Fuzzy chance-constrained portfolio selection

This paper selects the portfolio with fuzzy returns by criteria of chance represented by credibility measure. In the paper, two types of credibility-based portfolio selection model are provided according to two types of chance criteria. By one chance criterion, the objective is to maximize the inves...

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Bibliographic Details
Published inApplied mathematics and computation Vol. 177; no. 2; pp. 500 - 507
Main Author Huang, Xiaoxia
Format Journal Article
LanguageEnglish
Published New York, NY Elsevier Inc 15.06.2006
Elsevier
Subjects
Algorithmics. Computability. Computer arithmetics
Applied sciences
Computer science; control theory; systems
Exact sciences and technology
Fuzzy chance-constrained programming
Fuzzy portfolio selection
Genetic algorithm
Mathematical programming
Operational research and scientific management
Operational research. Management science
Portfolio theory
Theoretical computing
Fuzzy chance-constrained programming
Genetic algorithm
Fuzzy portfolio selection
Membership function
Portfolio selection
Algorithm performance
Applied mathematics
Model selection
Selection method
Hybrid model
Fuzzy programming
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Summary:This paper selects the portfolio with fuzzy returns by criteria of chance represented by credibility measure. In the paper, two types of credibility-based portfolio selection model are provided according to two types of chance criteria. By one chance criterion, the objective is to maximize the investor’s return at a given threshold confidence level; by another chance criterion, the objective is to maximize the credibility of achieving a specified return level subject to the constraints. To provide a general method to solve the new models, a hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is designed in the paper. Two numerical examples with security returns taking different types of membership function are also given to illustrate the modelling idea of the paper and to demonstrate the effectiveness of the proposed algorithm.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2005.11.027