Providing a comprehensive model to measure the liquidity risk of banks admitted to the Tehran Stock Exchange (case study: Bank Mellat)

Document Type : Original Article

Authors

1 PhD Student in Financial-Industrial Management, Kish International Branch, Islamic Azad University, Kish, Iran

2 Professor, Management and Accounting, University of Tehran, Tehran, Iran

3 Assistant Professor, Management and Accounting, Kish International Branch, Islamic Azad University, Kish, Iran

Abstract

Lack of liquidity management of banks is one of the most important risks for any bank and if less attention is paid to liquidity risk, it may lead 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, 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 proposed model. In this paper, we have used Lunberg-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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References

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