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Failure prediction models: performance, disagreements, and internal rating systems. NBB Working Papers. No. 123, 13 December 2007

机译:故障预测模型:性能,分歧和内部评级系统。 NBB工作论文。 2007年12月13日第123号

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摘要

We address a number of comparative issues relating to the performance of failure prediction models for small, private firms. We use two models provided by vendors, a model developed by the National Bank of Belgium, and the Altman Z-score model to investigate model power, the extent of disagreement between models in the ranking of firms, and the design of internal rating systems. We also examine the potential gains from combining the output of multiple models. We find that the power of all four models in predicting bankruptcies is very good at the one-year horizon, even though not all of the models were developed using bankruptcy data and the models use different statistical methodologies. Disagreements in firm rankings are nevertheless significant across models, and model choice will have an impact on loan pricing and origination decisions. We find that it is possible to realize important gains from combining models with similar power. In addition, we show that it can also be beneficial to combine a weaker model with a stronger one if disagreements across models with respect to failing firms are high enough. Finally, the number of classes in an internal rating system appears to be more important than the distribution of borrowers across classes.
机译:我们针对小型私营企业的故障预测模型的性能解决了许多比较问题。我们使用供应商提供的两个模型,比利时国家银行开发的模型和Altman Z评分模型来研究模型的功能,模型在公司排名中的分歧程度以及内部评级系统的设计。我们还将研究通过组合多个模型的输出获得的潜在收益。我们发现,尽管并非所有模型都是使用破产数据开发的并且模型使用不同的统计方法,但在一年的展望中,这四个模型的预测能力都非常好。但是,在各个模型中,公司排名的分歧仍然很明显,模型的选择将对贷款定价和发起决策产生影响。我们发现,将具有相似功效的模型组合在一起可以实现重要的收益。此外,我们表明,如果关于破产企业的各种模型之间的分歧足够高,则将较弱的模型与较强的模型相结合也是有益的。最后,内部评级系统中的类别数量似乎比借款人在各个类别中的分配更为重要。

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