An MOEA/D-Based Strategy for Multi-Objective Portfolio Optimization in
The Markowitz mean-variance (MV) model has been a fundamental framework
in modern portfolio investment theory, the goal of which is to choose an
optimal set of weights that maximizes the expected return for a given
level of risk. However, the MV model assumes that the returns in capital
markets are normally distributed, which ignores the asymmetry of returns
in real life. Recently, the mean-variance-skewness (MVS) portfolio
framework has attracted more attention as it introduces the skewness of
returns as an extension to the classical MV model. In this paper, based
on the MVS portfolio framework, a novel approach is proposed to solve
portfolio optimization problems in capital markets through using
Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D).
Finally, several simulations are provided to illustrate the eﬀectiveness
of the proposed approach.