Predicting private company failure: A multi-class analysis

Stewart Jones and Tim Wang

Journal of International Financial Markets, Institutions and Money, 2019, vol. 61, issue C, 161-188

Abstract: This study utilizes an advanced machine learning method known as TreeNet® (Salford Systems, 2017) to predict a variety of private company failure states, ranging from binary settings (i.e. failed vs non-failed) to more complex multi-class settings with up to five states of failure. Based on a large global sample, TreeNet® proved to be a significantly better predictor of private company failure than conventional models such as logistic regression. While the out-of-sample predictive performance of TreeNet® is best in binary settings, the model also produces strong area under the ROC curve (AUC) results for the multi-class models. We also find that the predictive performance of financial variables is significantly enhanced when combined with external risk factors such as macro-economic variables and other non-financial measures. The results of this study have several implications for the private company failure literature and the usefulness of machine learning methods in accounting and finance more generally.

Keywords: Private company failures; Multi-class; Machine learning; Gradient boosting; Logit; Macroeconomic variables; Accounting-based indicators (search for similar items in EconPapers)
JEL-codes: C1 M4 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfin:v:61:y:2019:i:c:p:161-188

DOI: 10.1016/j.intfin.2019.03.004

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