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Predicting private company failure: A multi-class analysis

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  • Jones, Stewart
  • Wang, Tim

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.

Suggested Citation

  • Jones, Stewart & Wang, Tim, 2019. "Predicting private company failure: A multi-class analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 161-188.
    • Handle: RePEc:eee:intfin:v:61:y:2019:i:c:p:161-188
    DOI: 10.1016/j.intfin.2019.03.004
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    More about this item

    Keywords

    Private company failures; Multi-class; Machine learning; Gradient boosting; Logit; Macroeconomic variables; Accounting-based indicators;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • M4 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting

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