Necip BULUT, Saber SHAKERİ, Seçil YÜZÜK, Mehmet Sıddık AKTAŞ

Öznitelik Seçim Yöntemleri ve Makine Öğrenmesi Kullanarak Şirket Bilanço Verilerine Dayalı İflas Riski Tahmini

Bir şirketin başarısı hem firmanın iç muhatapları hem de yatırımcılar ve üçüncü kişilerce büyük önem taşımaktadır. Finansal olarak başarısızlık kimi zaman iflaslar ile sonuçlanabilmekte ve firmanın muhatapları üzerinde yıkıcı etkiler yaratabilmektedir. Yatırımcılar, finansörler, yöneticiler bazen de politika yapıcıları için firmaların iflas risklerini tahmin etmek oldukça önemlidir. Literatürde iflas riskinin tahmini için birçok yöntem geliştirilse de Ohlson O-skoru ve Altman Z-skoru iflas riskini tahmin için oldukça sık kullanılan iki yöntemdir. Bu iki modelin hem lineer model olmaları hem de firmaların yalnızca son bilançolarıyla ilgilenmeleri bazen hatalı tahminlere yol açabilmektedir. İflas olgusunun bir süreç olduğu düşünüldüğünde şirketin sadece son finansal raporlarının incelenmesi bir takım sakıncalar barındırır. Bu sebeple iflas risklerini doğru tahmin etmek için şirketlerin geçmiş finansal raporlarının da incelenmesi gerekmektedir. Literatürdeki bu iki iflas riski tahmin yöntemi şirketlerin sadece son finansal raporlarıyla ilgilenmektedir. Ayrıca bu iki modelde şirketin başarısına dair karar verilemeyen gri alanlar bulunmaktadır. Bu çalışmada literatürdeki klasik lineer modeller yerine, lineer olmayan makine öğrenmesi algoritmaları kullanılarak şirketlerin iflas riskleri tahmin edilmeye çalışılmıştır. Bu amaç doğrultusunda öznitelik seçim metodu olarak Bilgi Kazanımı ve Temel Bileşenler Analizi, Lineer Diskriminant Analizi ile birleştirilerek ve makine öğrenmesi metodu olarak Lojistik Regresyon, Karar Destek Vektörleri ve Rassal Orman algoritması kullanılmıştır. Bu bağlamda şirketlerin iflas riskini makine öğrenmesi algoritmalarıyla tahmin etmenin, lineer klasik modellerden başarılı olduğu sonucuna ulaşılmıştır.

Anahtar Kelimeler:

Ohlson O-skoru, Altman Z-Skoru, Şirket İflas Riski Tahmini, Öznitelik Çıkarımı, Makine Öğrenmesi

Bankruptcy Risk Forecasting Based on Company Balance Sheet Data Using Feature Selection Methods And Machine Learning

The success of a company has a significant issue for both the interlocutors of companies and other related persons. Financial failure sometimes end up bankrupt and can have a critical effect on the company’s interlocutors. Prediction of bankruptcy is significant for investors, backers, directors and sometimes policymakers. Although there are a lot of models to predict bankruptcy in the financial literature, Ohlson O-score and Altman Z-score are models that are used quite often. The fact that these two models are both linear models and companies are only interested in their latest balance sheets can sometimes lead to incorrect predictions. Considering bankruptcy as a process, to interest in only the latest financial reports of the companies has some drawbacks. For this reason, in addition to the current, previously financial reports of companies should be interested to predict bankruptcy risk of the company correctly. In the literature, these two classical models interest in only the current financial reports of companies. Additionally, there are grey areas that are not decided about the bankruptcy of companies in these two classical models. In this study, it is tried to predict the bankruptcy risk of companies by using non-linear machine learning algorithms rather than classical linear models in the financial literature. In line with this main purpose, as feature selection methods Information Gain, Principle Component Analysis algorithms by combining Linear Discrimination Analysis algorithm and as machine learning methods Logistic Regression, Support Vector Machine, and Random Forest algorithms are used. It has been found that predicting the bankruptcy risk of companies by using non-linear machine learning algorithms is more successful than linear classical models.

Keywords:

Ohlson O-score, Altman Z-score, Bankruptcy Risk Prediction, Feature Extraction, Machine Learning,

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Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi-Cover

ISSN: 1305-8991
Başlangıç: 2005
Yayıncı: Türkiye Bilişim Vakfı

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