Portfolio selection with qualitative input

• Published in: Journal of banking & finance Vol. 36; no. 2; pp. 489 - 496
• Main Authors: Chiarawongse, Anant, Kiatsupaibul, Seksan, Tirapat, Sunti, Roy, Benjamin Van
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier 01.02.2012; Elsevier Sequoia S.A
• Subjects: Algorithms; Bayesian method; Expected returns; Heuristic; Inequality; Markov analysis; Markovian processes; Monte Carlo simulation; Optimization algorithms; Portfolio investments; Portfolio selection; Qualitative analysis; Simulation; Studies
• ISSN: 0378-4266; 1872-6372
• DOI: 10.1016/j.jbankfin.2011.08.005

We formulate a mean-variance portfolio selection problem that accommodates qualitative input about expected returns and 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. [PUBLICATION ABSTRACT]