A Bayesian Information Criterion for Portfolio Selection
32 Pages Posted: 17 Jun 2011
Date Written: May 8, 2011
Abstract
The mean-variance theory of Markowitz (1952) indicates that large investment portfolios naturally provide better risk diversication 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: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
Portfolio Selection and Asset Pricing Models
By Lubos Pastor
-
A Test for the Number of Factors in an Approximate Factor Model
-
Comparing Asset Pricing Models: an Investment Perspective
By Lubos Pastor and Robert F. Stambaugh
-
Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps
By Tongshu Ma and Ravi Jagannathan
-
On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model
By Louis K.c. Chan, Jason J. Karceski, ...
-
Honey, I Shrunk the Sample Covariance Matrix
By Olivier Ledoit and Michael Wolf
-
Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach
By Lorenzo Garlappi, Tan Wang, ...
-
Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach
By Lorenzo Garlappi, Tan Wang, ...
-
Portfolio Constraints and the Fundamental Law of Active Management