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Regularized Factor Portfolio for Cross-sectional Multifactor Models

Author

Listed:
  • Mian Huang

    (Shanghai University of Finance and Economics)

  • Shangbing Yu

    (Shanghai University of Finance and Economics)

  • Weixin Yao

    (University of California)

Abstract

Factor portfolio is an important concept in the field of active portfolio management. However, the traditional factor portfolio lacks of theoretical results and is not investable since it is over-diversified. To extend its applicability and capacity, we propose a new approach to define and construct a regularized factor portfolio. The new factor portfolio is investable due to the sparsity of estimated weights, allows to control the risk of factor portfolio and the gross exposure on the weights of factor portfolio, and reduces the computational burden when the number of stocks is large. Under the new framework, we are able to develop risk theory for the optimal portfolios and provide an upper bound for the optimized risk, which is close to the theoretical optimal one. The performance of our new approach is illustrated by simulation and empirical studies on the stocks from Russell 1000.

Suggested Citation

  • Mian Huang & Shangbing Yu & Weixin Yao, 2022. "Regularized Factor Portfolio for Cross-sectional Multifactor Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 427-449, August.
    • Handle: RePEc:spr:sankha:v:84:y:2022:i:2:d:10.1007_s13171-020-00201-8
    DOI: 10.1007/s13171-020-00201-8
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    References listed on IDEAS

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