Finansal Başarısızlığın Tahmini: Gelişmiş ve Gelişmekte Olan Ülkelerdeki Halka Açık Şirketlerden Ampirik Kanıtlar

Year 2025, , 107 - 126, 28.03.2025

Yavuz Gül , Serpil Altınırmak

https://doi.org/10.30784/epfad.1595915

Abstract

Çalışmada yüksek doğruluğa sahip finansal başarısızlık tahmin modelleri oluşturmak üzere gelişmiş ve gelişen ülkelerden 570 şirket 2010 – 2019 dönemi için analiz edilmektedir. Bu çerçevede, lojistik regresyon (LR), yapay sinir ağları (YSA) ve karar ağaçları (KA) uygulanmış ve bahsedilen yöntemlerin sınıflandırma doğrulukları karşılaştırılmıştır. 16 finansal oran bağımsız değişken olarak kullanılmış ve YSA en doğru tahmin sonuçlarını üreterek başarısızlık tahmininde diğer yöntemlere üstünlük sağlamıştır. Diğer bir ifadeyle, YSA başarısızlıktan bir yıl öncesi için %98,1 sınıflama doğruluğu üretirken, LR ve KA sırasıyla %94,7 ve %96,1 doğruluk oranlarına ulaşmışlardır. Buna ek olarak, ampirik sonuçlara göre başarısızlıktan iki yıl öncesi için ANN %92,5, KA %91,1 ve LR %84,4 sınıflama doğruluğu sağlamışlardır. Mevcut çalışmanın bulguları finansal başarısızlık tahminine yönelik ışık tutmaktadır ve YSA yönteminin kullanılmasının daha efektif olabileceğini işaret ederek, özellikle yatırımcılar ve düzenleyici otoriteler gibi paydaşlar açısından pratik sonuçlar ortaya koymaktadır.

Keywords

Finansal Başarısızlık, Lojistik Regresyon, Yapay Sinir Ağları, Karar Ağaçları

Predicting Financial Failure: Empirical Evidence from Publicly – Quoted Firms in Developed and Developing Countries

Abstract

This paper analyzes the data of 570 firms from developed and developing countries between 2010 and 2019 in an attempt to create high–accuracy financial failure prediction models. In this sense, we utilize three different methods, namely logistic regression (LR), artificial neural networks (ANN), and decision trees (DT), and compare the classification accuracy performances of these techniques. Using 16 financial ratios as independent variables, ANN is able to generate the most accurate prediction and outperforms the other methods in predicting failure. Otherwise said, ANN yields a correct classification accuracy of 98.1% one year prior to failure while LR and DT achieve accuracy rates of 94.7% and 96.1%, respectively. Furthermore, the empirical results demonstrate that the classification accuracy rate reaches 92.5% by ANN, 91.1% by DT, and 84.4% by logistic regression two years in advance. The findings of current research provide valuable insights into financial failure prediction and may entice practical implications for stakeholders, especially investors and regulatory bodies, by indicating that the use of the ANN approach may be more effective.

Keywords

Financial Failure, Logistic Regression, Artificial Neural Networks, Decision Trees

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