Introduction Determining the optimal portfolio of bank facilities with the Markowitz approach and meta-heuristic algorithms (Case study of Sina Bank)

Document Type : Research Paper

Authors

1 PHD student in economics, Department of Economics, Faculty of Economics and Accounting, Central Tehran Branch, Islamic Azad University

2 Assistant professor, Department of Economics, Faculty of Economics and Accounting, Central Tehran Branch, Islamic Azad University

Abstract

Banks face credit risk in the process of providing facilities based on the amount of resources provided. In the meantime, facility portfolio management can be effective in reducing credit risk by optimally allocating resources to economic sectors. In this research, the portfolio of Sina Bank's facilities is determined by using the Markowitz modern portfolio model and meta-heuristic algorithms of genetics and firefly. Comparing the performance of the models indicates the greatest efficiency of the genetic algorithm model in optimizing the bank's facilities portfolio. the results of the estimation of this model show that the service & commercial, housing & construction sectors have the largest share in the optimal portfolio of the bank's facilities. Industry & mining, agriculture & water sectors are considered risky assets of Sina Bank. the process of granting facilities has not been optimal. To reduce the credit risk of that bank's facilities, 52.4% should be allocated to the service & commercial sector, 40.7% to the housing & construction sector, 3.5% to the industry & mining sector, 3.4% to be allocated to agriculture & water sector.

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References

  1. Agarana, M. C., Bishop, S. A., & Odetunmibi, O. A. (2014). Optimization of banks loan portfolio management using goal programming technique. International Journal of Research in Applied, Natural and Social Sciences, 2(8), 43-52.‏
  2. Bargi, A. N. (2024). Stock portfolio optimization: Comparing the Markowitz Model with the Black Litterman Model. International Journal of Research Publication and Reviews, 5(3), 3388-3393.
  3. Central Bank of the Islamic Republic of Iran (2005). Principles of credit risk management. Ranjbar Motlaq, Lida (In Persion).
  4. Central Bank of the Islamic Republic of Iran, the Higher Institute of Banking Education of Iran, the performance report of the country's banks, https://www.ibi.ac.ir (In Persion).
  5. Chang, T. J., Yang, S. C., & Chang, K. J. (2009). Portfolio optimization problems in different risk measures using genetic algorithm. Expert Systems with applications, 36(7), 10529-10537.‏
  6. Dubinskas, P., & Urbšienė, L. (2017). Investment portfolio optimization by applying a genetic algorithm-based approach. Ekonomika, 96(2), 66-78.‏
  7. Eghtesad, A., & Mohammadi, Ů. (2023). Portfolio optimization with return prediction using LSTM, Random forest, and ARIMA. Financial Management Perspective, 13(43), 9-28.‏
  8. Enoch, C. N., & Labuschagne, L. (2014). Towards a theoretical foundation for project portfolio management. Project Management Institute.‏
  9. Estrada, J. (2000). The cost of equity in emerging markets: a downside risk approach.‏
  10. Gunjan, A., & Bhattacharyya, S. (2024). Quantum-inspired meta-heuristic approaches for a constrained portfolio optimization problem. Evolutionary Intelligence, 1-40.‏
  11. Li, H., & Shi, N. (2022). Application of genetic optimization algorithm in financial portfolio problem. Computational Intelligence and Neuroscience, 2022(1), 5246309.‏
  12. Jahanian, F., Motaghi, A. A., & Mohammadi, A. (2022). Portfolio optimization using the modified Markowitz model based on CO-GARCH modeling in comparison with the market. Journal of Stable Economics, (2)7, 70-84 (In Persion).
  13. Kamarei, M. (2011). Examining Sepeh Bank's loan portfolio and determining its optimal combination. Master's Thesis, University of Tehran (In Persion).
  14. Karimpour, A., & Agassi, S. (2022). Choosing the optimal stock portfolio using the meta-heuristic algorithm of flower pollination and comparing the results with the traditional Markowitz model. Journal of Modern Management Engineering, 28, 79-110 (In Persion).
  15. Kazan, H., & Uludag, K. (2014). Credit portfolio selection according to sectors in risky environments: Markowitz practice. Asian Economic and Financial Review, 4(9), 1208.‏
  16. Khandan, A. (2023). Comparison of average performance with median and other risk indicators in stock portfolio optimization. Quarterly Journal of Quantitative Economics, (20)1, 99-138 (In Persion).
  17. Markowitz, H. M. (1959). Portfolio Selection, Cowles Foundation Monograph 16. New York, New York: JohnWiley and Sons, 1959MarkowitzPortfolio Selection: Cowles Foundation Monograph, 161959.‏
  18. Mathworks, N. (2004). Genetic algorithm and direct search toolbox for use with MATLAB.‏
  19. Mehrara, M., & Sadeghian, S. (2008). Determining the optimal composition of loans in economic sectors (case study of Saman Bank). Financial Economics Quarterly, (2) 5, 116-134 (In Persion).
  20. Nahvi, A., Ghorbani, M., Sabohi, M., & Durandish, A. (2021). Examining the optimal composition of the credit portfolio using the worm algorithm (case study of Agricultural Bank). Iranian Economic Studies Quarterly, (37) 10, 53-97 (In Persion).
  21. Nobakht J., Ahmadi, M. M., Gholami, E., & Ebrahimi, M. (2019), Optimizing the National Bank's credit portfolio with the approach of reducing the cost of granted facilities. Financial Studies and Islamic Banking Quarterly, 5(12), 81-102 (In Persion).
  22. Raei, R., & Saeedi, A. (2022). Fundamentals of Financial Engineering and Risk Management. Organization for the Study and Compilation of Academic Books in Islamic and Human Sciences (Samt), Humanities Research and Development Institute, 2013 (In Persion).
  23. Rahnama Roudposhti, F., & Moosavi Enzahai, M. (2013). Comparison of performance of portfolios resulting from stock grouping by network model based on modern and traditional variables using Sharpe and Trainor indices. Investment Knowledge Quarterly, (2)7, 193-212 (In Persion).
  24. Haupt, R. L. & Haupt,S. E. (2004). Practical genetic algorithms.
  25. Soltani, A., Ehtsham Rathi, R., & Abedi, S. (2023). Providing a multi-objective mathematical model for optimizing the equipment and allocation of financial resources of the banking system. Journal of Industrial Management, (15)­2, 272-298 (In Persion).
  26. Yahyazadeh Far, M., Shams, Sh., & Rezazadeh, M. (2010), Evaluation of portfolio management performance of investment companies in Tehran Stock Exchange. Executive Management Research Journal, 10(2), 157-178 (In Persion).
  27. Yang, X. S. (2010). Nature-inspired metaheuristic algorithms. Luniver press.‏
  28. Yekta, A. (2015). Determining the optimal composition of the future bank's credit portfolio based on the generalized conditional variance heteroskedasticity model (GARCH). Master's thesis, Islamic Azad University, Firuzkoh branch (In Persion).
  29. Wang, Z., Zhang, X., Zhang, Z., & Sheng, D. (2022). Credit portfolio optimization: a multi-objective genetic algorithm approach. Borsa Istanbul Review, 22(1), 69-76.‏
Quarterly Journal of Applied Theories of Economics
Volume 11, Issue 2 - Serial Number 41
September 2024
Pages 167-198

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