An artificial neural network model for predicting the liquidity risk of Iranian private banks | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 8، دوره 14، شماره 9، آذر 2023، صفحه 127-136 اصل مقاله (703.41 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.29118.4071 | ||
| نویسندگان | ||
| Mahdi Khosroyani؛ Farzaneh Heidarpoor* ؛ Ahmad Yaghoob-nazhad؛ Zahra Pourzamani | ||
| Department of Accounting, Central Tehran Branch, Islamic Azad University, Tehran, Iran. | ||
| چکیده | ||
| A highly significant financial risk is liquidity risk. Liquidity risk management is a substantial part of Basel Recommendation no. three; with regard to the importance of this risk, this recommendation directs banks to develop and implement appropriate information systems for measuring, predicting, and controlling liquidity risks. Based on its structure, size, and features, each bank manages liquidity risk using different tools and methods. This study investigated the effectiveness of artificial neural networks in predicting liquidity risk in private Iranian banks. Relying on past studies and employing accounting information, this research developed a specific structure and architecture for a multilayer perceptron neural network; then, it predicted the liquidity risk of Iranian private banks from 2009 to 2019 using neural networks plus Matlab software. The research results revealed that artificial neural networks can be used to predict liquidity risk in private Iranian banks. | ||
| کلیدواژهها | ||
| Keywords: Modelling؛ Artificial Neural Networks؛ Liquidity Risk؛ Accounting Indicators؛ Private Iranian Banks | ||
| مراجع | ||
|
| ||
|
آمار تعداد مشاهده مقاله: 17,323 تعداد دریافت فایل اصل مقاله: 10,989 |
||
| تعداد نشریات | 22 |
| تعداد شمارهها | 723 |
| تعداد مقالات | 10,396 |
| تعداد مشاهده مقاله | 72,863,569 |
| تعداد دریافت فایل اصل مقاله | 64,556,951 |