TÜRK BANKACILIK SEKTÖRÜNDE FİNANSAL GÜÇ DERECELERİNİN TAHMİNİNDE YAPAY SİNİR AĞLARI VE ÇOK DEĞİŞKENLİ İSTATİSTİKSEL ANALİZ TEKNİKLERİNİN PERFORMANSLARININ KARŞILAŞTIRILMASI

Finansal güç derecelendirmesi (financial strength rating), bir bankanın temel finansal gücünü gösterir. Burada amaçlanan bir bankanın temel finansal gücünün dış faktörlerin değerlendirme harici bırakılması suretiyle ölçülmesidir. Dış faktörler, bankanın faaliyet çevresinden kaynaklanabileceği gibi, koruyucu nitelikteki dış destek mekanizmalarının varlığı ile de bağlantılı olabilir. Yapılan değerlendirme ile bankanın, koruyucu dış faktörlerden tamamen arındırılmış derecelendirmesi nasıl olurdu sorusuna cevap aranır. Ayrıca bu değerlendirmede bankanın finansal temeli, şube ağının gücü, faaliyet alanlarındaki ve varlıklarındaki çeşitlilik incelenir.Bu çalışmada Türk bankalarının finansal güç derecelerini yapay sinir ağları ve çok değişkenli istatistiksek analiz teknikleri kullanarak tahmin etmek amacıyla bir model geliştirilmiştir. Çalışmanın metodolojisi, modelde yer alan değişkenlerin seçilmesi, veri setinin oluşturulması, kullanılacak tekniklerin belirlenmesi ve bu tekniklerin doğru sınıflandırma başarısının değerlendirilmesinden oluşmaktadır. Yapay sinir ağı, modelin elde edildiği veri setinde çok değişkenli istatistiksek analiz teknikleri ne göre yüksek bir sınıflandırma performansı göstermiştir. Modelin geçerliliğinin test edildiği veri setinde ise kullanılan tekniklerin tahmin performansları arasında anlamlı bir fark bulunamamıştır.

Anahtar Kelimeler:

Bankalar, Finansal Güç Derecelendirmesi, Yapay Sinir Ağları, Çok Değişkenli İstatistiksel Analiz Teknikleri

A PERFORMANCE COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORKS AND MULTIVARIATE STATISTICAL METHODS IN FORECASTING FINANCIAL STRENGTH RATING IN TURKISH BANKING SECTOR

Financial strength rating indicates the fundamental financial strength of a bank. The aim of financial strength rating is to measure a bank’s fundamental financial strength excluding the external factors. External factors can stem from the working environment or can be linked with the outside protective support mechanisms. With the evaluation, the rating of a bank free from outside supportive factors is being sought. Also the financial fundamental, franchise value, the variety of assets and working environment of a bank are being evaluated in this context.In this study, a model has been developed in order to predict the financial strength rating of Turkish banks. The methodology of this study is as follows: Selecting variables to be used in the model, creating a data set, choosing the techniques to be used and the evaluation of classification success of the techniques. It is concluded that the artificial neural network system shows a better performance in terms of classification of financial strength rating in comparison to multivariate statistical methods in the raining set. On the other hand, there is no meaningful difference could be found in the validation set in which the prediction performances of the employed techniques are tested.

Keywords:

Banks Financial Strength Rating, , Artificial Neural Networks, Multivariate Statistical Methods,

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