Portfolio optimization in single-period under cumulative prospect theory using genetic algorithms and bootstrap method

Abstract

Cumulative prospect theory (CPT) has become one of the most popular approaches for evaluating the behavior of decision makers under conditions of uncertainty. Substantial experimental evidence suggests that human behavior may significantly deviate from the traditional expected utility maximization framework when faced with uncertainty. The problem of portfolio selection should be revised when the investor's preference is for CPT instead of expected utility theory (EUT). However, because of the complexity of the CPT function, little research has investigated the portfolio choice problem based on CPT. In this paper, we present an approach to solve the portfolio optimization in single-period under cumulative prospect theory, based upon the coupling of genetic algorithms with bootstrap method. The computational experiments show that the behavior characteristics of CPT investors when they faced the portfolio composed of risky assets by using the method we proposed. Finally, these phenomena are discussed in this paper.