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Feature selection for portfolio optimization

Author

Listed:
  • Thomas Trier Bjerring

    (Technical University of Denmark)

  • Omri Ross

    (Technical University of Denmark
    University of Copenhagen)

  • Alex Weissensteiner

    (Technical University of Denmark
    Free University of Bozen-Bolzano)

Abstract

Most portfolio selection rules based on the sample mean and covariance matrix perform poorly out-of-sample. Moreover, there is a growing body of evidence that such optimization rules are not able to beat simple rules of thumb, such as 1/N. Parameter uncertainty has been identified as one major reason for these findings. A strand of literature addresses this problem by improving the parameter estimation and/or by relying on more robust portfolio selection methods. Independent of the chosen portfolio selection rule, we propose using feature selection first in order to reduce the asset menu. While most of the diversification benefits are preserved, the parameter estimation problem is alleviated. We conduct out-of-sample back-tests to show that in most cases different well-established portfolio selection rules applied on the reduced asset universe are able to improve alpha relative to different prominent factor models.

Suggested Citation

  • Thomas Trier Bjerring & Omri Ross & Alex Weissensteiner, 2017. "Feature selection for portfolio optimization," Annals of Operations Research, Springer, vol. 256(1), pages 21-40, September.
  • Handle: RePEc:spr:annopr:v:256:y:2017:i:1:d:10.1007_s10479-016-2155-y
    DOI: 10.1007/s10479-016-2155-y
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    References listed on IDEAS

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    Cited by:

    1. Jingnan Chen & Gengling Dai & Ning Zhang, 2020. "An application of sparse-group lasso regularization to equity portfolio optimization and sector selection," Annals of Operations Research, Springer, vol. 284(1), pages 243-262, January.
    2. Thomas Trier Bjerring & Kourosh Marjani Rasmussen & Alex Weissensteiner, 2018. "Portfolio selection under supply chain predictability," Computational Management Science, Springer, vol. 15(2), pages 139-159, June.
    3. Han Yang & Ming-hui Wang & Nan-jing Huang, 2021. "The $$\alpha$$ α -Tail Distance with an Application to Portfolio Optimization Under Different Market Conditions," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1195-1224, December.
    4. Pablo Cristini Guedes & Fernanda Maria Müller & Marcelo Brutti Righi, 2023. "Risk measures-based cluster methods for finance," Risk Management, Palgrave Macmillan, vol. 25(1), pages 1-56, March.

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