Journal of Industrial Engineering and Management Studies

A multi-objective fuzzy goal programming model for portfolio selection in Tehran stock exchange

Document Type : Case Study

Authors

Hamed Asgari 1
Javad Behnamian 2

1 Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran

2 Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran.

https://doi.org/10.22116/jiems.2025.498677.1586
Abstract
In this research, a new emerging model for the Tehran stock exchange market is considered, and a model with realistic constraints for the mentioned market is provided. Realistic constraints are incorporated in this model for applicable purposes, one of which is the transaction cost. The limited maximum number of stocks that should be invested is also considered. Additionally, constraints have been added to the classic portfolio selection model to prevent stocks from being bought in tiny quantities and avoid over-buying some stocks. The mentioned constraint can improve diversity in the selected portfolio. One of the factors considered by many financial market investors is the amount of liquidity of the stocks they have purchased. This study also considers the amount of portfolio liquidity as one of the essential objective functions affecting the selection of the portfolio. Finally, in the model presented in the present study, the investors can have a different stock portfolio according to their preferences. The proposed model is multi-objective fuzzy goal programming, which can simulate uncertainty in the Tehran stock exchange market and provide a rational framework for investors who invest in the financial markets. As the numerical instances show, the solutions when additional constraints are added to the mathematical model are close to exact results. This difference became significant when the maximum number of stocks increased. According to the results, when the number of stocks increases, GAMS software loses its functionality, and the utilization of meta-heuristics as an option is inescapable. Finally, the harmony search algorithm with added realistic constraints has provided better portfolios in such a situation. To compare the results, a genetic algorithm was used as the competing algorithm. After solving different instances and comparing the results of the algorithms, the superiority of the proposed harmony search algorithm has been proved.

Keywords

Portfolio selection
Harmony search algorithm
Fuzzy goal programming
Liquidity
Davies, R.J., H.M. Kat, and S. Lu, Fund of hedge funds portfolio selection: A multiple-objective approach, in Derivatives and Hedge Funds. 2016, Springer. p. 45-71.
Yue, W. and Y. Wang, A new fuzzy multi-objective higher order moment portfolio selection model for diversified portfolios. Physica A: Statistical Mechanics and its Applications, 2017. 465: p. 124-140.
Saborido, R., et al., Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection. Applied Soft Computing, 2016. 39: p. 48-63.
Liagkouras, K. and K. Metaxiotis, A new efficiently encoded multiobjective algorithm for the solution of the cardinality constrained portfolio optimization problem. Annals of Operations Research, 2018. 267(1-2): p. 281-319.
Macedo, L.L., P. Godinho, and M.J. Alves, Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules. Expert Systems with Applications, 2017. 79: p. 33-43.
Mashayekhi, Z. and H. Omrani, An integrated multi-objective Markowitz–DEA cross-efficiency model with fuzzy returns for portfolio selection problem. Applied Soft Computing, 2016. 38: p. 1-9.
Speranza, M.G., A heuristic algorithm for a portfolio optimization model applied to the Milan stock market. Computers & Operations Research, 1996. 23(5): p. 433-441.
Konno, H. and H. Yamazaki, Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market. Management science, 1991. 37(5): p. 519-531.
Mansini, R. and M.G. Speranza, Heuristic algorithms for the portfolio selection problem with minimum transaction lots. European Journal of Operational Research, 1999. 114(2): p. 219-233.
Mansini, R., W. Ogryczak, and M.G. Speranza, Conditional value at risk and related linear programming models for portfolio optimization. Annals of operations research, 2007. 152(1): p. 227-256.
Mercangöz, B.A. and E. Eroglu, The Genetic Algorithm: An Application on Portfolio Optimization, in Metaheuristic Approaches to Portfolio Optimization. 2019, IGI Global. p. 154-178.
Yaman, I. and T.E. Dalkılıç, Portfolio selection based on a nonlinear neural network: An application on the Istanbul Stock Exchange (ISE30). Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, 2019. 68(2): p. 1709-1723.
Yue, W., Y. Wang, and H. Xuan, Fuzzy multi-objective portfolio model based on semi-variance–semi-absolute deviation risk measures. Soft Computing, 2019. 23(17): p. 8159-8179.
Lamon, C., E. Nielsen, and E. Redondo, Cryptocurrency price prediction using news and social media sentiment. SMU Data Sci. Rev, 2017. 1(3): p. 1-22.
Soler-Dominguez, A., A.A. Juan, and R. Kizys, A survey on financial applications of metaheuristics. ACM Computing Surveys (CSUR), 2017. 50(1): p. 1-23.
Strumberger, I., N. Bacanin, and M. Tuba. Constrained portfolio optimization by hybridized bat algorithm. in 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS). 2016. IEEE.
Seyedhosseini, S.M., M.J. Esfahani, and M. Ghaffari, A novel hybrid algorithm based on a harmony search and artificial bee colony for solving a portfolio optimization problem using a mean-semi variance approach. Journal of Central South University, 2016. 23(1): p. 181-188.
Ismail, A. and H. Pham, Robust Markowitz mean‐variance portfolio selection under ambiguous covariance matrix. Mathematical Finance, 2019. 29(1): p. 174-207.
Nystrup, P., et al., Multi-period portfolio selection with drawdown control. Annals of Operations Research, 2019. 282(1-2): p. 245-271.
Kar, M.B., et al., A new bi-objective fuzzy portfolio selection model and its solution through evolutionary algorithms. Soft Computing, 2019. 23(12): p. 4367-4381.
Mohammadi, S. and A. Nazemi, On portfolio management with value at risk and uncertain returns via an artificial neural network scheme. Cognitive Systems Research, 2020. 59: p. 247-263.
Galankashi, M.R., F.M. Rafiei, and M. Ghezelbash, Portfolio selection: a fuzzy-ANP approach. Financial Innovation, 2020. 6(1): p. 1-34.
Asgari, H., Behnamian, J.  Multi-objective stock market portfolio selection using multi-stage stochastic programming with a harmony search algorithm, Neural Computing & Applications, 2022, 34, p. 22257–22274.
Asgari, H. Behnamian, J. A self-adjusting algorithm in portfolio selection problem by simultaneously considering the fundamental index and technical analysis, Journal of Modelling in Management, 2025, DOI: 10.1108/JM2-01-2024-0032.
Naik, P. and Y. Reddy, Stock market liquidity: A literature review. Sage Open, 2021. 11(1): p. 2158244020985529.
Nasraoui, M., A. Ajina, and A. Kahloul, The influence of economic policy uncertainty on stock market liquidity? The mediating role of investor sentiment. The Journal of Risk Finance, 2024. 25(4): p. 664-683.
Abensur, E., Machine Learning for liquidity classification and its applications to portfolio selection. Brazilian Review of Finance, 2024. 22(2): p. 1-14.
Yadav, S., et al., A multi-objective sustainable financial portfolio selection approach under an intuitionistic fuzzy framework. Information Sciences, 2023. 646: p. 119379.
Faridi, S., et al., Portfolio rebalancing based on a combined method of ensemble machine learning and genetic algorithm. Journal of Financial Reporting and Accounting, 2023. 21(1): p. 105-125.
de Moraes, M.B., G.P. Coelho, and R.B. Bratvold, A Machine Learning-Assisted Decision-Making Methodology Based on Simplex Weight Generation for Non-Dominated Alternative Selection. Decision Analysis, 2025.
Lee, J. and K.H. Chung, Foreign ownership and stock market liquidity. International Review of Economics & Finance, 2018. 54: p. 311-325.
Chordia, T., R. Roll, and A. Subrahmanyam, Commonality in liquidity. Journal of financial economics, 2000. 56(1): p. 3-28.
Jiménez, M., A. Bilbao‐Terol, and M. Arenas‐Parra, A model for solving incompatible fuzzy goal programming: an application to portfolio selection. International Transactions in Operational Research, 2018. 25(3): p. 887-912.
Gupta, P., et al., A polynomial goal programming approach for intuitionistic fuzzy portfolio optimization using entropy and higher moments. Applied Soft Computing, 2019. 85: p. 105781.
Soler-Dominguez, A., A.A. Juan, and R. Kizys, A survey on financial applications of metaheuristics. ACM Computing Surveys (CSUR), 2017. 50(1): p. 15.
He, Y.-C., et al., Exact and approximate algorithms for discounted {0-1} knapsack problem. Information Sciences, 2016. 369: p. 634-647.
Pai, G.V. and T. Michel, Metaheuristic optimization of constrained large portfolios using hybrid particle swarm optimization. International Journal of Applied Metaheuristic Computing (IJAMC), 2017. 8(1): p. 1-23.
Sabar, N.R., et al. Multi-population genetic algorithm for cardinality constrained portfolio selection problems. in International Conference on Computational Science. 2018. Springer.
Journal of Industrial Engineering and Management Studies
Volume 12, Issue 1
July 2025
Pages 39-52

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