Hybrid bi-objective portfolio optimization with pre-selection strategy

Abstract

Classical Markowitz mean-variance model is widely used for portfolio assets selection and allocation, which aims at simultaneously maximizing the expected return of the portfolio and minimizing portfolio variance. Many numerical approaches and metaheuristic algorithms have been proposed to effectively solve this portfolio optimization problem under an ideal condition. However, introducing various realistic constraints inadvertently leads to a non-convex search space, which has hindered the application of many classic, exact algorithms such as quadratic programming (QP). The increasing size of available assets and complex constraints has made the effectiveness of metaheuristic algorithms deteriorated. This paper proposes a hybrid bi-objective algorithm combining with the respective advantages of local search algorithm, evolutionary algorithm and QP with a pre-selection strategy. The algorithm first down select the assets that have greater contribution to the Pareto frontier by applying the pre-selection strategy. Then local search and evolutionary algorithm combined with QP are employed to fully exploit the useful assets combination modes to lead the search process toward the frontier direction quickly. The experimental study demonstrates that the proposed hybrid approach can obtain faster and better convergence compared with eight state-of the-art multi-objective evolutionary algorithms. The results also show that the proposed method with the pre-selection strategy always displays a closer proximity to the Pareto frontier compared with k-means strategy.