | 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. |
|---|