A comparative study of corporate credit rating prediction with machine learning
Yükleniyor...
Dosyalar
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wroclaw University of Science and Technology
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Credit scores are critical for financial sector investors and government officials, so it is important to develop reliable, transparent and appropriate tools for obtaining ratings. This study aims to predict company credit scores with machine learning and modern statistical methods, both in sectoral and aggregated data. Analyses are made on 1881 companies operating in three different sectors that applied for loans from Turkey's largest public bank. The results of the experiment are compared in terms of classification accuracy, sensitivity, specificity, precision and Mathews correlation coefficient. When the credit ratings are estimated on a sectoral basis, it is observed that the classification rate considerably changes. Considering the analysis results, it is seen that logistic regression analysis, support vector machines, random forest and XGBoost have better performance than decision tree and k-nearest neighbour for all data sets.
Açıklama
Anahtar Kelimeler
Credit Ratings, Credit Risk, Machine Learning
Kaynak
WoS Q Değeri
N/A
Scopus Q Değeri
Q4
Cilt
32
Sayı
1
Künye
Doğan, S., Büyükkör, Y., & Atan, M. (2022). A comparatıve study of corporate credıt ratıng predıctıon wıth machıne learning. Operations Research and Decisions, 32(1), 25-47. doi:10.37190/ord220102
