A comparative study of corporate credit rating prediction with machine learning

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dc.authorid0000-0001-7835-7905en_US
dc.authorid0000-0002-1006-0539en_US
dc.contributor.authorDoğan, Seyyide
dc.contributor.authorBüyükkör, Yasin
dc.contributor.authorAtan, Murat
dc.date.accessioned2022-09-12T08:37:26Z
dc.date.available2022-09-12T08:37:26Z
dc.date.issued2022en_US
dc.departmentKMÜ, İktisadi ve İdari Bilimler Fakültesi, Uluslararası Ticaret ve İşletmecilik Bölümü en_US
dc.description.abstractCredit 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.en_US
dc.identifier.citationDoğan, 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/ord220102en_US
dc.identifier.doi10.37190/ord220102
dc.identifier.endpage47en_US
dc.identifier.issn2081-8858
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85134531815
dc.identifier.scopusqualityQ4
dc.identifier.startpage25en_US
dc.identifier.urihttps://doi.org/10.37190/ord220102
dc.identifier.urihttps://hdl.handle.net/11492/6534
dc.identifier.volume32en_US
dc.identifier.wosWOS:000815263300002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.institutionauthorDoğan, Seyyide
dc.institutionauthorBüyükkör, Yasin
dc.language.isoen
dc.publisherWroclaw University of Science and Technologyen_US
dc.relation.journalOperations Research and Decisionsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCredit Ratingsen_US
dc.subjectCredit Risken_US
dc.subjectMachine Learningen_US
dc.titleA comparative study of corporate credit rating prediction with machine learningen_US
dc.typeArticle

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