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Some improved sparse and stable portfolio optimization problems

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  • Dai, Zhifeng
  • Wen, Fenghua

Abstract

Parameter uncertainty and estimation errors often cause the presence of unstable asset weights and the poor performance of portfolio model. In addition, in the real world, most investors prefer to choose a small number of stocks to invest. In this paper, we propose some improved sparse and stable portfolio models by combining the shrinkage method and objective function L1 regularization method. An ‘optimal’ shrinkage constant is obtained by minimizes the expected distance between the shrinkage estimator and the true covariance matrix. Moreover, we investigate the combination of the constant correlation and objective function L1 regularization method. Empirical studies show that the proposed strategies have better out-of-sample performance than many other strategies for tested datasets.

Suggested Citation

  • Dai, Zhifeng & Wen, Fenghua, 2018. "Some improved sparse and stable portfolio optimization problems," Finance Research Letters, Elsevier, vol. 27(C), pages 46-52.
    • Handle: RePEc:eee:finlet:v:27:y:2018:i:c:p:46-52
    DOI: 10.1016/j.frl.2018.02.026
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    More about this item

    Keywords

    Portfolio optimization; Minimum-variance model; Sparse and stable portfolios;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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