Estimating cash in bank branches by time series and neural network approaches - Pejman Peykani; Farzad Eshghi; Alireza Jandaghian; Hamed Farrokhi-Asl; Farid Tondnevis (Big Data and Computing Visions)
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.