Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering

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 wi...

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Published in:	Computational economics Vol. 61; no. 2; pp. 715 - 741
Main Authors:	Ben Jabeur, Sami, Stef, Nicolae, Carmona, Pedro
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.02.2023 Springer Springer Nature B.V Springer Verlag
Subjects:	Algorithms Bankruptcy Behavioral/Experimental Economics Big Data Classification Computer Appl. in Social and Behavioral Sciences Data mining Decision makers Discriminant analysis Economic Theory/Quantitative Economics/Mathematical Methods Economics Economics and Finance Feature selection Humanities and Social Sciences Information technology Machine learning Math Applications in Computer Science Medical decision making Operations Research/Decision Theory Prediction models Predictions Social media Specification Corporate failure XGBoost Bankruptcy Machine learning Apprentissage automatique Défaillance d'entreprise Faillite
ISSN:	0927-7099, 1572-9974
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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.
AbstractList	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. 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. L'émergence des big data, des technologies de l'information et des médias sociaux fournit une énorme quantité d'informations sur la santé financière actuelle des entreprises. Face à cette abondance de données, les décideurs doivent identifier les informations cruciales pour construire un modèle de prédiction efficace et opérationnel avec une qualité élevée des résultats estimés. La technique de sélection des caractéristiques peut être utilisée pour sélectionner des variables significatives sans diminuer la qualité de la classification des performances. En outre, l'un des principaux objectifs de la prédiction des faillites est d'identifier la spécification du modèle ayant le plus grand pouvoir explicatif. Partant de ce principe, un algorithme XGBoost amélioré basé sur la sélection de l'importance des caractéristiques (FS-XGBoost) est proposé. FS-XGBoost est comparé à sept algorithmes d'apprentissage automatique basés sur trois méthodes de sélection des caractéristiques bien connues et fréquemment utilisées dans la prédiction des faillites : l'analyse discriminante par étapes, la régression logistique par étapes et l'analyse discriminante par moindres carrés partiels (PLS-DA). Nos résultats expérimentaux confirment que FS-XGBoost fournit des prédictions plus précises, surpassant les méthodes traditionnelles de sélection des caractéristiques.
Audience	Academic
Author	Stef, Nicolae Ben Jabeur, Sami Carmona, Pedro
Author_xml	– sequence: 1 givenname: Sami orcidid: 0000-0002-9242-4913 surname: Ben Jabeur fullname: Ben Jabeur, Sami organization: Institute of Sustainable Business and Organizations, Sciences and Humanities Confluence Research Center - UCLY, ESDES – sequence: 2 givenname: Nicolae orcidid: 0000-0001-9471-4669 surname: Stef fullname: Stef, Nicolae email: nicolae.stef@bsb-education.com organization: Department of Accounting, Finance and Law, CEREN EA 7477, Burgundy School of Business, Université Bourgogne Franche-Comté – sequence: 3 givenname: Pedro orcidid: 0000-0002-9979-2727 surname: Carmona fullname: Carmona, Pedro organization: Department of Accounting, University of Valencia
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ContentType	Journal Article
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Keywords	Corporate failure XGBoost Bankruptcy Machine learning Apprentissage automatique Défaillance d'entreprise Faillite
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Snippet	The emergence of big data, information technology, and social media provides an enormous amount of information about firms’ current financial health. When... The emergence of big data, information technology, and social media provides an enormous amount of information about firms' current financial health. When...
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SubjectTerms	Algorithms Bankruptcy Behavioral/Experimental Economics Big Data Classification Computer Appl. in Social and Behavioral Sciences Data mining Decision makers Discriminant analysis Economic Theory/Quantitative Economics/Mathematical Methods Economics Economics and Finance Feature selection Humanities and Social Sciences Information technology Machine learning Math Applications in Computer Science Medical decision making Operations Research/Decision Theory Prediction models Predictions Social media Specification
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Title	Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering
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