Estimation of Input & Output Cash of Tejarat Branches in order to Calculate Branches’ Required Cash Via Multivariate Bayesian Clustering Analysis and the Implementation in Neural Network

Document Type : Research Paper

Authors

1 PhD. Candidate Faculty of Statistics, Campus of Allameh Tabataba'i University, Tehran, Iran

2 Associate Prof., Faculty of Statistics, Allameh Tabataba'i University, Tehran, Iran

Abstract

Cash adequacy in banks’ branches is considered as the significant issues for branch managers; because the daily cash shortage in branches’ funds might lead to the lack of fulfilling customers’ needs. On the other hand, cash surplus in branches will increase the expenses which arise from its transfer to the banks’ treasuries. Therefore, banks have always been attempting to estimate their required cash according to their daily operations and. In this regard, iIn this article, branches of Tejarat Bank, with regard to their diversity, have been classified in similar clusters with the two methods of hierarchical clustering and clustering based on Bayesian approach .Then, based on the results obtained from the clustering, the input cash to the branches as well as the cash consumption in the branches were estimated through the neural networks, which made it possible to calculate the required cash in branches. The results show that the estimation of input and consumed cash of branches using neural network and regarding the results obtained from Bayesian approach for branches clustering enjoys higher precision in comparison to the results obtained from the classic methods of clustering.

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References

 
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Financial Research Journal
Volume 19, Issue 1
2017
Pages 41-60

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