THE USE OF MACHINE LEARNING IN PREDICTING FINANCIAL FAILURE IN BUSINESSES: ISTANBUL STOCK EXCHANGE APPLICATION

Year 2023, , 564 - 580, 09.10.2023

İsmail Fatih Ceyhan

https://doi.org/10.29106/fesa.1359358

Abstract

The aim of the study is to estimate the risk of financial failure of enterprises by using machine learning, one of the artificial intelligence techniques. In this context, machine learning methods NaiveBayes, J48, RandomForest, LinearRegression, RandomTree were used with 43 financial ratios obtained from the financial statements of 14 companies in Borsa Istanbul Main Market and 14 companies in Borsa Istanbul Watchlist Market for the year 2022. With the data obtained using the financial statements of the companies, it is investigated which of the machine learning application models provides better classification accuracy. In addition, it was tested whether it is possible to predict the financial situation of a company in the close monitoring market in 2022 for the following year with machine learning. It was concluded that the highest classification accuracy rate was achieved by applying the RandomForest algorithm and 10-fold cross-validation technique together, while the NaiveBayes algorithm and 10-fold cross-validation technique achieved a very high rate of success in the prediction model for a single year.

Keywords

Classification, Financial Distress, Financial Failure, Machine Learning, Prediction

İŞLETMELERDE FİNANSAL BAŞARISIZLIK ÖNGÖRÜSÜNDE MAKİNE ÖĞRENMESİNİN KULLANIMI: BİST UYGULAMASI

Abstract

Çalışmanın amacı işletmelerin finansal başarısızlık riski ile ilgili tahmin yapay zekâ tekniklerinden makine öğrenmesi kullanılarak yapılmasıdır. Bu kapsamda, Borsa İstanbul Ulusal Pazar’da yer alan 14 firma ile Borsa İstanbul Yakın İzleme Pazarı’nda yer alan14 firmanın 2022 yılı 12 aylık gelir tabloları ve bilançolarından elde edilen 43 adet finansal oran kullanılmış makine öğrenmesi yöntemlerinden NaiveBayes, J48, RandomForest, LinearRegression, RandomTree kullanılmıştır. Şirketlerin mali tabloları kullanılarak elde edilen veriler ile, makine öğrenmesi uygulama modellerinden hangisinin daha iyi sınıflandırma doğruluğu sağladığı araştırılmıştır. Ayrıca 2022 yılında yakın izleme pazarında yer alan bir şirketin bir sonraki sene için finansal durumunun makine öğrenmesi ile öngörüsünün mümkün olup olmadığı test edilmiştir. En yüksek sınıflandırma doğruluğu oranına RandomForest algoritması ve 10 kat çapraz doğrulama tekniğinin birlikte uygulanması ile ulaşıldığı, tek yıl için yapılan öngörü modelinde ise NaiveBayes algoritması ve 10 kat çapraz doğrulama tekniğinin çok yüksek bir oranda başarı sağladığı sonuçlarına ulaşılmıştır.

Keywords

Sınıflandırma, Finansal Sıkıntı, Finansal Başarısızlık, Makine Öğrenmesi, Tahmin

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