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

[1] M. Ansar, Kh. Waleh Zaghard, M. Taqvi and Z. Amirhosseini, Financial strength modeling based on risk and performance using bank accounting items, J. Financ. Account. Audit Res. 13 (2022), no. 4, 185–209.

[2] H.M.K. AlDuhaidahawi, A. Jing, Z.S.M. Sebai and S. Abdullah Harjan, An efficient model for financial risks assessment based on artificial neural networks, J. Southwest Jiaotong Univer. 55 (2020), no. 33, 1–10.

[3] M.R. Asghari Oskoui, The use of neural networks in forecasting time series, Iran. Econ. Res. Quart. 12 (2008), 71–97.

[4] M. Barzandeh and R. Hosseini, Income on risk management and concepts related to it, Keshavarzi Bank Pub. 2 (2001), 125–139.

[5] Basel Committee on Banking Supervision,’ Basel III: A global regulatory framework for more resilient banks and banking systems’, Basel, Switzerland, Bank for International Settlements, December (rev June 2011), 2011.

[6] Communicating the minimum requirements for liquidity risk management of credit institutions, Central Bank of I.R.I., Circular No. 96/251081 dated 09/08/2016.

[7] R. Che Mamat, A. Ramli, M.B.H. Che Omar, A.M. Samad and S.A. Solaiman, Application of machine learning for predicting ground surface settlement beneath road embankments, Int. J. Nonlinear Anal. Appl. 12 (2021), 1025–1034.

[8] M. Dasineh, Effect of criteria based on accounting of profit characteristics on adverse profit risk, Financ. Account. Audit Res. 41 (2018), 153–176. 

[9] A. Diwandari, K. Louks and S.R. Mousavi, Designing a forecasting model in the liquidity management of financial

institutions in the framework of the usury-free banking system using artificial neural networks, Manag. Perspect. Mag. 11 (2013), 23–58.

[10] F. Faramarzi, M. Lashkari and S. Bafande Imandoost, Liquidity risk modeling using neural networks, Int. Res. Conf. Manag. Econ. Account., 2016.

[11] S.M.R. Hashemi, H. Hossanpour, E. Kozegar and T. Tan, Cystoscopy Image Classification Based on Combining MLP and GA, Int. J. Nonlinear Anal. Appl. 11 (2020), no. 1, 93–105.

[12] S.M.R. Hashemi, H. Hossanpour, E. Kozegar and T. Tan, Cystoscopy image classification using deep convolutional neural networks, Int. J. Nonlinear Anal. Appl. 10 (2019), no. 1, 193–215.

[13] B. Joel, Risk Management in Banking, New York, John Wiley & Sons, 2002.

[14] S.M.A. Kafaei and M. Rahzani, Investigating the impact of macroeconomic variables on liquidity risk, J. Econ. Res. Polic. 81 (2016), 261–310.

[15] Z. Khoshnoud, T. Akbari Alashti and R. Khansari, Designing a new guideline for liquidity risk management in the banking system of Iran, Tehran, Research Institute of Money and Banking, 2013.

[16] M. Khosroyani and F. Heydarpoor, Modeling to predict the liquidity risk of Iran's government banks using artificial neural networks and accounting Indicators, Financ. Account. Audit. Res. 14 (2022), no. 55, 163–180.

[17] A. Komijani and J. Saadatfar, Application of artificial neural network models in predicting economic bankruptcy of the registered corporations in stock market, J. Iran. Econ. Ess. 3 (2006), no. 6, 11–44.

[18] M. Lili Dost, Liquidity risk measurement and its relationship with profitability, Master’s thesis, Tehran, Iran’s Higher Institute of Banking, 2016.

[19] J.A. Lopez, What is liquidity Risk?, FRBSF Economic Letter, issue oct24, Federal Reserve Bank of San Francisco, 2008.

[20] L. Matz, Scenario analysis and stress testing, L. Matz, P. Neu (Eds.), Liquidity Risk Measurement and Management, John Wiley & Sons Inc, New Jersey, 2007, pp. 37-64.

[21] N. Mishraz, S. Ashok and D. Tandon, Predicting Financial Distress in the Indian Banking Sector: A Comparative Study between the Logistic Regression, LDA and ANN Models, Global Business Review, New Delhi, 2021.

[22] B. Ostadi and P. Pezhedani Pajoh, Presenting a model for measuring the relationship between financial risks and financial ratios, Empir. Stud. Financ. Account. 16 (2019), no. 63, 109–127.

[23] A. Parsaian, A.R. Shirani and M. Ahmadi, Modern Banking in Theory and Practice, second edition, Sepah Bank, 2006.

[24] R. Raee and A. Saeedi, Fundamentals of Financial Engineering and Risk Management, Publications of Organization for the Study and Compilation of Humanities Books of Universities, 2015.

[25] M. Talebi and N. Shirzadi, Credit Risk: Measurement and Management, Tehran, Organization for Studying and Compiling Humanities Books of Universities, 2012.

[26] M. Tavana, A. R. Abtahi, Di Caprio and M. Poortarigh, An artificial neural network and Bayesian network model for liquidity risk assessment in banking, Neurocomputing 275 (2018), 2525–2554.

[27] G. Teles, J. J.P.C.Rodringues, R.A.L. Rabelo and S.A. Kozlov, Artificial neural network and Bayesian network models for credit risk prediction, J. Artif. Intel. Syst. 2 (2020), no. 1, 118–132.

[28] A. Thaghafi and V. Saif, Identification and measurement of financial ratios and economic variables affecting the health and stability of the banking system in Iran, Econ. Res. J. 5 (2014), no. 17, 65–111.

[29] M. Valipour, M. Kargosha, Using accounting ratios in predicting systematic risk using artificial neural networks model, Sec. Nat. Conf. Account. Manag. Econ., Iran, 2014.

[30] W. Yan and Y. Song, Intelligent evaluation and early warning of liquidity risk of commercial banks based on RNN, Comput. Intel. Neurosci. 2022 (2022), 1–12.