Prospect Theory-Based Portfolio Selection Using Multiple Fuzzy Reference Intervals
本研究提出一个基于前景理论的模糊模型，并开发了自适应协同粒子群优化算法，在高度不确定的市场中为投资者提供指导，通过真实市场数据验证了模型有效性。
Portfolio selection stands as a paramount concern within the realm of decision-making and management engineering. However, owing to the inherent intricacies of capital markets and the presence of irrational investor behaviors, the attainment of predefined investment objectives by investors remains a formidable challenge. In order to comprehensively depict investor behavior patterns and to provide investment guidance in highly uncertain and volatile markets, this study introduces a novel fuzzy model for representing prospect theory and based on this, develops a novel portfolio selection optimization framework. In addition, a new particle swarm optimization consists of adaptive and cooperative strategy is proposed to find the optimal solution of this model. The effectiveness of this model is validated through two case study utilizing real-market data, while the efficiency of the solution algorithm is confirmed through a test fitness functions-based case study.