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No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure

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  • Carmona, Pedro
  • Dwekat, Aladdin
  • Mardawi, Zeena

Abstract

This study opens the black boxes and fills the literature gap by showing how it is possible to fit a very precise Machine Learning model that is highly interpretable, by using a novel ML technique, Extreme Gradient Boosting (XGBoost), and applying new model interpretability improvements. In addition, we identify several significant indicators that could assist in predicting business financial distress. The data were collected from the Eikon database from a sample of 1760 French firms (1585 healthy and 175 failing) in 2018. Identifying the leading indicators of business failure is critical in assisting regulators, and for business managers to act expeditiously before a distressed business reaches crisis point. Our results reveal that higher levels of equity per employee, solvency, the current ratio, net profitability, and a sustainable return on investment are associated with a lower risk of business failure. In contrast, a higher number of employees leads to business failure.

Suggested Citation

  • Carmona, Pedro & Dwekat, Aladdin & Mardawi, Zeena, 2022. "No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure," Research in International Business and Finance, Elsevier, vol. 61(C).
    • Handle: RePEc:eee:riibaf:v:61:y:2022:i:c:s027553192200037x
    DOI: 10.1016/j.ribaf.2022.101649
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    References listed on IDEAS

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    More about this item

    Keywords

    Business failure; Machine learning; XGBoost; Model interpretability;
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C - Mathematical and Quantitative Methods
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

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