A New Method of Portfolio Optimization Under Cumulative Prospect Theory

作     者：Chao Gong Chunhui Xu Masakazu Ando Xiangming Xi 

作者机构：Department of Risk Science in Finance and Management Chiba Institute of Technology Chiba 275-0016 Japan Department of Automation Tsinghua University Beijing 100084 China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2018年第23卷第1期

页      面：75-86页

核心收录：

学科分类：12[管理学] 02[经济学] 0202[经济学-应用经济学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 020204[经济学-金融学（含∶保险学）] 070104[理学-应用数学] 0701[理学-数学] 

主　　题：portfolio choice cumulative prospect theory bootstrap method adaptive real-coded genetic algorithm 

摘      要：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.