A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns |
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Authors: | Xiang Li Yang Zhang Hau-San Wong Zhongfeng Qin |
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Institution: | aDepartment of Mathematical Sciences, Tsinghua University, Beijing 100084, China;bDepartment of Computer Science, City University of Hong Kong, Hong Kong, China;cSchool of Economics and Management, Beihang University, Beijing 100191, China |
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Abstract: | Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean–variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems. |
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Keywords: | Portfolio selection Fuzzy variable Simulated annealing Neural network Fuzzy simulation Credibility measure |
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