Mevduat Bankalarının Karlılığının Yapay Sinir Ağları ile Tahmini: Bir Yazılım Modeli Tasarımı

Son yıllarda karlılık analizlerinde; esnek hesaplama (EH) teknikleri, doğrusal olmayan çok değişkenli veri yapısında başarılı uygulamalarından dolayı tercih edilmektedir. Ancak, EH kullanımında karşılaşılan birtakım yetersizlikler nedeniyle, adaptif bir sisteme gereksinim duyulmuştur. Makalenin amacı; aktif karlılığı ve özkaynak karlılığı ile ifade edilen banka karlılığı üzerinde etkisi olan değişkenlerin kullanılmasıyla ve ilk defa geliştirilecek adaptif bir yazılım modeli ile Türkiye'deki mevduat bankalarının karlılığını önemli bir EH tekniği olan yapay sinir ağları ile analiz etmektir. Modelden çıkan sonuçlar, kullanılan değişkenlerin tamamının karlılık üzerinde değişen oranlarda önemli etkisinin olduğunu ve tahminlerin hedeflenen ve kabul edilebilir başarı performansını yakaladığını göstermektedir. Bu başarılı sonuçlarından dolayı ve kullanıcı farklılıklarından etkilenmemesine de bağlı olarak, bu yazılım modelinin; banka karlılığı tahmininde kolaylıklar sağlayacağı düşünülmektedir

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

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

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Kaynak Göster