A Bayesian Information Criterion for Portfolio Selection

32 Pages Posted: 17 Jun 2011

See all articles by Wei Lan

Wei Lan

Peking University - Guanghua School of Management

Hansheng Wang

Peking University - Guanghua School of Management

Chih-Ling Tsai

University of California, Davis - Graduate School of Management

Date Written: May 8, 2011

Abstract

The mean-variance theory of Markowitz (1952) indicates that large investment portfolios naturally provide better risk diversi cation 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.

Keywords: Bayesian Information Criterion, Minimal Variance Portfolio, Portfolio Selection, Risk Diversification

JEL Classification: C12, C13

Suggested Citation

Lan, Wei and Wang, Hansheng and Tsai, Chih-Ling, A Bayesian Information Criterion for Portfolio Selection (May 8, 2011). Available at SSRN: https://ssrn.com/abstract=1863631 or http://dx.doi.org/10.2139/ssrn.1863631

Wei Lan

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

Hansheng Wang (Contact Author)

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

HOME PAGE: http://hansheng.gsm.pku.edu.cn

Chih-Ling Tsai

University of California, Davis - Graduate School of Management ( email )

One Shields Avenue
Davis, CA 95616
United States

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