A Bayesian Information Criterion for Portfolio Selection

Wei Lan, Hansheng Wang, Chih-Ling Tsai
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引用次数: 8

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.
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投资组合选择的贝叶斯信息准则
Markowitz(1952)的均值方差理论表明,大的投资组合自然比小的投资组合具有更好的风险分散能力。然而,由于参数估计的误差,在实践中可能会发现模糊的结果。因此,在投资组合选择中识别相关股票以减轻估计误差的影响至关重要。为此,我们提出了一个联动条件,通过条件回归关系将相关和不相关的股票收益联系起来。随后,我们获得了一个BIC选择标准,使我们能够一致地识别相关股票。数值研究表明,BIC优于文献中常用的投资组合策略。
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A Bayesian Information Criterion for Portfolio Selection
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