Estimating Deposit Banks Profitability with Artificial Neural Networks: A Software Model Design

Ferdi Sonmez, Metin Zontul and Sahamet Bulbul

Journal of BRSA Banking and Financial Markets, 2015, vol. 9, issue 1, 9-46

Abstract: In recent years, soft computing (SC) techniques have been preferred to measure bank profitability because of their successful applications in nonlinear multivariate situations. However, an adaptive system was needed due to the insufficient use of application software programs for SC. This paper is intended to measure profitability of deposit banks in Turkey with an adaptive SC software model of artificial neural networks which is developed for the first time and using variables that have impact on profitability. The results from the model indicate that all of the variables used have significant impact, in varying proportions, on profitability and that obtained estimations achieved the targeted and acceptable performance of success. This software model is expected to provide easiness on estimating bank profitability, since giving such successful estimations and not being affected by user differences.

Keywords: Bank Profitability; Turkish Banking Sector; Soft Computing Techniques; Artificial Neural Networks; Multilayer Perceptron; Levenberg Marquardt Back Propagation Algorithm (search for similar items in EconPapers)
JEL-codes: C45 C88 G21 (search for similar items in EconPapers)
Date: 2015
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