The total cost of misclassification in credit scoring: A comparison of generalized linear models and generalized additive models

Christian Lohmann and Thorsten Ohliger

Journal of Forecasting, 2019, vol. 38, issue 5, 375-389

Abstract: This study examines whether the evaluation of a bankruptcy prediction model should take into account the total cost of misclassification. For this purpose, we introduce and apply a validity measure in credit scoring that is based on the total cost of misclassification. Specifically, we use comprehensive data from the annual financial statements of a sample of German companies and analyze the total cost of misclassification by comparing a generalized linear model and a generalized additive model with regard to their ability to predict a company's probability of default. On the basis of these data, the validity measure we introduce shows that, compared to generalized linear models, generalized additive models can reduce substantially the extent of misclassification and the total cost that this entails. The validity measure we introduce is informative and justifies the argument that generalized additive models should be preferred, although such models are more complex than generalized linear models. We conclude that to balance a model's validity and complexity, it is necessary to take into account the total cost of misclassification.

Date: 2019
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https://doi.org/10.1002/for.2545

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:38:y:2019:i:5:p:375-389

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