A New Method of Portfolio Optimization Under Cumulative Prospect Theory

• 来源期刊: 清华大学学报自然科学版（英文版）
• 年，卷（期）: 2018,（1）
• 页码范围: 75-86
• 页数: 12页
• 语种: 英文
• DOI: 10.26599/TST.2018.9010057

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.