Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/311766 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Business Economics [ISSN:] 1861-8928 [Volume:] 93 [Issue:] 9 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2022 [Pages:] 1661-1690
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
This study uses generalized additive models to identify and analyze nonlinear relationships between accounting-based and market-based independent variables and how these affect bankruptcy predictions. Specifically, it examines the independent variables that Altman (J Financ 23:589–609, 1968; Predicting financial distress of companies. Revisiting the Z-score and ZETA ® models. Working paper, 2000) and Campbell et al. (J Financ 63:2899–2939, 2008) used and analyzes what specific form these nonlinear relationships take. Drawing on comprehensive data on listed U.S. companies, we show empirically that the bankruptcy prediction is influenced by statistically and economically relevant nonlinear relationships. Our results indicate that taking into account these nonlinear relationships improves significantly several statistical validity measures. We also use a validity measure that is based on the profitability of the bankruptcy prediction models in the context of credit scoring. The findings demonstrate that taking into account nonlinear relationships can substantially increase the discriminatory power of bankruptcy prediction models.
Subjects: 
Accounting-based information
Bankruptcy prediction
Profitability of credit scoring
Generalized additive model
Nonlinear relationships
JEL: 
C53
D81
G33
M41
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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