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2014 (Vol. 7, Issue: 3)
Article Information:

Forecasting Bank Deposits Rate: Application of ARIMA and Artificial Neural Networks

Morteza Cheshti, Mohammad Taher Ahmadi Shadmehri and Hamid Safaye Nikoo
Corresponding Author:  Morteza Cheshti 

Key words:  ARIMA models, deposit, forecasting, neural networks, , ,
Vol. 7 , (3): 527-532
Submitted Accepted Published
February 11, 2013 March 14, 2013 January 20, 2014
Abstract:

In this study application of ARIMA and Artificial Neural Networks for Forecasting Bank Deposits Rate is investigated. As itís observed nowadays, banking industry is faced with great competition. The number of banks and use of new tools especially electronic banking and development of Islamic banking have maximized this competition and turned intelligent management of banks into a critical issue. Foundation of banks is based on attracting deposits; hence, forecasting the deposits has a great importance for banks. This study seeks to forecast the bank deposits. To do this, we have used the ARIMA methods with emphasis on the Box-Jenkins method as well as the Artificial Neural Network. The monthly data of different branches was used in this study for an eight-year period. This study examined the hypothesis that neural networks are more accurate than ARIMA models in forecasting the bank deposits. Research results indicate that although both models have a high capacity to forecast the variables, generally the neural network models present better results and it is better to use this method for forecasting. The neural network method has a relative advantage as R2 is 16% in ARIMA Method and 99% in Neural network Method. Also RMSE is 170985 and 176960 for ARIMA Method and Neural network Method respectively.
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  Cite this Reference:
Morteza Cheshti, Mohammad Taher Ahmadi Shadmehri and Hamid Safaye Nikoo, 2014. Forecasting Bank Deposits Rate: Application of ARIMA and Artificial Neural Networks.  Research Journal of Applied Sciences, Engineering and Technology, 7(3): 527-532.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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