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Portfolio selection with qualitative input
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AI
DOI：10.1016/j.jbankfin.2011.08.005.png)
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Abstract
En 中文
We formulate a mean-variance portfolio selection problem that accommodates qualitative input about expected returns anti provide an algorithm that solves the problem. This model and algorithm can be used, for example, when a portfolio manager determines that one industry will benefit more from a regulatory change than another but is unable to quantify the degree of difference. Qualitative views are expressed in terms of linear inequalities among expected returns. Our formulation builds on the Black-Litterman model for portfolio selection. The algorithm makes use of an adaptation of the hit-and-run method for Markov chain Monte Carlo simulation. We also present computational results that illustrate advantages of our approach over alternative heuristic methods for incorporating qualitative input. (C) 2011 Elsevier B.V. All rights reserved.
Keywords:
Portfolio selection
Bayesian inference
Markov chain Monte Carlo
Black-Litterman model
Hit-and-run algorithm
Journal
J
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