Reliable Portfolio Selection Problem in Fuzzy Environment: An mλ Measure Based Approach
Mathematics and Physics Department, Beijing Institute of Petrochemical Technology, Beijing 102617, China
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Author to whom correspondence should be addressed.
Academic Editor: Oscar Castillo
Received: 16 February 2017 / Revised: 31 March 2017 / Accepted: 13 April 2017 / Published: 18 April 2017
This paper investigates a fuzzy portfolio selection problem with guaranteed reliability, in which the fuzzy variables are used to capture the uncertain returns of different securities. To effectively handle the fuzziness in a mathematical way, a new expected value operator and variance of fuzzy variables are defined based on the
measure that is a linear combination of the possibility measure and necessity measure to balance the pessimism and optimism in the decision-making process. To formulate the reliable portfolio selection problem, we particularly adopt the expected total return and standard variance of the total return to evaluate the reliability of the investment strategies, producing three risk-guaranteed reliable portfolio selection models. To solve the proposed models, an effective genetic algorithm is designed to generate the approximate optimal solution to the considered problem. Finally, the numerical examples are given to show the performance of the proposed models and algorithm.
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MDPI and ACS Style
Feng, Y.; Wang, L.; Liu, X. Reliable Portfolio Selection Problem in Fuzzy Environment: An mλ Measure Based Approach. Algorithms 2017, 10, 43.
Feng Y, Wang L, Liu X. Reliable Portfolio Selection Problem in Fuzzy Environment: An mλ Measure Based Approach. Algorithms. 2017; 10(2):43.
Feng, Yuan; Wang, Li; Liu, Xinhong. 2017. "Reliable Portfolio Selection Problem in Fuzzy Environment: An mλ Measure Based Approach." Algorithms 10, no. 2: 43.
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