Optimization of Financial Complexity of Iranian Listed Companies Using the Artificial Bee Colony Algorithm and a Fuzzy Multi-Criteria Decision-Making Approach

Behnaz   Khoshnoodnasab; Maryam Akbaryan; Mehdi  Zolfaghari

Authors

Behnaz Khoshnoodnasab Department of Accounting, Ra.C., Islamic Azad University, Rasht, Iran Author https://orcid.org/0009-0006-0983-3182
Maryam Akbaryan Department of Accounting, Som.C., Islamic Azad University, Somesara, Iran Corresponding author https://orcid.org/0009-0006-0983-3182
Mehdi Zolfaghari Department of Accounting, Som.C., Islamic Azad University, Somesara, Iran Author https://orcid.org/0009-0006-0983-3182

Keywords:

Optimization, Fuzzy Multi-Criteria Decision Making, Artificial Bee Colony Algorithm, Listed Companies

Abstract

Financial complexity is one of the fundamental challenges faced by firms in the unstable environment of financial markets, which can lead to increased risk and reduced financial performance efficiency. The objective of this study is to optimize the financial complexity of companies listed on the Tehran Stock Exchange using the Artificial Bee Colony (ABC) algorithm and a fuzzy multi-criteria decision-making approach. This research is quantitative and applied in terms of methodology. In the first stage, the factors influencing financial complexity that had been previously identified were weighted and prioritized using the Fuzzy Step-wise Weight Assessment Ratio Analysis (Fuzzy SWARA) method. Subsequently, to optimize the level of financial complexity, the Artificial Bee Colony algorithm, as an artificial intelligence–based metaheuristic algorithm, was implemented on the financial data of 20 companies listed on the Tehran Stock Exchange. The optimization criterion was the minimization of the objective function value and the error level resulting from the algorithmic process. The results of the algorithm execution indicate that as the number of iterations increases, the objective function value decreases and the optimization process achieves satisfactory convergence. Specifically, in the fifth execution of the algorithm, after 1,550 iterations, the minimum objective function value of 1.3473 was obtained, representing the lowest optimization error level and the attainment of the most desirable financial complexity condition. The findings demonstrate that the Artificial Bee Colony algorithm, in combination with a fuzzy multi-criteria decision-making approach, constitutes an efficient tool for optimizing and managing the financial complexity of listed companies and can assist financial managers and capital market policymakers in making optimal decisions.

References

[1] N. F. Johnson, P. Jefferies, and P. M. Hui, Financial Market Complexity. Oxford University Press, 2003.

[2] D. Sornette, Why stock markets crash: Critical events in complex financial systems. Princeton University Press, 2017.

[3] B. I. Carlin, "Strategic complexity in financial markets," Annual Review of Financial Economics, vol. 12, pp. 99-119, 2020, doi: 10.1146/annurev-financial-012820-022439.

[4] M. Dooley, "Financial system complexity and systemic risk," Journal of Economic Perspectives, vol. 32, no. 1, pp. 77-100, 2018, doi: 10.1257/jep.32.1.77.

[5] S. Arora, B. Barak, M. K. Brunnermeier, and R. Ge, "Computational complexity and information asymmetry in financial markets," Journal of Finance, vol. 75, no. 6, pp. 3159-3207, 2020, doi: 10.1111/jofi.12945.

[6] B. I. Carlin, S. Kogan, and R. Lowery, "Trading complex assets," Journal of Finance, vol. 68, no. 5, pp. 1937-1960, 2013, doi: 10.1111/jofi.12055.

[7] C. Célérier and B. Vallée, "Catering to investors through security design: Headline rate and complexity," Quarterly Journal of Economics, vol. 132, no. 3, pp. 1469-1508, 2017, doi: 10.1093/qje/qjx012.

[8] D. Awrey, "Complexity, innovation, and the regulation of modern financial markets," Harvard Business Law Review, vol. 9, no. 2, pp. 235-294, 2019.

[9] J. Benamati, A. L. Lederer, and M. Singh, "Managing financial complexity in modern organizations," Decision Support Systems, vol. 168, p. 113868, 2023, doi: 10.1016/j.dss.2023.113868.

[10] X. Kong and Y. Li, "Financial complexity, instability, and adaptive systems," Physica A, vol. 572, p. 125886, 2021, doi: 10.1016/j.physa.2021.125886.

[11] M. Sharifi and N. Rezaei, "The effect of fintech on the financial efficiency of banks considering the role of credit and economic risks in the Tehran Stock Exchange," 2024.

[12] M. K. Brunnermeier and Y. Sannikov, "A macroeconomic model with a financial sector," American Economic Review, vol. 104, no. 2, pp. 379-421, 2014, doi: 10.1257/aer.104.2.379.

[13] J. Gual, S. Jódar-Rosell, and J. Ruiz, "Financial complexity and economic growth," Economic Policy, vol. 32, no. 92, pp. 725-777, 2017, doi: 10.1093/epolic/eix018.

[14] D. Rickles, "Measuring economic complexity," Philosophy of Science, vol. 85, no. 3, pp. 453-476, 2018, doi: 10.1086/697592.

[15] M. J. Saei, A. Taleb Ahmadi, and M. A. Bagherpour Velashani, "An Analysis of the Impact of Financing Methods on the Returns and Performance of Companies Listed on the Iran Stock Exchange," Journal of Monetary Economics Research, vol. 32, no. 1, 2025.

[16] H. Mohammadi and S. Pendar, "Investigating the impact of accounting information complexity on the speed of capital structure adjustment," Capital Market Analysis Journal, no. 4, pp. 142-172, 2022.

[17] S. Hasanzadeh and N. Ahmadian, "The role of the capital market in Iran's economic growth," Iranian Economic Research Quarterly, vol. 19, no. 3, pp. 87-110, 2015.

[18] M. Soltani Pour Sardou, "Examining the effect of institutional investors and financial statement entropy on stock returns in companies listed on the Tehran Stock Exchange," in The 20th National Conference on Economics, Management, and Accounting, Shirvan, 2024. [Online]. Available: https://civilica.com/doc/2070243/.

[19] R. Talebi, M. R. Kiani, and A. Khodadadi, "Investigating the impact of financial statement auditing on stock return volatility and trading volume in the Tehran Stock Exchange," in The First International Conference on the Islamic Human Sciences Revolution, Tehran, 2024. [Online]. Available: https://civilica.com/doc/2087327/.

[20] Y. Ebrahimpour, E. Noubahar, and P. Mohammadzadeh, "Investigating Herding Behavior in Industry Groups in the Tehran Stock Exchange," Asset Management and Financing, vol. 13, no. 3, pp. 1-28, 2025.

[21] S. M. M. Afshin, A. Mohammadzadeh, F. Rezaei, and E. Abbasi, "Identification of Stock Return Components Using Novel Composite Variables in Tehran Stock Exchange," Accounting, Finance, and Computational Intelligence, vol. 3, no. 1, pp. 1-20, 2025. [Online]. Available: https://jafci.com/index.php/jafci/article/view/126.

[22] A. Taslimpour, G. Askarzadeh, K. Qalmaq, and H. Nasiri, "Forecasting the Tehran Stock Exchange index using the NARX neural network model," Asset Management and Financial Supply, vol. 14, no. 1, pp. 21-48, 2026, doi: 10.22108/amf.2025.143461.1940.

[23] J. Rosenhead and J. Mingers, Systems Thinking and Modeling Complex Problems (Persian Translation). Tehran: SAMT Publications, 2015.

[24] M. Bayat and M. Asadi, "Application of meta-heuristic algorithms in financial problem optimization," Financial Research, vol. 18, no. 2, pp. 45-68, 2017.

[25] M. Alam-Tabriz, H. Rezaei, and S. Mousavi, "Nature-inspired benchmarking in the development of intelligent algorithms," Computational Intelligence Quarterly, vol. 4, no. 1, pp. 23-40, 2013.

[26] J. C. Bansal, H. Sharma, and S. S. Jadon, "Artificial bee colony algorithm: a survey," International Journal of Advanced Intelligence Paradigms, vol. 5, no. 1/2, pp. 123-159, 2013, doi: 10.1504/IJAIP.2013.054681.

[27] D. Karaboga and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm," Journal of Global Optimization, vol. 39, no. 3, pp. 459-471, 2007, doi: 10.1007/s10898-007-9149-x.

[28] M. Rahimi and A. Ramezani-Khansari, "Artificial Bee Colony (ABC) algorithm and its application in optimization," Journal of Iranian Industrial Engineering, vol. 25, no. 2, pp. 55-72, 2014.

[29] R. K. Mavi, N. Zarbakhshnia, and A. Khazraei, "An integrated SWARA-MABAC approach for sustainable supplier selection," Economic Research-Ekonomska Istraživanja, vol. 30, no. 1, pp. 1-22, 2017, doi: 10.1080/1331677X.2017.1314828.