Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks. (27th October 2016)

• Record Type: Journal Article
• Authors: Jones, Stewart; Johnstone, David; Wilson, Roy
• Is Part Of: Journal of business finance & accounting. Volume 44:Number 1/2(2017)
• Page Start: 3
• Page End: 34
• Publication Date: 2016-10-27
• Subjects: corporate bankruptcy prediction -- binary classifiers -- statistical learning; Finance -- Periodicals; Accounting -- Periodicals; Business -- Periodicals; 658.15
• DOI: 10.1111/jbfa.12218
• Languages: English
• ISSNs: 0306-686X

Abstract

Abstract: Corporate bankruptcy prediction has attracted significant research attention from business academics, regulators and financial economists over the past five decades. However, much of this literature has relied on quite simplistic classifiers such as logistic regression and linear discriminant analysis (LDA). Based on a large sample of US corporate bankruptcies, we examine the predictive performance of 16 classifiers, ranging from the most restrictive classifiers (such as logit, probit and linear discriminant analysis) to more advanced techniques such as neural networks, support vector machines (SVMs) and "new age" statistical learning models including generalised boosting, AdaBoost and random forests. Consistent with the findings of Jones et al. (2015 ), we show that quite simple classifiers such as logit and LDA perform reasonably well in bankruptcy prediction. However, we recommend the use of "new age" classifiers in corporate bankruptcy modelling because: (1) they predict significantly better than all other classifiers on both the cross‐sectional and longitudinal test samples; (2) the models may have considerable practical appeal because they are relatively easy to estimate and implement (for instance, they require minimal researcher intervention for data preparation, variable selection and model architecture specification); and (3) while the underlying model structures can be very complex, we demonstrate that "new age" classifiers have a reasonably good level of interpretability through such metrics as relative variable importances (RVIs).