Handling outliers in bankruptcy prediction models based on logistic regression

IF 0.4 Q4 BUSINESS, FINANCE Public Finance Quarterly-Hungary Pub Date : 2023-09-29 DOI:10.35551/pfq_2023_3_5
Tünde Katalin Szántó
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Abstract

The primary tool for managing bank default risk is the credit rating of potential customers. The focus of the present study is on the logistic regression method used to construct 95% of the lender scorecards. The aim of the research is to determine how much the treatment of outliers improves the classification accuracy of the models when using a method that is highly sensitive to outliers, and which method of treating outliers results in the highest classification accuracy. Furthermore, what criteria should be used to determine the cut-off value of the models for a sample that does not contain solvent and insolvent businesses in equal proportions. The analysis was carried out on a sample of 1677 construction companies. The results show that the treatment of outliers significantly improves the predictive ability of the models, while the replacement of outliers with the closest non-outlier proved to be the most effective for treating outliers. When determining the cut-off, it is inappropriate to use the value that results in the highest classification accuracy, as this may lead to an increase in the proportion of first-order errors. The optimisation of this value may depend on the degree of credit risk taken by a given financial institution in its portfolio of loans.
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基于逻辑回归的破产预测模型异常值处理
管理银行违约风险的主要工具是对潜在客户的信用评级。本研究的重点是用于构建95%的贷方记分卡的逻辑回归方法。研究的目的是确定在使用对离群值高度敏感的方法时,对离群值的处理在多大程度上提高了模型的分类精度,以及哪种处理离群值的方法可以获得最高的分类精度。此外,应该使用什么标准来确定样本模型的截止值,该样本不包含有偿债能力和资不抵债的企业的比例相等。该分析是在1677家建筑公司的样本上进行的。结果表明,异常值处理显著提高了模型的预测能力,而用最接近的非异常值替代异常值是处理异常值最有效的方法。在确定截止值时,不适合使用分类精度最高的值,因为这可能导致一阶错误比例的增加。该值的优化可能取决于特定金融机构在其贷款组合中所承担的信用风险程度。
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来源期刊
CiteScore
0.90
自引率
40.00%
发文量
30
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