Bankruptcy prediction using the XGBoost algorithm and variable importance feature engineering

Sami Ben Jabeur (), Nicolae Stef and Carmona Pedro
Additional contact information
Sami Ben Jabeur: UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University), ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University)

Post-Print from HAL

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: XGBoost; Machine learning; Bankruptcy; Corporate failure; Faillite; Apprentissage automatique; Défaillance d'entreprise (search for similar items in EconPapers)
Date: 2023-02
References: Add references at CitEc
Citations:

Published in Computational Economics, 2023, 61, pp.715-741. ⟨10.1007/s10614-021-10227-1⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05238451

DOI: 10.1007/s10614-021-10227-1

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().