Multi-period portfolio optimization under possibility measures |
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| Institution: | 1. School of Management, Zhejiang University, Hangzhou, China;2. School of Business Administration, South China University of Technology, Guangzhou, China;1. Management School, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil;2. Universidade Federal de Pernambuco, CDSID – Center for Decision System and Information Development, Recife, PE, Brazil;1. School of Finance, Yunnan University of Finance and Economics, Kunming 650221, PR China;2. Business School, Sichuan University, Chengdu 610064, PR China;3. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China;1. Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran;2. Department of Industrial Engineering, Tarbiat Modares University, 1411713114 Tehran, Iran;1. Department of Quantitative Methods, School of Business, King Faisil University, P.B. 400 Al-Hassa 31982, Saudi Arabia;2. Department of Mathematics, College of Arts and Sciences, Northen Border University, P.B. 840 Rafha 91911, Saudi Arabia |
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| Abstract: | A single-period portfolio selection theory provides optimal tradeoff between the mean and the variance of the portfolio return for a future period. However, in a real investment process, the investment horizon is usually multi-period and the investor needs to rebalance his position from time to time. Hence it is natural to extend the single-period fuzzy portfolio selection to the multi-period case based on the possibility theory. In this paper, we propose the possibilistic expected value and variance for the terminal wealth with fuzzy forms after T periods by using the central value operator. Classes of multi-period possibilistic mean-variance models are formulated originally under the assumption that the proceeds of risky assets are fuzzy variables. Besides, we apply a particle swarm optimization algorithm to solve the proposed multi-period fuzzy portfolio selection models. A numerical example is given to illustrate the performance of the proposed models and algorithm. |
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