Grabit: Gradient tree-boosted Tobit models for default prediction

Fabio Sigrist and Christoph Hirnschall

Journal of Banking & Finance, 2019, vol. 102, issue C, 177-192

Abstract: A frequent problem in binary classification is class imbalance between a minority and a majority class such as defaults and non-defaults in default prediction. In this article, we introduce a novel binary classification model, the Grabit model, which is obtained by applying gradient tree boosting to the Tobit model. We show how this model can leverage auxiliary data to obtain increased predictive accuracy for imbalanced data. We apply the Grabit model to predicting defaults on loans made to Swiss small and medium-sized enterprises (SME) and obtain a large and significant improvement in predictive performance compared to other state-of-the-art approaches.

Keywords: Bankruptcy prediction; Censored regression; Class imbalance; Classification; Credit scoring (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (24)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:102:y:2019:i:c:p:177-192

DOI: 10.1016/j.jbankfin.2019.03.004

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