 A comparative study of corporate credit ratings prediction with machine learningSeyyide Doğan (), Yasin Büyükkör () and Murat Atan () Operations Research and Decisions, 2022, vol. 32, issue 1, 25-47 Abstract: Credit scores are critical for financial sector investors and government officials, it is important to develop reliable, transparent and appropriate tools for obtaining ratings. The aim of this study is to predict company credit scores with machine learning and modern statistical methods, both in sectoral and aggregated data. Analyzes 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, specivity, precision, and Mathews correlation coefficient. When credit ratings are estimated on sectoral basis, it is observed that the classification rate changes considerably. 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. Keywords: credit ratings; credit risk; machine learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wut:journl:v:32:y:2022:i:1:p:25-47:id:2643 DOI: 10.37190/ord220102 Access Statistics for this article More articles in Operations Research and Decisions from Wroclaw University of Science and Technology, Faculty of Management Contact information at EDIRC. |
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