Financial Management Perspective

Financial Management Perspective

Presentation of the structural model of risk types in banks using the Fuzzy Interpretative Structural Modeling Approach

Document Type : Original Article

Authors
Hassan Farsijani 1
Mohsen Arefnezhad 2
Somayeh Asadi 3
Ali Hasanvand 4
1 Associate Prof, Department of Industrial Management, Shahid Beheshti University, Tehran, Iran.
2 Assistant Prof, Department of Business Management, Lorestan University, Lorestan, Iran.
3 Master of Business Management, Lorestan University, Lorestan, Iran.
4 Ph.D. Candidate in Economics, Razi University, Kermanshah, Iran.
10.52547/jfmp.11.33.173
Abstract
The experiences of recent decades in financial markets, and in particular the banks of different countries, indicate an increase in the importance of risk management in financial activities. Therefore, international efforts are aimed at creating a framework for standards that can be achieved by improving the financial health of the institution, especially banks. The axis of these standards has been manifested in creating an integrated risk management framework in the context of corporate risk management. The main purpose of the present research is to design a structural model of the types of existing risks in the banking sector. By studying thematic literature and using the textual content analysis approach, eleven effective risks were identified and for localization of them in the country's banking sector, Delphi technique was used for three periods of use It turned out The statistical population of the study consisted of managers and experts familiar with the subject and working in the banking sector. A researcher-made questionnaire was used to collect data. The reliability and validity of the questionnaire was confirmed by calculating Kendall's correlation coefficient (0.82) and Goghos and Boucher's correlation coefficient (0.08, 0.06), respectively. In order to design a structural model of risk, an interpretive structural modeling approach was used in fuzzy environment to manage linguistic ambiguity in judgments. The results of the Mick-Mac modeling and analysis showed that liquidity, credit, operational, interest rate, exchange rate risk and risk of laws and regulations are key and basic risks in the banking sector.
Keywords
Risk
Risk Management
Fuzzy Interpretative Structural Modeling
  1. Abbasian, E. A., Fallahi, S., Rahmani, A. S. (2016). The effect of diversification in the facility portfolio on banks' credit risk. Quarterly Journal of Financial Research, 18(3), 149-166. (In Persian)
  2. Akyuz, E., Celik, E. (2015). A fuzzy dematel method to evaluate critical operational hazards during gas freeing in crude oil tankers. Journal of Loss Prevention in the Process Industries. 38, 243-253.
  3. Asgharpour, M. J. (2003). Group decision making and game theory with an operational research approach. Tehran: University of Tehran Press, first edition. (In Persian)
  4. Bacudioa, L. (2016). Analyzing barriers for implementing industrial symbiotic networks using Sustainable Production and Consumption, 7, 57-65.
  5. Busse, J., Goyal, A., Wahal, S. (2010). Performance and persistence in institutional investment management. The Journal of Finance .Vol. LXV, NO.2.
  6. Elbadry, A. (2017). Bank’s financial stability and risk management. Journal of Islamic Accounting and Business Research. Permanent link to this document: https://doi.org/10.1108/JIABR-03-2016-0038.
  7. Eslami Gh. & Honardust A. (2016). Cross-Sectional Stock Returns, Market Liquidity Risk, And Financial Market Anomalies. Financial Research,18 (1), 185-200. (In Persian).
  8. Ferdowsi, M., Fotros, M. H. (2017). The effect of credit risk and liquidity risk on the performance of banks. Quarterly Journal of Risk Modeling and Financial Engineering, 2(1), 22-41. (In Persian)
  9. Ghorbanpour, A., Pouya, A., Nazemi, Sh., Naji Azimi, Z. (2016). Structural model design of green supply chain management measures using fuzzy interpretive structural modeling approach. Journal of Operations Research in its Applications, 13(4), 1-20. (In Persian)
  10. Goghardchian, A., Fathi, S., Amiri, H., Saeedi Vernamkhasti, N. (2015). Comparative analysis of the impact of political risk on the stock market development of selected countries. Journal of Investment Knowledge, 4(15), 135-156. (In Persian)
  11. González, F. (2005). Bank regulation and risk-taking incentives: An international comparison of bank risk. Journal of Banking and Finance, 29,1153- 1184.
  12. Gumus, A. T., Yayla, A. Y., Celik, E., Yildiz, A. (2013). A combined fuzzy-ahp and fuzzygramethodology for hydrogen energy storage method selection in Turkey. Energies, 6(6), 3017 -3032.
  13. Ismailzadeh, A. . & Javanmardi, H. (2017). Designing a suitable model for liquidity management and risk forecasting in Bank Saderat Iran. Financial Economics, 11( 39), 191-171. (In Persian)
  14. Jamaat, A. & Asgari, F. (2011). Credit Risk Management In Banking System With Data Mining Approach. Quantitative Researches In Management, 1(3), 115-126 . (In Persian)
  15. Jeng, D. (2015). Generating a causal model of supply chain collaboration using the fuzzy dematel technique. Computers & Industrial Engineering, 87, 283–295.
  16. Jia, P., Diabat, A., Mathiyazhagan, K. (2014). Analyzing the SSCM practices in the mining and mineral industry by ISM approach. Resources Policy, 46, 76–85.
  17. Khosh-sima, R. & Shahiki-Tash, M. (2013). The Impact of Credit, Operational and Liquidity Risks on the Efficiency of Banking System in Iran. JPBUD, 17 (4), 69-95. (In Persian)
  18. Kollára, B., Weissováb, I ., & Siekelovác, A. (2015). Comparative analysis of theoretical aspects in credit risk models. Journal of Procedia Economices and Finance, 24, 331-338.
  19. Mathiyazhagan, K., Haq, A. N. (2013). Analysis of the influential pressures for green supply chain management adoption an Indian perspective using interpretive structural modeling. The International Journal of Advanced Manufacturing Technology, 68, 817–833.
  20. Muduli, K., Govindan, K., Barve, A., Kannan, D., Geng, Y. (2013). Role of behavioral factors in green supply chain management implementation in Indian mining industries. Conservation and Recycling, 76, 50–60.
  21. Pashaei Pham, R. & Attached farmers, A. (2011). Monetary policies in line with the economic transformation plan, Proceedings of the 21st Annual Conference on Monetary and Foreign Exchange Policies, Tehran, Monetary and Banking Research Institute. (In Persian)
  22. Pramod, V. R., Banwet, D. K. (2013). Fuzzy ISM for analyzing the inhibitors of a telecom service supply chain. International Journal of Information Systems and Supply Chain Management, 6, 77-98.
  23. Saiful, A. (2107). Contingency factors, risk management, and performance of indonesian banks. Asian Journal of Finance & Accounting, 9 (1), 35-54.
  24. Salarzehi, H. & Javid, S. (2014). Identify and rank the effective management dimensions in establishing an information security management system with a fuzzy hierarchical analysis approach. Scientific-Research Journal of Security and Protection Research, 3(11) , 132-115. (In Persian)
  25. Seifollah, N. (2017). Negative Relationship between Credit Risk and Foreign Exchange Risk with Bank Stock Returns in Iran (GARCH-M. Approach) Quarterly Journal of Financial Engineering and Securities Management, 8(30), 19-31. (In Persian)
  26. Sheng, C., Zhang, D.,Wangb, G., H, Yingli. (2021). Research on risk mechanism of China’s carbon financial market development from the perspective of ecological civilization. Journal of Computational and Applied Mathematics. 381, 112-126.
  27. Suleman, T., Randal, J. (2013). Dynamics of political risk rating and stock market volatility. https://papers.ssrn.com/sol3/papers. cfm? Abstract _id=2315645.
  28. Thakkar, J., Kanda, A., Deshmukh, S. G. (2008). Interpretive Structural Modeling (ISM) of ITenablers for Indian manufacturing SMEs. Information Management & Computer Security, 16, 113–136.
  29. Tseng, M. (2013). Modeling sustainable production indicators with linguistic preferences. Journal of Cleaner Production, 40, 46-56.
  30. Tseng, M., Chiu, A. (2013). Evaluating firm’s green supply chain management in linguistic Journal of Cleaner Production, 40, 22-31.
  31. Xiong, G., Li, L., Hao, J. (2011) . 2-tuple linguistic fuzzy ISM and its application. In Fuzzy Information and Engineering 2010, Springer, Berlin, Heidelberg. 353–362.
  32. Zulkifli, N., Abdullah, L. (2015). Integration of fuzzy AHP and interval type-2 fuzzy DEMATEL: An application to human resource management. Expert Systems with Applications, 42 (9): 4397–4409.
Financial Management Perspective
Volume 11, Issue 33
Spring 2021
Pages 173-192