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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
In the citing paper
“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]
Citations
Cites
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
Cited by
Sorted by citations
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
“When compared to other models, these are understandable enough to make users feel confident on using them for practical applications in finance [193–195] , environmental studies [196] , geology [197] , healthcare [44] , biology [198,199] and energy [200] .”
Machine Learning in Financial Crisis Prediction: A Survey
Lin, W.-Y., Hu, Y., & Tsai, C. (2011) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2170420 · Cited by 332
Comparative analysis of data mining methods for bankruptcy prediction
Olson, D. L., Delen, D., & Meng, Y. (2011) · Decision Support Systems · doi:10.1016/j.dss.2011.10.007 · Cited by 323
Weber, P., Carl, K. V., & Hinz, O. (2024) · Management Review Quarterly · doi:10.1007/s11301-023-00320-0 · Cited by 292
“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 (e.g., Berg 2007; Taylan et al. 2007).”
Bankruptcy prediction using terminal failure processes
du Jardin, P. (2014) · European Journal of Operational Research · doi:10.1016/j.ejor.2014.09.059 · Cited by 152
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
