Presenting a Comprehensive Model for Measuring the Liquidity Risk of Banks Listed on the Tehran Stock Exchange (Case Study: Mellat Bank)
Subject Areas : Financial EconomicsToraj Azari 1 , Mojtaba Tastori 2 , Reza Tehrani 3
1 - Management and Accounting, Kish International Unit, Islamic Azad University, Tehran, Iran
2 - Management and Accounting, Kish International Unit, Islamic Azad University, Tehran, Iran
3 - Management and Accounting, Kish International Unit, Islamic Azad University, Tehran,
Keywords: Machine Learning, Artificial Neural Network, Banking industry, C45, G24, D83, C11, Keywords: Liquidity Risk, Bayesian Network JEL Classification: G32,
Abstract :
AbstractLack of liquidity management of banks is one of the most important risks for any bank and lack of attention to liquidity risk leads to irreparable consequences. Preventing liquidity risk requires a comprehensive measurement method but liquidity risk is complicated issue, and this complexity makes it difficult to provide a proper definition. In addition, defining liquidity risk determinants and formulation of the related objective function to measurement its value is a difficult task. To address these problems and assess liquidity risk and its key factors, in this study we propose a model that uses artificial neural networks and Bayesian networks. Design and implementation of this model includes several algorithms and experiments to validate the model. In this paper, we have used Levenberg-Marquardt and Genetic optimization algorithms to teach artificial neural networks. We have also implemented a case study in Bank Mellat to demonstrate the feasibility, efficiency, accuracy and flexibility of the research liquidity risk measurement model.
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Bassey, G. E. & Moses, C. E., 2015, Bank Profitability and Liquidity Management: A Case Study of Selected Nigerian Deposit Money Banks, University Of Uyo, International Journal of Economics, Commerce and Management, Vol. 3, Issue 4, pp.1-24.
Chen, Y.K, Shen, H.H, Kao, L., 2018, Bank Liquidity Risk and Performance. Review of Pacific Basin Financial Markets and Policies., 21(1).
Crouhy, M., Galai, D., Mark, A., 2000, A Comparative Analysis of Current Credit Risk Models, Journal of Banking and Finance, Vol. 24, No. 1-2, P 59-117.
Drehmann, M., Nikolaou, K., 2009, Funding Liquidity Risk: Definition and Measurement, European Central Bank, Working Paper Series March, No. 1024.
Jobst, A. A., 2014, Measuring Systemic Risk-adjusted Liquidity (SRL): A Model Approach, J. Bank. Financ, 45 270–287.
Konovalova, N., and Zarembo, J., 2015, Imbalanced Liquidity Risk Management: Evidence from Latvian and Lithuanian Commercial Banks, Copernican Journal of Finance and accounting, vol4, No 1.
Marz , L., Neu, p., 2007, Scenario Analysis and Stress Testing, in Liquidity Risk Measurement and Management, John Wiley & Sons Inc, New Jersey, pp. 37–64 .
Musakwa, F.T, 2013, Measuring Bank Funding Liquidity Risk, Retrieved from Actuaries.org, 10.
Papadamou, S., Sogiakas, D., Sogiakas, V., Toudas, K. 2021, The Prudential Role of Basel III liquidity Provisions towards Financial Stability, Journal of forcasting.
Rahman, M. L., & Banna, S. H., 2016, Liquidity Risk Management: A Comparative Study between Conventional and Islamic Banks in Bangladesh. Journal of Business and Technology (Dhaka), 10(2), 18-35.
Sabri Mohammad, Mehmet Asutay, Rob Dixon, Elena Platonova., 2020, Liquidity Risk Exposure and Its Determinants in the Banking Sector: A Comparative Analysis Between Islamic, Conventional and Hybrid Banks. Journal of International Financial Markets, Institutions and Money. Volume 66.
Scannella, E., 2016, Theory and Regulation of Liquidity Risk Management in Banking, International Journal of Risk Assessment and Management, Vol.19 No.1/2, pp.4 – 21.
Tripe, D., 1999, Liquidity Risk in Banks – A New Zealand Perspective, New Zealand, Massey University, 42.
Vento, G.A., La Ganga, P., 2009, Bank Liquidity Risk Management and Supervision: Which Lessons from Recent Market Turmoil, J. Money Invest Bank, 10, 78–125.