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Shrinking Factor Dimension: A Reduced-Rank Approach

Author

Listed:
  • Ai He

    (Darla Moore School of Business, University of South Carolina, Columbia, South Carolina 29208)

  • Dashan Huang

    (Lee Kong Chian School of Business, Singapore Management University, Singapore 178899)

  • Jiaen Li

    (Olin School of Business, Washington University in St. Louis, St. Louis, Missouri 63130)

  • Guofu Zhou

    (Olin School of Business, Washington University in St. Louis, St. Louis, Missouri 63130)

Abstract

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.

Suggested Citation

  • Ai He & Dashan Huang & Jiaen Li & Guofu Zhou, 2023. "Shrinking Factor Dimension: A Reduced-Rank Approach," Management Science, INFORMS, vol. 69(9), pages 5501-5522, September.
    • Handle: RePEc:inm:ormnsc:v:69:y:2023:i:9:p:5501-5522
    DOI: 10.1287/mnsc.2022.4563
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