Robust multi-period portfolio model based on prospect theory and ALMV-PSO algorithm

The studies of behavioral finance show that the cognitive bias plays an important role in investors’ decision-making process. In this paper, we propose a new robust multi-period model for portfolio optimization that considers investors’ behavioral factors by introducing dynamically updated loss aversion parameters as well as a dynamic value function based on prospect theory. We also develop a novel particle swarm optimization (PSO) algorithm with an aging leader and multi-frequency vibration to solve the portfolio model. Furthermore, a two-stage initialization strategy and an improved stochastic ranking approach are incorporated in the proposed algorithm. The two-stage initialization strategy guarantees that all of initial particles are feasible, and the improved stochastic ranking approach handles the constrained portfolio problem. We illustrate the robust model with real market data and show its effectiveness based on the performance of the proposed PSO algorithm.