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本文研究了投资组合管理中的证券子集选择问题,通过分析证券组合有效子集与均值-方差张成的关系,给出了一种新的基于统计推断的证券子集有效性检验方法,同时也拓展了Huberman和Kandel[Journalof Finance,1987]的均值-方差张成条件。实证结果表明,本文的方法相对于现有的基于矩阵秩的判别方法更稳健。
In this paper, we study the selection of securities subsets in portfolio management. By analyzing the relationship between the effective subsets of securities portfolio and the mean-variance spread, we propose a new method to test the validity of securities subsets based on statistical inference. Simultaneously, Also extends the mean-variance spread conditions of Huberman and Kandel [Journal of Finance, 1987]. The empirical results show that the proposed method is more robust than the existing discriminant methods based on matrix rank.