Banka Başarısızlıklarının Düzeltilmiş Minimum Sapma Modeli ile Tahmnin Edilmesi

Banka başarısızlıklarının tahmini kadar, başarılı ile başarısızı birbirinden ayıran özelliklerin tespiti de finans alanında önemli bir araştırma konusu olmuştur. Başarlı ile başarısızı birbirinden ayırt etmede kullanılacak özelliklerin seçimi için faktör ve diskriminant analizlerini kullanan bir süreç tasarlamak olanaklı olmakla beraber, böyle bir sürecin başta bilgi kaybı olmak üzere birçok sakıncasından bahsetmek olanaklıdır. Bu çalışmada bu tahmin sürecinin sakıncalarını giderebilmek amacıyla, matematiksel programlama tabanlı diskriminant analizi amacıyla kullanılan minimum sapma modeline yeni kısıtlar eklenmek suretiyle, modelin tahminde kullanacağı özellikleri kendisinin seçmesi sağlanmış ve yukarıda bahsedilen birden fazla aşamalı süreç tek bir model ile ikame edilmiştir. Geliştirilen model 39 özel sermayeli Türk bankasının 1994-2001 verileri kullanılarak test edilmiş, modelin tahmin performansı ve geçerliliği iki aşamalı süreç sonuçlarıyla karşılaştırmalı olarak ortaya konmuştur.

Modified Minimum Sum of Deviations and Bank Failure Prediction

In discriminant analysis which is one of the widely used failure prediction techniques, a group of observations whose memberships are already identified, are used for the measurement of weight estimates of a function by minimizing their group misclassifications. Since the 1980's a group of researchers have studied developing non parametric discriminant methods. Non parametric models provide the analyst with the opportunity to add new conditions (constraints) to the model. In this paper a new non parametric discriminant model is proposed. This new model which is a modified version of the well known minimum deviation model, helps the analyst by choosing the optimal variables to predict the discriminant function. Using the data set of Turkish commercial banks for the period of 1994-2001, the modified model is tested, the validity and the prediction performance are compared with a two stage prediction process that employs both factor and discriminant analysis.

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