The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Bankruptcy prediction by generalized additive models
Open accessAbstract
We compare several accounting‐based models for bankruptcy prediction. The models are developed and tested on large data sets containing annual financial statements for Norwegian limited liability firms. Out‐of‐sample and out‐of‐time validation shows that generalized additive models significantly outperform popular models like linear discriminant analysis, generalized linear models and neural networks at all levels of risk. Further, important issues like default horizon and performance depreciation are examined. We clearly see a performance depreciation as the default horizon is increased and as time goes by. Finally a multi‐year model, developed on all available data from three consecutive years, is compared with a one‐year model, developed on data from the most recent year only. The multi‐year model exhibits a desirable robustness to yearly fluctuations that is not present in the one‐year model. Copyright © 2006 John Wiley & Sons, Ltd.
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“Relevant research concerning XAI in the form of general additive models in Finance (Berg 2007; Taylan etal. 2007; Calabrese 2012) did not appear in our search.”
From Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature (2024) · cites this paper as Berg 2007
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“Although we tried to base our literature search on a broad variety of databases, there is still the possibility of researching more outlets, varying keywords, and including a broader publication date range to receive an even richer set of results”
From Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature (2024) · cites this paper as Berg 2007
In the citing paper
“When compared to other models, these are understandable enough to make users feel confident on using them for practical applications in finance [193, 194, 195], environmental studies [196], geology [197], healthcare [44], biology [198, 199] and energy [200].”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [193]
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FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY
Altman, E. I. (1968) · The Journal of Finance · doi:10.1111/j.1540-6261.1968.tb00843.x · Cited by 14,888
Hastie, T., & Tibshirani, R. (1986) · Statistical Science · doi:10.1214/ss/1177013604 · Cited by 12,575
Brown, R. A., Hastie, T. J., & Tibshirani, R. J. (1991) · Biometrics · doi:10.2307/2532174 · Cited by 8,263
Pattern Recognition and Neural Networks
Ripley, B. D. (1996) · Cambridge University Press eBooks · doi:10.1017/cbo9780511812651 · Cited by 6,500
Financial Ratios and the Probabilistic Prediction of Bankruptcy
Ohlson, J. A. (1980) · Journal of Accounting Research · doi:10.2307/2490395 · Cited by 6,250
Financial Ratios As Predictors of Failure
Beaver, W. H. (1966) · Journal of Accounting Research · doi:10.2307/2490171 · Cited by 4,882
Assessing the Probability of Bankruptcy
Hillegeist, S. A., Keating, E. K., Cram, D. P., & Lundstedt, K. G. (2004) · Review of Accounting Studies · doi:10.1023/b:rast.0000013627.90884.b7 · Cited by 1,357
Managerial Applications of Neural Networks: The Case of Bank Failure Predictions
Tam, K. Y., & Kiang, M. Y. (1992) · Management Science · doi:10.1287/mnsc.38.7.926 · Cited by 1,169
Statistical Classification Methods in Consumer Credit Scoring: A Review
Hand, D. J., & Henley, W. E. (1997) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.1997.00078.x · Cited by 1,042
Bankruptcy prediction using neural networks
Wilson, R. L., & Sharda, R. (1994) · Decision Support Systems · doi:10.1016/0167-9236(94)90024-8 · Cited by 733
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Möllenhoff, S. (2020) · ELPUB (Universitat Wuppertal) · doi:10.25926/h3h8-gh24
Alfaro, E., Gámez, M., & García, N. (2018) · doi:10.1002/9781119421566.refs
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Duwalage, K. I. (2022) · Queensland University of Technology · doi:10.5204/thesis.eprints.228610
Genelleştirilmiş Toplamsal Modellerin Zooteknide Kullanımı
KURDAL, N., & Önder, H. (2022) · Black Sea Journal of Engineering and Science · doi:10.34248/bsengineering.1075808
Reyes, M., & Ordóñez, L. B. T. (2022) · UDA AKADEM · doi:10.33324/udaakadem.v2i10.531
Estimation of Additive Logistic Model Based on B-Spline
昝, 思. (2024) · Pure Mathematics · doi:10.12677/pm.2024.142060
Lohmann, C., & Ohliger, T. (2024) · Research in International Business and Finance · doi:10.1016/j.ribaf.2024.102395
Lan, H., Pu, J., Xia, Y., He, Y., Zhang, Y., Huang, J., & Rao, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71315-6_4
What happens at Trinity Spa stays at Trinity Spa: spa-goers’ privacy expectations
Koranteng, A., Adongo, C. A., Amoah, C. O., & Dayour, F. (2025) · International Journal of Spa and Wellness · doi:10.1080/24721735.2025.2543690
A Neural-Symbolic learning framework to produce interpretable predictions for image classification
Bennetot, A. (2022) · theses.fr (ABES) · doi:10.70675/2b59f598z2028z49afz8a20z29b21f19f394
A bibliometric analysis of the impact of artificial intelligence on the accounting profession
Said, F. B., Chakroun, R., & Slim, C. (2026) · Fintech and Digital Accounting Review · doi:10.1108/fdar-08-2025-0020
Kesavan, V., & Polisetty, A. (2026) · Wiley eBooks · doi:10.1002/9781394357789.ch24
Metadata
- Authors
- Berg, D.
- Affiliations
- Norwegian Computing Center (NO)
- Corresponding author
- Berg, Daniel
- ORCID
- Berg, Daniel
- Venue
- Applied Stochastic Models in Business and Industry
- Publisher
- Wiley
- Published
- 2006-11-08
- Volume
- 23
- Issue
- 2
- Pages
- 129-143
- ISSN
- 1524-1904 (ISSN-L)
- 1526-4025
- OpenAlex
- W2110767824
- Cited by
- 114
- References
- 22
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 5 · 2023: 7 · 2022: 10 · 2021: 5 · 2020: 9 · 2019: 9 · 2018: 6 · 2017: 11 · 2016: 6 · 2015: 6 · 2014: 7 · 2013: 4 · 2012: 4
- FWCI
- 7.64
- Type
- journal article
- Language
- en
- Open access
- green
- urn.nb.no
- Licence
- Locations
- Applied Stochastic Models in Business and Industry (publishedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- Funders
- Norges Teknisk-Naturvitenskapelige Universitet
- Topics
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Social Sciences › Law › Corporate Insolvency and Governance
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- bankruptcy prediction, generalized additive models, financial statements, out-of-sample validation, Norwegian companies
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