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A Bayesian information criterion for portfolio selection

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  • Lan, Wei
  • Wang, Hansheng
  • Tsai, Chih-Ling

Abstract

The mean-variance theory of Markowitz (1952) indicates that large investment portfolios naturally provide better risk diversification than small ones. However, due to parameter estimation errors, one may find ambiguous results in practice. Hence, it is essential to identify relevant stocks to alleviate the impact of estimation error in portfolio selection. To this end, we propose a linkage condition to link the relevant and irrelevant stock returns via their conditional regression relationship. Subsequently, we obtain a BIC selection criterion that enables us to identify relevant stocks consistently. Numerical studies indicate that BIC outperforms commonly used portfolio strategies in the literature.

Suggested Citation

  • Lan, Wei & Wang, Hansheng & Tsai, Chih-Ling, 2012. "A Bayesian information criterion for portfolio selection," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 88-99, January.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:1:p:88-99
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    References listed on IDEAS

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    Cited by:

    1. Shiguo Huang & Linyu Cao & Ruili Sun & Tiefeng Ma & Shuangzhe Liu, 2024. "Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization," Mathematics, MDPI, vol. 12(21), pages 1-21, October.
    2. Ruili Sun & Tiefeng Ma & Shuangzhe Liu, 2018. "A Stein-type shrinkage estimator of the covariance matrix for portfolio selections," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(8), pages 931-952, November.
    3. Ruili Sun & Tiefeng Ma & Shuangzhe Liu, 2020. "Portfolio selection: shrinking the time-varying inverse conditional covariance matrix," Statistical Papers, Springer, vol. 61(6), pages 2583-2604, December.

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