An artificial neural network model for predicting the liquidity risk of Iranian private banks

Document Type : Research Paper


Department of Accounting, Central Tehran Branch, Islamic Azad University, Tehran, Iran.


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.    


Articles in Press, Corrected Proof
Available Online from 30 December 2022
  • Receive Date: 23 April 2022
  • Revise Date: 13 June 2022
  • Accept Date: 28 July 2022
  • First Publish Date: 30 December 2022