TY - GEN
T1 - A Fast Converging Evolutionary Algorithm for Constrained Multiobjective Portfolio Optimization
AU - Chen, Yi
AU - Singh, Hemant Kumar
AU - Zhou, Aimin
AU - Ray, Tapabrata
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Portfolio optimization is a well-known problem in the domain of finance with reports dating as far back as 1952. It aims to find a trade-off between risk and expected return for the investors, who want to invest finite capital in a set of available assets. Furthermore, constrained portfolio optimization problems are of particular interest in real-world scenarios where practical aspects such as cardinality (among others) are considered. Both mathematical programming and meta-heuristic approaches have been employed for handling this problem. Evolutionary Algorithms (EAs) are often preferred for constrained portfolio optimization problems involving non-convex models. In this paper, we propose an EA with a tailored variable representation and initialization scheme to solve the problem. The proposed approach uses a short variable vector, regardless of the size of the assets available to choose from, making it more scalable. The solutions generated do not need to be repaired and satisfy some of the constraints implicitly rather than requiring a dedicated technique. Empirical experiments on 20 instances with the numbers of assets, ranging from 31 to 2235, indicate that the proposed components can significantly expedite the convergence of the algorithm towards the Pareto front.
AB - Portfolio optimization is a well-known problem in the domain of finance with reports dating as far back as 1952. It aims to find a trade-off between risk and expected return for the investors, who want to invest finite capital in a set of available assets. Furthermore, constrained portfolio optimization problems are of particular interest in real-world scenarios where practical aspects such as cardinality (among others) are considered. Both mathematical programming and meta-heuristic approaches have been employed for handling this problem. Evolutionary Algorithms (EAs) are often preferred for constrained portfolio optimization problems involving non-convex models. In this paper, we propose an EA with a tailored variable representation and initialization scheme to solve the problem. The proposed approach uses a short variable vector, regardless of the size of the assets available to choose from, making it more scalable. The solutions generated do not need to be repaired and satisfy some of the constraints implicitly rather than requiring a dedicated technique. Empirical experiments on 20 instances with the numbers of assets, ranging from 31 to 2235, indicate that the proposed components can significantly expedite the convergence of the algorithm towards the Pareto front.
KW - Constrained portfolio optimization
KW - Multi-objective portfolio optimization
KW - Representation for evolutionary algorithm
UR - https://www.scopus.com/pages/publications/85107292820
U2 - 10.1007/978-3-030-72062-9_23
DO - 10.1007/978-3-030-72062-9_23
M3 - 会议稿件
AN - SCOPUS:85107292820
SN - 9783030720612
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 283
EP - 295
BT - Evolutionary Multi-Criterion Optimization - 11th International Conference, EMO 2021, Proceedings
A2 - Ishibuchi, Hisao
A2 - Zhang, Qingfu
A2 - Cheng, Ran
A2 - Li, Ke
A2 - Li, Hui
A2 - Wang, Handing
A2 - Zhou, Aimin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021
Y2 - 28 March 2021 through 31 March 2021
ER -