Multiobjective Evolutionary Algorithms for Financial Portfolio Design

Abstract

Efficient portfolio design is a principal challenge in modern computational finance. Optimization based on Markowitz two-objective mean-variance approach is computationally expensive for real financial world. Practical portfolio design introduces further complexity as it requires the optimization of multiple return and risk measures. Some of these measures are nonlinear and nonconvex. The problem of portfolio design is a standard problem in financial world and has received a lot of attention. Three well known multi-objective evolutionary algorithms i.e. Pareto envelope-based selection algorithm , Micro Genetic algorithm and Multiobjective particle swarm optimization has been applied for solving the bi-objective portfolio optimization problem which simultaneously maximize the return measures and minimize the risk measures. Performance comparison carried out by performing different numerical experiments. The approach has been tested on real-life portfolio with many assets. The results show that MOPSO outperforms the existing method for the considered test cases.