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Journals / Bankacılar / 2015 / Cilt: 26 - Sayı: 94

An Eclectic Approach to the Prediction of Bank Bankruptcies

Banka İflaslarının Öngörülmesinde Eklektik Bir Yaklaşım

SONER AKKOÇ
NİLÜFER DALKILIÇ
AYŞEN ALTUN ADA
Pages
25–39
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results obtained show that artificial neural networks can be used in predicting bank failures and logistic regression analysis is the best method for selecting variables.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Developing prediction models and determining which variables among many others will be used in modelling are very important for the prediction of bank failures, which is a significant issue for both national economies and investors.
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Key Findings & Data:This study first identifies the highly representative variables via discriminant analysis, logistic regression and principal component analysis methods for the evaluation of the failure risk of the Turkish banking sector.
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Conclusion & Impact:Results obtained show that artificial neural networks can be used in predicting bank failures and logistic regression analysis is the best method for selecting variables.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Developing prediction models and determining which variables among many others will be used in modelling are very important for the prediction of bank failures, which is a significant issue for both national economies and investors. This study first identifies the highly representative variables via discriminant analysis, logistic regression and principal component analysis methods for the evaluation of the failure risk of the Turkish banking sector. Then, it aims to develop prediction models for failure predictions by using artificial neural networks. Results obtained show that artificial neural networks can be used in predicting bank failures and logistic regression analysis is the best method for selecting variables.

Özet

Gerek ülke ekonomileri gerekse yatırımcılar açısından büyük önem taşıyan banka iflaslarının öngörüsünde, öngörü modellerinin geliştirilmesi ve birçok değişken arasından hangisinin modellemede kullanılacağı önem arz etmektedir. Bu çalışma, Türk bankacılık sektörünün iflas riskinin değerlendirilmesinde, diskriminant analizi, lojistik regresyon ve temel bileşen analizi yöntemleri ile temsil gücü yüksek değişkenler belirlendikten sonra, iflas öngörüsünde yapay sinir ağları kullanarak öngörü modelleri geliştirmeyi amaçlamaktadır. Elde edilen sonuçlar, yapay sinir ağlarının, banka iflas tahmininde etkin olarak kullanılabildiğini ve lojistik regresyon analizinin en iyi değişken seçen yöntem olduğunu göstermektedir.

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Cite This Article

AKKOÇ, S., DALKILIÇ, N., ADA, A. A. (2015). An Eclectic Approach to the Prediction of Bank Bankruptcies. Bankacılar, Cilt: 26 - Sayı: 94, 25–39. https://ajindex.com/articles/148944

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