Journal
INSURANCE MATHEMATICS & ECONOMICS
Volume 59, Issue -, Pages 243-250Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.insmatheco.2014.10.001
Keywords
Portfolio selection; Uncertain programming; Genetic algorithm; Mean-chance model; Uncertain variable
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Funding
- National Natural Science Foundation of China [71171018]
- Specialized Research Fund for the Doctoral Program of Higher Education Grant [20130006110001]
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This paper discusses a portfolio selection problem in which security returns are given by experts' evaluations instead of historical data. A factor method for evaluating security returns based on experts' judgment is proposed and a mean-chance model for optimal portfolio selection is developed taking transaction costs and investors' preference on diversification and investment limitations on certain securities into account. The factor method of evaluation can make good use of experts' knowledge on the effects of economic environment and the companies' unique characteristics on security returns and incorporate the contemporary relationship of security returns in the portfolio. The use of chance of portfolio return failing to reach the threshold can help investors easily tell their tolerance toward risk and thus facilitate a decision making. To solve the proposed nonlinear programming problem, a genetic algorithm is provided. To illustrate the application of the proposed method, a numerical example is also presented. (C) 2014 Elsevier B.V. All rights reserved.
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