Solving Multi-Objective Portfolio Optimization Problem Based on MOEA/D
Pengxiang Zhao, Shang Gao, Nachuan Yang
Abstract
The Markowitz mean-variance (MV) model is the basis of modern portfolio theory, the goal of which is to choose an optimal set of weights with the maximum expected return for a given level of risk. However, the MV model assumes that returns are normally distributed, which ignores the asymmetry of returns in real life. The mean-variance-skewness (MVS) portfolio framework introduces the skewness of the return as an extension of the classical MV model. Because of the increasing conflicting objective functions, the multi-objective portfolio optimization problem is more difficult to solve. This paper investigates the use of a multi-objective evolutionary algorithm based on decomposition (MOEA/D) for solving the portfolio optimization problem in the stock market. The results show that MOEA/D can produce Pareto fronts efficiently with three objectives to be optimized.