An iterative method for solving a bi-objective constrained portfolio optimization problem

作     者：Bezoui, Madani Moulai, Mustapha Bounceur, Ahcene Euler, Reinhardt 

作者机构：Univ Mhamed Bougara Boumerdes Boumerdes 35000 Algeria USTHB LaROMad Fac Math BP 32 Algiers 16111 Algeria UBO UMR CNRS 6285 Lab STICC Brest France 

出 版 物：《COMPUTATIONAL OPTIMIZATION AND APPLICATIONS》 (计算优化及其应用)

年 卷 期：2019年第72卷第2期

页      面：479-498页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070104[理学-应用数学] 0701[理学-数学] 

主　　题：Cardinality and quantity constraints Cardinality portfolio selection Bi-objective programming Mixed integer programming Steepest descent method Pascoletti-Serafini method 

摘      要：In this work, we consider the problem of portfolio optimization under cardinality and quantity constraints. We use the standard model of mean-variance in its bi-objective form which is presented here as a bi-objective quadratic programming problem under cardinality and quantity constraints. This problem is NP-hard, which is why the majority of methods proposed in the literature use metaheuristics for its resolution. In this paper, we propose an iterative method for solving constrained portfolio optimization problems. Experiments are performed with major market indices, such as the Hang Seng, DAX, FTSE, S&P 100, Nikkei, S&P 500 and Nasdaq using real-world datasets involving up to 2196 assets. Comparisons with two exact methods and a metaheuristic are performed. These results show that the new method allows to find efficient portfolio fronts in reasonable time.