Document Type : Original Article
1 Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
2 Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3 Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.
4 Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA.
5 Department of Management, University of Tehran, Tehran, Iran.
Providing efficient and powerful approach for liquidity management of bank branches has always been one of the most important and challenging issues for researchers and scholars in the banking field. In other words, estimating the amount of required cash in different branches of the bank is one of the basic and important questions for managers of the banking system. Because on the one hand, if the amount of cash is less than the required amount, the bank runs the default risk, and on the other hand, if the amount of cash is more than the required amount, the bank incurs opportunity costs. Therefore, the purpose of this study is to provide a practical approach to predict the optimal amount of required cash in bank branches. For this purpose, the concepts of time series, neural network approach and vector autoregressive model are used. The effectiveness of the proposed approach is also examined using real data.
- Santos, J. A. (2001). Bank capital regulation in contemporary banking theory: a review of the literature. Financial markets, institutions & instruments, 10(2), 41-84.
- Demyanyk, Y., & Hasan, I. (2010). Financial crises and bank failures: a review of prediction methods. Omega, 38(5), 315-324.
- Balthazar, L. (2006). From Basel 1 to Basel 3: The integration of state-of-the-art risk modelling in banking regulation. Springer.
- Herring, R. J. (2002). The basel 2 approach to bank operational risk: regulation on the wrong track. The journal of risk finance, 4(1), 42-45.
- King, P., & Tarbert, H. (2011). Basel III: an overview. Banking & financial services policy report, 30(5), 1-18.
- Zaragoza, M. A., & Mota, I. F. D. L. (2016). Tactics for approaching cash optimisation in bank branches. International journal of simulation and process modelling, 11(6), 492-503.
- Baghbani, G., & Eskandari, F. (2018). Calculating the required cash in bank branches: a Bayesian-data mining approach. Neural computing and applications, 30(9), 2831-2841.
- Bilir, C., & Döşeyen, A. (2018). Optimization of ATM and branch cash operations using an integrated cash requirement forecasting and cash optimization model. Business & management studies: an international journal, 6(1), 237-255.
- Lázaro, J. L., Jiménez, Á. B., & Takeda, A. (2018). Improving cash logistics in bank branches by coupling machine learning and robust optimization. Expert systems with applications, 92, 236-255.
- Tavana, M., Abtahi, A. R., Di Caprio, D., & Poortarigh, M. (2018). An Artificial Neural Network and Bayesian Network model for liquidity risk assessment in banking. Neurocomputing, 275, 2525-2554.
- Desai, A. P., Bobade, S. R., Baravkar, K. K., Nerkar, Y. A., Ankleshwar, M., & Navale, M. P. (2019). A survey on identifying operational ATM and optimization of cash management using mobile app. International journal of innovative science and research technology, 4(1), 279-281.
- Ranjbarfard, M., & Ahmadi, S. (2020). A study of data requirements for data mining applications in banking. Journal of Digital Information Management, 18(3), 109-117.
- Cabello, J. G. (2013). Cash efficiency for bank branches. Springer Plus, 2(1), 1-15.
- Tam, K. Y. (1991). Neural network models and the prediction of bank bankruptcy. Omega, 19(5), 429-445.
- Charnes, A., Cooper, W., Lewin, A. Y., & Seiford, L. M. (1997). Data envelopment analysis theory, methodology and applications. Journal of the operational research society, 48(3), 332-333.
- Emrouznejad, A., & Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-economic planning sciences, 61, 4-8.
- An, Q., Tao, X., & Xiong, B. (2021). Benchmarking with data envelopment analysis: an agency perspective. Omega, 101, 102235. https://doi.org/10.1016/j.omega.2020.102235
- Peykani, P., Farzipoor Saen, R., Seyed Esmaeili, F. S., & Gheidar‐Kheljani, J. (2021). Window data envelopment analysis approach: a review and bibliometric analysis. Expert systems, 38(7), e12721. https://doi.org/10.1111/exsy.12721
- Olesen, O. B., & Petersen, N. C. (2016). Stochastic data envelopment analysis—a review. European journal of operational research, 251(1), 2-21.
- Peykani, P., Mohammadi, E., Saen, R. F., Sadjadi, S. J., & Rostamy‐Malkhalifeh, M. (2020). Data envelopment analysis and robust optimization: a review. Expert systems, 37(4), e12534. https://doi.org/10.1111/exsy.12534
- Peykani, P., Mohammadi, E., Jabbarzadeh, A., Rostamy-Malkhalifeh, M., & Pishvaee, M. S. (2020). A novel two-phase robust portfolio selection and optimization approach under uncertainty: a case study of Tehran stock exchange. Plos one, 15(10), e0239810.
- Banker, R. D. (2021). Stochastic data envelopment analysis. Data envelopment analysis journal, 5(2), 281-309.
- Peykani, P., Mohammadi, E., & Emrouznejad, A. (2021). An adjustable fuzzy chance-constrained network DEA approach with application to ranking investment firms. Expert systems with applications, 166, 113938.
- Peykani, P., Hosseinzadeh Lotfi, F., Sadjadi, S. J., Ebrahimnejad, A., & Mohammadi, E. (2021). Fuzzy chance-constrained data envelopment analysis: a structured literature review, current trends, and future directions. Fuzzy optimization and decision making, 1-65. https://link.springer.com/article/10.1007/s10700-021-09364-x
- Zahedi-Seresht, M., Khosravi, S., Jablonsky, J., & Zykova, P. (2021). A data envelopment analysis model for performance evaluation and ranking of DMUs with alternative scenarios. Computers & industrial engineering, 152, 107002. https://doi.org/10.1016/j.cie.2020.107002