Abstract

AbstractWe first deduce the variance formulas of normal, triangular and trapezoidal fuzzy variables in credibility theory. Then two classes of fuzzy portfolio selection models are built based on credibility measure, the expected value and variance of a fuzzy variable. To solve the proposed models, a genetic algorithm is employed. Finally, two numerical examples are provided for the proposed portfolio selection models to test the designed algorithm.KeywordsGenetic AlgorithmReturn RatePortfolio SelectionFuzzy VariablePossibility DistributionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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