4.7 Article

Multi-criteria fuzzy portfolio selection based on three-way decisions and cumulative prospect theory

期刊

APPLIED SOFT COMPUTING
卷 134, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.asoc.2023.110033

关键词

Three-way decisions; Cumulative prospect theory; Outranking relations; Fuzzy multi-period portfolio selection; Particle swarm optimization

向作者/读者索取更多资源

In this study, a novel three-way decisions model is proposed based on cumulative prospect theory and outranking relations, which introduces a boundary region and reduces decision risk. Three strategies are proposed to design the model by constructing an outranked set for each alternative and a hybrid multi-criteria decision-making matrix.
Portfolio selection is one of the hottest issue in decision-making and management engineering. But due to the capital market natural complexity and investors' irrational behaviors, it is not easy for investors to achieve their predefined goals. In this study, we propose a novel three-way decisions model based on cumulative prospect theory and outranking relations. Compared with traditional two-way decisions, the introduction of a boundary region into the three-way decisions theory makes it possible to reduce decision risk. By constructing an outranked set for each alternative and a hybrid multi-criteria decision-making matrix, three strategies are proposed by us to design the three-way decisions model. In order to test the effectiveness of the proposed model, we introduce it into a fuzzy multi-period portfolio selection case and design an improved particle swarm optimization as the solution algorithm. Finally, the effectiveness of the algorithm is validated by some test functions. And an experiment based on real market data validates the proposed multi-period portfolio selection model outperforms other compared models in terms of return, risk and risk-adjusted criteria.(c) 2023 Elsevier B.V. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据