Random fuzzy mean-absolute deviation models for portfolio optimization problem with hybrid uncertainty
作者:Qin, ZF(Qin, Zhongfeng)
APPLIED SOFT COMPUTING
DOI:10.1016/j.asoc.2016.06.017
出版年:JUL 2017
Absolutedeviationis a commonly used risk measure, which has attracted more attentions inportfoliooptimization. The existingmean-absolutedeviationmodelsare devoted to either stochasticportfoliooptimizationorfuzzyone. However, practical investment decision problems often involve the mixture of randomness and fuzziness such as stochastic returnswithfuzzyinformation. Thus it is necessary to modelportfolioselectionproblemin such ahybriduncertain environment. In this paper, we employrandomfuzzyvariables to describe the stochastic return on individual securitywithambiguous information. We first define the absolutedeviationofrandomfuzzyvariable and then employ it as risk measure to formulatemean-absolutedeviationportfoliooptimizationmodels. To find the optimalportfolio, we designrandomfuzzysimulation and simulation-based genetic algorithm to solve the proposedmodels. Finally, a numerical exampleforsynthetic data is presented to illustrate the validity of the method. (C) 2016 Elservier B.V. All rights reserved.
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研究方向:Computer Science
Web of Science 类别:Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications
