Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering

Sami Ben Jabeur (), Nicolae Stef () and Pedro Carmona ()
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Sami Ben Jabeur: Sciences and Humanities Confluence Research Center - UCLY, ESDES
Nicolae Stef: Université Bourgogne Franche-Comté
Pedro Carmona: University of Valencia

Computational Economics, 2023, vol. 61, issue 2, No 9, 715-741

Abstract: Abstract The emergence of big data, information technology, and social media provides an enormous amount of information about firms’ current financial health. When facing this abundance of data, decision makers must identify the crucial information to build upon an effective and operative prediction model with a high quality of the estimated output. The feature selection technique can be used to select significant variables without lowering the quality of performance classification. In addition, one of the main goals of bankruptcy prediction is to identify the model specification with the strongest explanatory power. Building on this premise, an improved XGBoost algorithm based on feature importance selection (FS-XGBoost) is proposed. FS-XGBoost is compared with seven machine learning algorithms based on three well-known feature selection methods that are frequently used in bankruptcy prediction: stepwise discriminant analysis, stepwise logistic regression, and partial least squares discriminant analysis (PLS-DA). Our experimental results confirm that FS-XGBoost provides more accurate predictions, outperforming traditional feature selection methods.

Keywords: Corporate failure; XGBoost; Machine learning; Bankruptcy (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10614-021-10227-1

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