Providing an intelligent credit risk management system of the bank based on the macroeconomic indicators in the country's stock exchange banks

Subject Areas : Financial Accounting

Mohsen ziaee Bidhendy ¹ , Mehrzad Minooee ² , Mirfeiz Fallahshams ³

1 - Financial Department, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Financial Department, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Financial Department, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Received: 2021-09-28 Accepted : 2021-10-31 Published : 2023-09-01

Keywords: MATLAB programming environment, Financial risk management, Economic and financial crisis, Exchange Banks,

Abstract :

This study focuses on providing an intelligent credit risk management system of the bank in the presence of the macroeconomic indicators using a combined methodology of econometrics and artificial intelligence. In addition to the use of scientific documents and reports, the panel data related to the annual reports and datasets of stock exchange banks are analyzed by using the MATLAB programming environment. One of the most important results of the this paper is that the proposed approach has been based on the calculations made with the GARCH economic model in which the input values of the component "Inflation rate factor (A4)” have a weight of 0.943734 (equivalent to the membership function "High H"); the component "rate factor Bank deposit (B4)” has a weight of 0.959346 (equivalent to the "High H" membership function); the component “Unemployment rate factor (A3)” has a weight of 0.990343 (equivalent to the "High H" membership function); the component "Exchange Rate Factor (B2)" has a weight of 0.990413 (equivalent to the membership function "High H"); And the component "GDP growth rate factor (A1)” has a weight of 0.959256 (equivalent to the membership function of "high H"); This means that, 5.46 is within a range of 6, i.e. the target variable is exactly in the 91st position (the fifth level of the system output is excellent).

References:

[1] Abad, P., Benito, S., A Detailed Comparison of Value at Risk in International Stock Exchanges, Fundación De Las Cajas De Ahorros, Documento De Trabajo (452/2009), 2009, P.1-45.

[2] Abdul Shah, F., Moshiri, S., Stress test of default probabilities of Iranian stock exchange banks with credit portfolio approach,  Economic Research Review,  2017 , 17(66) , P. 23-54. (in Persian)

[3] Asteriou, D., Spanos, K., The relationship between financial development and economic growth during the recent crisis: Evidence from the EU, Finance Research Letters, 2019, 1(28), P. 238-45. Doi:10.1016/j.frl.2018.05.011.

[4] Bakhtiyariasl Asl, C., Davoodi, SM., Abdolbaghi Ataabadi A., Designing and evaluating the profitability of linear trading system based on the technical analysis and correctional property, Advances in Mathematical Finance and Applications, 2021, 3.  Doi:10.22034/AMFA.2021.1906285.1474

[5] Boss, M., A macroeconomic credit risk model for stress testing the Austrian credit portfolio, Financial stability report, 2002,4, P. 64-82.

[6] Cerullo, M.J., Cerullo, V., Using neural networks to predict financial reporting fraud: Part 1, Computer Fraud & Security, 1999, 1(5), P.14-7. Doi:10.1016/S1361-3723(99)80015-3

[7] Chan, W.H., Le, M., Wu, Y.W., Holding Bitcoin longer: The dynamic hedging abilities of Bitcoin, The Quarterly Review of Economics and Finance, 2019, 1(71), P. 107-13. Doi:10.1016/j.qref.2018.07.004

[8] Chen, Y., Du, K., The role of information disclosure in financial intermediation with investment risk, Journal of Financial Stability, 2020, 1(46), P. 100-120. Doi: 10.1016/j.jfs.2019.100720

[9] Chen, Y., Wang, Z., Zhang, Z., Mark to market value at risk, Journal of Econometrics, 2019, 208(1), P. 299-321. Doi:10.1016/j.jeconom.2018.09.017

[10] Corzo, T., Lazcano, L., Márquez, J., Gismera, L., Lumbreras, S., A common risk factor in global credit and equity markets: An exploratory analysis of the subprime and the sovereign-debt crises, Heliyon. 2020, 6(6), P. 39-80. Doi:10.1016/j.heliyon.2020.e03980

[11] Dstgir, M., Sakiani, A., Salehi, N., The effect of financial constraints on the risk of stock price falls considering the effect of accruals, Accounting Knowledge Quarterly, 2009, 10(1) (in Persian).

[12] Falahzadeh Abarqoui, A., Taftian, A., and Heerani, F., Investigating the Relationship between Information Disclosure and Stock Price Simultaneity and Its Risk of Falling Using the Simultaneous Equation System, Accounting Knowledge Quarterly, 10, 2009 (in Persian).

[13] Farid, D., Mahmoudi, M., and Taghiban Dinani, Z., Systematic Financial Risk Survey in Momentum Trading Strategy in Tehran Stock Exchange, International Conference on Management and Humanities, UAE-Dubai, Vira Capital Institute of Managers, 2015 (in Persian).

[14] Gholamzadeh, M., A Study of Factors Affecting Interest Rates after the 2008 Financial Crisis in Selected Developed and Developing Countries, The First Accounting and Management Conference, 2019 (in Persian).

[15] He, Q, Liu, J, Gan, J, Qian, Z., Systemic financial risk and macroeconomic activity in China, Journal of Economics and Business, 2019, 102, P. 57-63. Doi:10.1016/j.jeconbus.2018.10.002

[16] Hu, GX., Rollover risk and credit spreads in the financial crisis of 2008, The Journal of Finance and Data Science, 2020, 1(6), P.1-5. Doi:10.1016/j.jfds.2020.06.001

[17] Izadikhah, M., DEA Approaches for Financial Evaluation-A Literature Review, Advances in Mathematical Finance and Applications, 2022, 7(1), P. 1-37. Doi:10.22034/AMFA.2021.1942092.1639

 

[18] Izadikhah, M. Financial Assessment of Banks and Financial Institutes in Stock Exchange by Means of an Enhanced Two stage DEA Model. Advances in Mathematical Finance and Applications, 2021, 6(2), P. 207-232. Doi: 10.22034/amfa.2020.1910507.1491

[19] Kohzadi Tahneh, A., A Study of the Relationship between Credit Risk and Profitability of Iranian Commercial Banks, Sixth International Conference on Management and Accounting, Tehran, 2019 (in Persian)

[20] Koskivaara, E., Different pre-processing models for financial accounts when using neural networks for auditing, ECIS 2000 Proceedings, 2000, 1, P. 3-20.

[21] Lei, L., Shang, Y., Chen, Y., Wei, Y., Does the financial crisis change the economic risk perception of crude oil traders? A MIDAS quantile regression approach, Finance Research Letters, 2019, 30, P.341-51. Doi:10.1016/j.frl.2018.10.016

[22] Lin, C.T., Lee, C.G., Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems, Prentice hall, 1996.

[23] Mishra, R.N., Mohanty, K.B., Real time implementation of an ANFIS-based induction motor drive via feedback linearization for performance enhancement, Engineering Science and Technology, an International Journal, 2016, 19(4), P. 1714-30. Doi:10.1016/j.jestch.2016.09.014

 

[24] Moayer S, Bahri PA., Hybrid intelligent scenario generator for business strategic planning by using ANFIS, Expert Systems with Applications, 2009, 36(4), P. 7729-37. Doi:10.1016/j.eswa.2008.09.046

[25] Nofsinger, J.R., Patterson, F.M., Shank, C.A., Decision-making, financial risk aversion, and behavioral biases: The role of testosterone and stress, Economics & Human Biology, 2018, 29, P. 1-6. Doi:10.1016/j.ehb.2018.01.003

[26] Pattnaik, D., Hassan, M.K., Kumar, S., Paul, J., Trade credit research before and after the global financial crisis of 2008–A bibliometric overview, Research in International Business and Finance, 2020, 30, P. 101-287. Doi:10.1016/j.ribaf.2020.101287

[27] Rostamian, N., Hosseini Yakani, A., Kashiri Kolaei, F., Measuring Operational Risk in the Iranian Banking System Using Risk-Value Index, 7th National Conference on Accounting and Management Applications, Tehran, Asia Gold Communication Group, 2017 (in Persian).

[28] Tabandeh, R., Jusoh, M., Nor, N.G., Zaidi, M.A., Estimating factors affecting tax evasion in Malaysia: A neural network method analysis, Prosiding Persidangan Kebangsaan Ekonomi Malaysia Ke VII. 2012, P. 15-25.

[29] Tavakolia, M., Doostib, H., Chesneauc, C., Capsule Network Regression Using Information Measures: An Application in Bitcoin Market. Doi:10.22034/AMFA.2021.1932547.1603

[30] Zadeh, LA., Fuzzy collection, Inf. Control. 1965, 8(33), P. 8-56.

[31] Zomordian, G., Hemmati Asyabraki, M., Radkoftroudi, H., Periodic Value Exposure Value Test (LiVaR) and risk management using self-regression vector (VAR) model, Financial Knowledge of Securities Analysis (Financial Studies), 2017, 10, P. 59-69 (in Persian).