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Reliable Portfolio Selection Problem in Fuzzy Environment: An mλ Measure Based Approach

by Yuan Feng 1, Li Wang 2,* and Xinhong Liu 1
1
Mathematics and Physics Department, Beijing Institute of Petrochemical Technology, Beijing 102617, China
2
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Academic Editor: Oscar Castillo
Algorithms 2017, 10(2), 43; https://doi.org/10.3390/a10020043
Received: 16 February 2017 / Revised: 31 March 2017 / Accepted: 13 April 2017 / Published: 18 April 2017
(This article belongs to the Special Issue Extensions to Type-1 Fuzzy Logic: Theory, Algorithms and Applications)
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 m λ 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. View Full-Text
Keywords: portfolio selection problem; mλ measure; expected value operator; genetic algorithm portfolio selection problem; mλ measure; expected value operator; genetic algorithm
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Feng, Y.; Wang, L.; Liu, X. Reliable Portfolio Selection Problem in Fuzzy Environment: An mλ Measure Based Approach. Algorithms 2017, 10, 43.

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