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Some new efficient mean–variance portfolio selection models

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  • Zhifeng Dai
  • Jie Kang

Abstract

The poor out‐of‐sample performance of mean–variance portfolio model is mainly caused by estimation errors in the covariance matrix and the mean return, especially the mean return vector. Meanwhile, in financial practice, what most investors actually like is to hold a few stocks in their portfolio. The goal of this paper is to propose some new efficient mean–variance portfolio selection models by considering the following aspects: (i) use the L1‐regularization in objective function to obtain sparse portfolio; (ii) use the shrinkage method of Ledoit and Wolf, Journal of Economics Financial, 2003, 10, 603–621 to estimate the covariance matrix; (iii) use the robust optimization method to mitigate the estimation errors of the expected return. Finally, empirical analysis demonstrates that the proposed strategies have better out‐of‐sample performance.

Suggested Citation

  • Zhifeng Dai & Jie Kang, 2022. "Some new efficient mean–variance portfolio selection models," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4784-4796, October.
    • Handle: RePEc:wly:ijfiec:v:27:y:2022:i:4:p:4784-4796
    DOI: 10.1002/ijfe.2400
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    References listed on IDEAS

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