Possibilistic mean-variance models and efficient frontiers for portfolio selection problem
Information Sciences: an International Journal
A new perspective for optimal portfolio selection with random fuzzy returns
Information Sciences: an International Journal
Portfolio optimization problems in different risk measures using genetic algorithm
Expert Systems with Applications: An International Journal
Solving a comprehensive model for multiobjective project portfolio selection
Computers and Operations Research
A portfolio optimization model with three objectives and discrete variables
Computers and Operations Research
Fuzzy mean-variance-skewness portfolio selection models by interval analysis
Computers & Mathematics with Applications
Nadir compromise programming: A model for optimization of multi-objective portfolio problem
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A portfolio selection model with borrowing constraint based on possibility theory
Applied Soft Computing
Selection of Socially Responsible Portfolios using Goal Programming and fuzzy technology
Information Sciences: an International Journal
Information Sciences: an International Journal
Multiobjective credibilistic portfolio selection model with fuzzy chance-constraints
Information Sciences: an International Journal
Some new results on value ranges of risks for mean-variance portfolio models
Information Sciences: an International Journal
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In financial markets, some nonprobabilistic factors can be modeled as fuzzy numbers. Based on possibility theory and the assumption that the returns of assets are triangular fuzzy numbers, a bi-objective nonlinear portfolio selection model is proposed in this paper. This model aims to maximize the future expected return and minimize the future expected risk. Moreover, the obtained nonlinear bi-objective model is equivalent to the linear bi-objective minimizing programming model on the basis of possibilistic mean and possibilistic variance. Using the gradually tolerant constraint method proposed in this paper, we give a numerical example to illustrate the efficiency of the proposed model and method. The proposed method in this paper has improvements in two aspects. One is that our method offers several satisfactory solutions for the same model compared with the linear weighted method of Chang (2009), which offers only one satisfactory solution according to the investor's risk preference degree. The other is that the effective frontier of our method is more efficient than that of the method proposed by Markowitz (1987) and Zhang (2007).