Shrinking factor dimension: A reduced-rank approach

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...

Full description

Saved in:
Bibliographic Details
Main Authors: HE, Ai, HUANG, Dashan, LI, Jiaen, ZHOU, Guofu
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2023
Subjects:
reduced rank
PCA
PLS
factors
factor model
cross section
Corporate Finance
Finance and Financial Management
Online Access:https://ink.library.smu.edu.sg/lkcsb_research/5924
https://ink.library.smu.edu.sg/context/lkcsb_research/article/6923/viewcontent/SSRN_id3205697.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
Description
Summary: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.

Similar Items