Modeling of linear uncertain portfolio selection with uncertain constraint and risk index

• Published in: Fuzzy optimization and decision making Vol. 23; no. 3; pp. 469 - 496
• Main Authors: Guo, Weiwei, Zhang, Wei-Guo, Gong, Zaiwu
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Calculus of Variations and Optimal Control; Optimization; Constraint modelling; Environment models; Investment policy; Linear programming; Mathematical Logic and Foundations; Mathematics; Mathematics and Statistics; Maximization; Operations Research/Decision Theory; Optimization; Parameter sensitivity; Probability Theory and Stochastic Processes; Random variables; Risk analysis; Securities markets; Uncertainty analysis; Portfolio selection; Uncertain variable; Risk index; Uncertain constraint
• ISSN: 1568-4539; 1573-2908
• DOI: 10.1007/s10700-024-09429-7

Since securities market is subject to a great deal of uncertainty and complexity, the return of securities cannot be accurately estimated by historical data. In this case, it must use experts’ knowledge and judgment. Therefore, we investigate portfolio selection problems in such uncertain environments. First, this paper regards the rate of return on security as an uncertain variable which obeys linear uncertainty distribution, and then provides the analytical expressions of the corresponding risk, return and risk index in the uncertain portfolio selection environment. Afterwards, we construct three types uncertain portfolio selection models with uncertain constraint, namely, the minimizing risk, the maximizing return and the maximizing belief degree. Meanwhile, in order to more intuitively reflect the investor’s sense of loss, three types uncertain portfolio selection models considering both uncertain constraint and risk index are also constructed. These models are transformed into corresponding deterministic models. Finally, through an example analysis, this paper obtains the portfolio selection strategies under different objectives, compares the results under different models, and analyzes the sensitivity of the parameters.