Volume 8, Issue 2 (6-2026)
sjamao 2026, 8(2): 1-9 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Esmaeili A, Hashemi S S. Robust Multi-Objective Portfolio Optimization with ENS-NSGA-II Under Real-World Constraints. sjamao 2026; 8 (2) :1-9
URL: http://sjamao.srpub.org/article-7-288-en.html
URL: http://sjamao.srpub.org/article-7-288-en.html
1- Department of Accounting, Khomein Branch, Islamic Azad University, Khomein, Iran.
2- Professor, Department of Accounting, Khomein Branch, Islamic Azad University, Khomein, Iran. ,shashemi@gmail.com
2- Professor, Department of Accounting, Khomein Branch, Islamic Azad University, Khomein, Iran. ,
Abstract: (5 Views)
This study explores portfolio optimization by employing the Enhanced Non-dominated Sorting Genetic Algorithm II (ENS-NSGA-II), a robust multi-objective evolutionary algorithm, alongside classical optimization methods. Utilizing data from 135 companies listed on the Tehran Stock Exchange over the period 2013–2024, an extended Markowitz framework was constructed based on return and semi-variance as key performance criteria. To better reflect market realities, several practical investment constraints were incorporated, transforming the problem into a multi-objective optimization task. The ENS-NSGA-II algorithm, equipped with adaptive mutation control and improved diversity preservation mechanisms, significantly outperformed classical models by generating portfolios with superior return-risk trade-offs under nonlinear and volatile market conditions. Furthermore, the algorithm exhibited strong convergence behavior and stability across multiple independent runs and time intervals. These findings confirm that ENS-NSGA-II offers a powerful and flexible approach for constructing efficient investment portfolios in uncertain financial environments, providing investors with a reliable decision-making tool that adapts to dynamic market complexities.
Type of Study: Research |
Subject:
Accounting
Received: 2026/03/17 | Revised: 2026/04/15 | Accepted: 2026/05/13 | Published: 2026/06/15
Received: 2026/03/17 | Revised: 2026/04/15 | Accepted: 2026/05/13 | Published: 2026/06/15
References
1. Emamat, M. S. M., & Hanafi Zadeh, P. (2021). Stock portfolio optimization using the reliability approach. Investment Knowledge, 9(36), 435-450.
2. Daryabar, A., Rahnamaye Roudposhti, F., Nikoomaram, H., & Ghaffari, A. (2020). Portfolio optimization in the context of financial bubbles in the capital market. Financial Knowledge of Securities Analysis, 11(40), 113-126.
3. Bayat, L., & Asadi, S. (2017). Stock portfolio optimization: The usefulness of the bird algorithm and the Markowitz model. Financial Engineering and Securities Management, 8(32), 63-85.
4. Heydari Heratmeh, H. (2020). Portfolio optimization through Conditional Value at Risk (CVaR) under a Variance Gamma (VG) process. Financial Knowledge of Securities Analysis, 12(41), 101-112.
5. Taghizadegan, G. R., Zomorodian, F. S., Mirfeyz, M., & Saadi, M. (2023). A comparison of the performance of the Markowitz model and liquidity risk-based Value at Risk model with DCC t-Copula LVaR in optimizing portfolios on the Tehran Stock Exchange. Financial Research, 25(1), 152-179.
6. Zamanpour, M., Zanjirdar, H., & Davoudi Nasr, M. (2022). Identification and ranking of factors influencing stock portfolio optimization using the fuzzy network analysis approach. Financial Engineering and Securities Management, 12(47), 210-236.
7. Gunjan, A., & Bhattacharyya, S. (2023). A brief review of portfolio optimization techniques. Artificial Intelligence Review, 56(5), 3847-3886. [DOI:10.1007/s10462-022-10273-7]
8. Freitas, W. B., & Junior, J. R. B. (2023). Random walk through a stock network and predictive analysis for portfolio optimization. Expert Systems with Applications, 119597. [DOI:10.1016/j.eswa.2023.119597]
9. Song, Y., Zhao, G., Zhang, B., Chen, H., Deng, W., & Deng, W. (2023). An enhanced distributed differential evolution algorithm for portfolio optimization problems. Engineering Applications of Artificial Intelligence, 121, 106004. [DOI:10.1016/j.engappai.2023.106004]
10. Butler, A., & Kwon, R. H. (2023). Integrating prediction in mean-variance portfolio optimization. Quantitative Finance, 23(3), 429-452. [DOI:10.1080/14697688.2022.2162432]
11. Wang, Y., & Aste, T. (2023). Dynamic portfolio optimization with inverse covariance clustering. Expert Systems with Applications, 213, 118739. [DOI:10.1016/j.eswa.2022.118739]
12. Estrada-Padilla, A., Gómez-Santillán, C., Fraire-Huacuja, H. J., Cruz-Reyes, L., Rangel-Valdez, N., Morales-Rodríguez, M. L., & Puga-Soberanes, H. J. (2023). GRASP/Δ: An efficient algorithm for the multi-objective portfolio optimization problem. Expert Systems with Applications, 211, 118647. [DOI:10.1016/j.eswa.2022.118647]
13. Ikhlef, M., & Aïder, M. (2023). MULTIOBJECTIVE EVOLUTIONARY METAHEURISTIC APPROACH TO THE CONSTRAINED PORTFOLIO OPTIMIZATION PROBLEM. Pesquisa Operacional, 43, e266962. [DOI:10.1590/0101-7438.2023.043.00266962]
14. Erwin, K., & Engelbrecht, A. (2023). Meta-heuristics for portfolio optimization. Soft Computing, 1-29. [DOI:10.1007/s00500-023-08177-x]
15. Ruiz-Vélez, A., García, J., Alcalá, J., & Yepes, V. (2024). Enhancing robustness in precast modular frame optimization: Integrating NSGA-II, NSGA-III, and RVEA for sustainable infrastructure. Mathematics, 12(10), 1478. [DOI:10.3390/math12101478]
16. IHSANE, I., CHABANE, A. N., & SAHNOUN, M. H. A Multi-Criteria Decision-Making Approach for Optimizing Artificial Neural Networks with Fast NSGA-II for Electricity Demand Prediction.
17. Dong, Y., Liu, S., Pei, X., & Wang, Y. (2025). Spatially explicit multi-objective optimization tool for green infrastructure planning based on InVEST and NSGA-II towards multifunctionality. Land Use Policy, 150, 107465. [DOI:10.1016/j.landusepol.2024.107465]
18. Al-Majali, B. H., & Zobaa, A. F. (2025). Analyzing bi-objective optimization Pareto fronts using square shape slope index and NSGA-II: A multi-criteria decision-making approach. Expert Systems with Applications, 126765. [DOI:10.1016/j.eswa.2025.126765]
19. IHSANE, I., CHABANE, A. N., & SAHNOUN, M. H. A Multi-Criteria Decision-Making Approach for Optimizing Artificial Neural Networks with Fast NSGA-II for Electricity Demand Prediction.
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |