Journal
SOFT COMPUTING
Volume 25, Issue 8, Pages 6445-6467Publisher
SPRINGER
DOI: 10.1007/s00500-021-05638-z
Keywords
Multiperiod portfolio selection; Credibility measure; Multiobjective optimization; Value at risk; Bat algorithm
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Funding
- Postgraduate Academic Newcomer Program of Capital University of Economics and Business of China [2021XSXR05]
- National Natural Science Foundation of China [72071134]
- Project of High-level Teachers in Beijing Municipal Universities in the Period of 13th Five-year Plan [CITTCD20190338]
- Humanity and Social Science Foundation of Ministry of Education of China [19YJAZH005]
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This paper presents a credibility theory-based model for a multiperiod multiobjective fuzzy portfolio selection problem, introducing a credibilistic multiobjective mean-VaR model and an improved multiobjective bat algorithm IMBA to enhance convergence speed and solution quality, and demonstrating its superiority through comparative experiments.
This paper deals with a multiperiod multiobjective fuzzy portfolio selectiossn problem based on credibility theory. A credibilistic multiobjective mean-VaR model is formulated for the multiperiod portfolio selection problem, whereby the return is quantified by the credibilistic mean and the risk is measured by the credibilistic VaR. We also consider liquidity, cardinality, and upper and lower bound constraints to obtain a more realistic model. Furthermore, to solve the proposed model efficiently, an improved multiobjective bat algorithm termed IMBA is designed, in which three new strategies, i.e., the global best solution selection strategy, candidate solution generation strategy, and competitive learning strategy, are proposed to increase the convergence speed and improve the solution quality. Finally, comparative experiments are presented to show the applicability and superiority of the proposed approaches from two aspects. First, the designed IMBA is compared with seven typical algorithms, i.e., multiobjective particle swarm optimization, multiobjective artificial bee colony, multiobjective firefly algorithm, multiobjective differential evolution, multiobjective bat, the non-dominated sorting genetic algorithm (NSGA-II) and strength pareto evolutionary algorithm 2 (SPEA2), on a number of benchmark test problems. Second, the applicability of the proposed model to practical applications of portfolio selection is given under different circumstances.
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