A New Method of Portfolio Optimization Under Cumulative Prospect Theory

作者：Gong, Chao^*; Xu, Chunhui; Ando, Masakazu; Xi, Xiangming

来源：Tsinghua Science and Technology, 2018, 23(1): 75-86.

DOI：10.26599/TST.2018.9010057

• portfolio choice
• cumulative prospect theory
• bootstrap method
• adaptive real-coded genetic algorithm

In this paper, the portfolio selection problem under Cumulative Prospect Theory (CPT) is investigated and a model of portfolio optimization is presented. This model is solved by coupling scenario generation techniques with a genetic algorithm. Moreover, an Adaptive Real-Coded Genetic Algorithm (ARCGA) is developed to find the optimal solution for the proposed model. Computational results show that the proposed method solves the portfolio selection model and that ARCGA is an effective and stable algorithm. We compare the portfolio choices of CPT investors based on various bootstrap techniques for scenario generation and empirically examine the effect of reference points on investment behavior.

• 出版日期2018-2
• 单位清华大学