Modeling the relationship between financial stability and banking risks: artificial intelligence approach

Iranian Journal of Accounting, Auditing and Finance

مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 26 آبان 1403

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.22067/ijaaf.2024.90185.1498

نویسندگان

HAKEEM FARAJ GUMAR¹؛ parviz piri^{* 2}؛ Mehdi Heydari³

¹nazlo

²Associate Professor, Faculty of Economics and Management, Department of Accounting, Urmia University, Urmia, Iran

³urmia

چکیده

This research was conducted with the aim of evaluating the effect of financial stability efficiency on the financial stability of banks in Iran and Iraq. In addition, this study aims to identify the main factors affecting financial stability and provide solutions to improve risk management and banking supervision.The statistical population of this research includes 66 banks (22 Iranian banks and 44 Iraqi banks) which are listed on the stock exchanges of Iran and Iraq. The study period is from 2000 to 2023. A wide range of artificial neural network approaches and machine learning algorithms have been used for data analysis. The results of various analyzes show that machine learning models and neural networks have a significant performance in examining and evaluating the financial stability of banks. The artificial neural network showed the highest accuracy with a coefficient of determination of 0.95. Gradient boosting and random forest also had high performance with determination coefficients of 0.9566 and 0.9441. Spatial clustering and k-means algorithms were able to group banks based on their financial stability with an accuracy of nearly 100%. The variables of capital adequacy ratio, cash flow, bank size, and Z score were identified as the most important factors affecting financial stability. Deep, convolutional, and recurrent neural network models also showed similar performance with coefficients of determination of about 0.94. Support vector regression and support vector machine also provided acceptable results with determination coefficients of 0.9162 and 0.8500.

کلیدواژه‌ها

financial stability efficiency؛ financial stability؛ neural network؛ machine learning