A comparative study of corporate credit ratings prediction with machine learning

IF 0.7 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Seyyide Doğan, Yasin Büyükkör, Murat Atan
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引用次数: 1

Abstract

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.
企业信用评级预测与机器学习的比较研究
信用评分对金融部门投资者和政府官员至关重要,因此开发可靠、透明和适当的评级工具非常重要。本研究旨在用机器学习和现代统计方法预测公司信用评分,包括部门和汇总数据。该研究分析了1881家向土耳其最大的公共银行申请贷款的公司,这些公司分布在三个不同的行业。实验结果在分类准确率、灵敏度、特异度、精密度和马修斯相关系数等方面进行了比较。在按部门估计信用等级时,可以观察到分类率有很大变化。从分析结果来看,对于所有数据集,逻辑回归分析、支持向量机、随机森林和XGBoost的性能都优于决策树和k近邻。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Operations Research and Decisions
Operations Research and Decisions OPERATIONS RESEARCH & MANAGEMENT SCIENCE-
CiteScore
1.00
自引率
25.00%
发文量
16
审稿时长
15 weeks
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