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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Veröffentlicht in:Computational economics Jg. 61; H. 2; S. 715 - 741
Hauptverfasser: Ben Jabeur, Sami, Stef, Nicolae, Carmona, Pedro
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.02.2023
Springer
Springer Nature B.V
Springer Verlag
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0927-7099
1572-9974
DOI:10.1007/s10614-021-10227-1