An uncertain bi-objective mean-entropy model for portfolio selection with realistic factors

被引:1
作者
Lv, Linjing [1 ]
Zhang, Bo [2 ]
Li, Hui [3 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Econ, Wuhan 430074, Peoples R China
[2] Zhongnan Univ Econ & Law, Sch Stat & Math, Wuhan 430073, Peoples R China
[3] Hubei Univ Educ, Bigdata Modeling & Intelligent Comp Res Inst, Sch Math & Stat, Wuhan 430205, Peoples R China
基金
中国国家自然科学基金;
关键词
Uncertainty theory; Portfolio selection; Uncertain modeling; Genetic algorithm; Cross-entropy; OPTIMIZATION; ALGORITHM; INSURANCE;
D O I
10.1016/j.matcom.2024.05.013
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In the imprecise investment environment, there are many indeterminate factors impacting security returns. This paper introduces a portfolio optimization problem where cross-entropy is utilized to control portfolio risk within the framework of uncertainty theory and presents an uncertain bi-objective mean-entropy portfolio selection model. To be more realistic, some realistic factors such as minimum transaction lots, dividend factors and tax factors are also considered. By introducing a risk preference coefficient, the bi-objective model is converted into a single-objective model and some equivalents are discussed. Additionally, a hybrid intelligent algorithm integrating a genetic algorithm with uncertain estimation is designed to solve the proposed model. Finally, a case study is executed to confirm the practicability of the model and the performance of the algorithm, and an empirical analysis based on the proposed model and the uncertain mean-variance model is developed to illustrate the advantage of the uncertain mean-entropy model in practical investment.
引用
收藏
页码:216 / 231
页数:16
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