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Open Access

A New Method of Portfolio Optimization Under Cumulative Prospect Theory

Chao Gong( ), Chunhui Xu, Masakazu Ando, Xiangming Xi
Department of Risk Science in Finance and Management, Chiba Institute of Technology, Chiba 275-0016, Japan.
Department of Automation, Tsinghua University, Beijing 100084, China and joined Huawei Technologies Co. Ltd. since 2016.
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Abstract

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.

Keywords

portfolio choicecumulative prospect theorybootstrap methodadaptive real-coded genetic algorithm

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Tsinghua Science and Technology
Volume 23 Issue 1,
February 2018
Pages 75-86
DOI: 10.26599/TST.2018.9010057
Cite this article:
Gong C, Xu C, Ando M, et al. A New Method of Portfolio Optimization Under Cumulative Prospect Theory. Tsinghua Science and Technology, 2018, 23(1): 75-86. https://doi.org/10.26599/TST.2018.9010057

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Received: 13 November 2016
Accepted: 23 December 2016
Published: 15 February 2018
© The authors 2018
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