Abstract
Portfolio selection is an important problem in the practice and theory of finance. This paper uses a five-objective (including mean, variance, skewness, kurtosis, and entropy) model to replace the classical Markowitz mean-variance model for finding better portfolio selection. To obtain a more accurate estimation of risk asset returns, a fuzzy number variable, instead of a random variable, based on the acknowledge of experts is used to estimate the return of a risk asset. A new reference point-based evolutionary algorithm (NRPEA) is proposed to obtain well-convergence and well-distributed solutions for the many-objective optimization problems. In NRPEA, the auxiliary reference points are generated and selected to guide the population evolution. Experiment results on six well-known data sets demonstrate the effectiveness and efficiency of NRPEA in the comparison with other three state-of-the-art many-objective optimization algorithms.
This work was supported in part by the National Natural Science Foundation of China, under Grants 61976143, 61471246, 61603259, and 61871272, Guangdong Special Support Program of Top-notch Young Professionals, under Grant 2014TQ01X273, Shenzhen Fundamental Research Program, under Grant JCYJ20170302154328155, Scientific Research Foundation of Shenzhen University for Newly-introduced Teachers, under Grant 2019048, and Zhejiang Lab’s International Talent Fund for Young Professionals. This work was supported by the National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen 518060, China and Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), Shenzhen University, Shenzhen 518060, China.
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Chen, J., Ma, X., Sun, Y., Zhu, Z. (2020). A Reference Point-Based Evolutionary Algorithm for Many-Objective Fuzzy Portfolio Selection. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_10
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DOI: https://doi.org/10.1007/978-981-15-3425-6_10
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