Taghi Ebrahimi Salary; Seyed Mohammad Fahimifard; Hanif Kheirkhah
Abstract
Abstract
In this research the comparative prediction of Iran's banking system (included 14 banks) was carried out by using econometric and artificial neural network models. Accordingly, at first, by using the Kohonen neural network model, the considered banks were divided into two categories of high ...
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Abstract
In this research the comparative prediction of Iran's banking system (included 14 banks) was carried out by using econometric and artificial neural network models. Accordingly, at first, by using the Kohonen neural network model, the considered banks were divided into two categories of high performance and low performance groups and then using the output of Kohonen neural network model, financial proportions and Panel Data econometric model, the performance of Iran's banking system was estimated for the period 2004-2010 and finally by using models evaluation criteria, the performance of Panel Data and ANN models was compared.
The results of Kohonen neural network model indicated that from 14 considered bank, 4 banks belong to high performance group and 10 banks are belong to low performance group. Also the results of Panal Data estimations showed that “capital income/total income "portion has the lowest and “cash/total deposits", has the haighes effect on the Iran's banking system. Finally the results of models comparison stated that the ANN model outperforms the Panel Data model to predict the performance of Iran's banking system.
