AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Complex System Modeling and Simulation Article
PDF (1.6 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Iϵ+LGEA: A Learning-Guided Evolutionary Algorithm Based on Iϵ+ Indicator for Portfolio Optimization

School of Computer Science, Wuhan University, Wuhan 430072, China
Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Show Author Information

Abstract

Portfolio optimization is a classical and important problem in the field of asset management, which aims to achieve a trade-off between profit and risk. Previous portfolio optimization models use traditional risk measurements such as variance, which symmetrically delineate both positive and negative sides and are not practical and stable. In this paper, a new model with cardinality constraints is first proposed, in which the idiosyncratic volatility factor is used to replace traditional risk measurements and can capture the risks of the portfolio in a more accurate way. The new model has practical constraints which involve the sparsity and irregularity of variables and make it challenging to be solved by traditional Multi-Objective Evolutionary Algorithms (MOEAs). To solve the model, a Learning-Guided Evolutionary Algorithm based on Iϵ+ indicator ( Iϵ+LGEA) is developed. In Iϵ+LGEA, the Iϵ+ indicator is incorporated into the initialization and genetic operators to guarantee the sparsity of solutions and can help improve the convergence of the algorithm. And a new constraint-handling method based on Iϵ+ indicator is also adopted to ensure the feasibility of solutions. The experimental results on five portfolio trading datasets including up to 1226 assets show that Iϵ+LGEA outperforms some state-of-the-art MOEAs in most cases.

Keywords

evolutionary algorithmportfolio optimizationsparse solution spaceindicator-based Evolutionary Algorithm (EA)

References

[1]

T. Hendershott, X. Zhang, J. L. Zhao, and Z. Zheng, FinTech as a game changer: Overview of research frontiers, Inf. Syst. Res., vol. 32, no. 1, pp. 1–17, 2021.
Crossref Google Scholar
[2]

B. S. Alhashel, F. W. Almudhaf, and J. A. Hansz, Can technical analysis generate superior returns in securitized property markets? Evidence from East Asia markets, Pac. Basin Finance J., vol. 47, pp. 92–108, 2018.
Crossref Google Scholar
[3]

O. Ertenlice and C. B. Kalayci, A survey of swarm intelligence for portfolio optimization: Algorithms and applications, Swarm Evol. Comput., vol. 39, pp. 36–52, 2018.
Crossref Google Scholar
[4]

I. B. Salehpoor and S. Molla-Alizadeh-Zavardehi, A constrained portfolio selection model at considering risk-adjusted measure by using hybrid meta-heuristic algorithms, Appl. Soft Comput., vol. 75, pp. 233–253, 2019.
Crossref Google Scholar
[5]

L. Wang, Z. Pan, and J. Wang, A review of reinforcement learning based intelligent optimization for manufacturing scheduling, Complex System Modeling and Simulation, vol. 1, no. 4, pp. 257–270, 2021.
Crossref Google Scholar
[6]

F. Wang, X. Wang, and S. Sun, A reinforcement learning level-based particle swarm optimization algorithm for large-scale optimization, Inf. Sci., vol. 602, pp. 298–312, 2022.
Crossref Google Scholar
[7]

M. Sun, C. Sun, X. Li, G. Zhang, and F. Akhtar, Large-scale expensive optimization with a switching strategy, Complex System Modeling and Simulation, vol. 2, no. 3, pp. 253–263, 2022.
Crossref Google Scholar
[8]

W. Gong, Z. Liao, X. Mi, L. Wang, and Y. Guo, Nonlinear equations solving with intelligent optimization algorithms: A survey, Complex System Modeling and Simulation, vol. 1, no. 1, pp. 15–32, 2021.
Crossref Google Scholar
[9]

X. Yan, H. Zuo, C. Hu, W. Gong, and V. S. Sheng, Load optimization scheduling of chip mounter based on hybrid adaptive optimization algorithm, Complex System Modeling and Simulation, vol. 3, no. 1, pp. 1–11, 2023.
Crossref Google Scholar
[10]

C. Hu, R. Qiao, Z. Zhang, X. Yan, and M. Li, Dynamic scheduling algorithm based on evolutionary reinforcement learning for sudden contaminant events under uncertain environment, Complex System Modeling and Simulation, vol. 2, no. 3, pp. 213–223, 2022.
Crossref Google Scholar
[11]

M. Kaucic, M. Moradi, and M. Mirzazadeh, Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures, Financial Innov., vol. 5, no. 1, p. 26, 2019.
Crossref Google Scholar
[12]
Q. Zhang, H. Li, D. Maringer, and E. Tsang, MOEA/D with NBI-style Tchebycheff approach for portfolio management, in Proc. IEEE Congress on Evolutionary Computation, Barcelona, Spain, 2010, pp. 1–8.
Crossref
[13]

X. Ma, J. Chen, Y. Sun, and Z. Zhu, Assistant reference point guided evolutionary algorithm for many-objective fuzzy portfolio selection, Swarm Evol. Comput., vol. 62, p. 100862, 2021.
Crossref Google Scholar
[14]
E. Zitzler and S. Künzli, Indicator-based selection in multiobjective search, in Proc. 8th International Conference on Parallel Problem Solving from Nature, Birmingham, UK, 2004, pp. 832–842.
Crossref
[15]
E. F. Fama and K. R. French, Common risk factors in the returns on stocks and bonds, in The Fama Portfolio: Selected Papers of Eugene F. Fama, J. H. Cochrane and T. J. Moskowitz, eds. Chicago, IL, USA: University of Chicago Press, 2017, pp. 392–449.
Crossref
[16]

K. Deb and R. B. Agrawal, Simulated binary crossover for continuous search space, Complex Systems, vol. 9, no. 2, pp. 115–148, 1995.
Google Scholar
[17]

K. Deb and M. Goyal, A combined genetic adaptive search (GeneAS) for engineering design, Journal of Computer Science and Informatics, vol. 26, pp. 30–45, 1996.
Google Scholar
[18]

Y. Tian, R. Cheng, X. Zhang, and Y. Jin, PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum], IEEE Comput. Intell. Mag., vol. 12, no. 4, pp. 73–87, 2017.
Crossref Google Scholar
[19]

Y. Tian, X. Zhang, C. Wang, and Y. Jin, An evolutionary algorithm for large-scale sparse multiobjective optimization problems, IEEE Trans. Evol. Comput., vol. 24, no. 2, pp. 380–393, 2019.
Crossref Google Scholar
[20]
F. Streichert, H. Ulmer, and A. Zell, Evaluating a hybrid encoding and three crossover operators on the constrained portfolio selection problem, in Proc. 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753), Portland, OR, USA, 2004, pp. 932–939.
Complex System Modeling and Simulation
Volume 3 Issue 3,
September 2023
Pages 191-201
DOI: 10.23919/CSMS.2023.0012
Cite this article:
Wang F, Huang Z, Wang S. Iϵ+LGEA: A Learning-Guided Evolutionary Algorithm Based on Iϵ+ Indicator for Portfolio Optimization. Complex System Modeling and Simulation, 2023, 3(3): 191-201. https://doi.org/10.23919/CSMS.2023.0012

640

Views

64

Downloads

3

Crossref

4

Scopus

Altmetrics
Received: 17 April 2023
Revised: 27 April 2023
Accepted: 09 May 2023
Published: 02 August 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return