Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis
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Prior research has demonstrated that artificial neural networks can, in many instances, provide superior information than traditional methods.
“It has been demonstrated by prior research that ANNs can, in many instances, provide superior information than the information provided by traditional methods.”
From A Neural Network Approach to Estimating the Allowance for Bad Debt (2011) · cites this paper as Zhang, et al. (1999)
Cites
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578 · see citing works
A leisurely look at the bootstrap, the jackknife, and cross-validation
Efron, B., & Gong, G. (1983) · The American Statistician · doi:10.1080/00031305.1983.10483087 · Cited by 3,229 · see citing works
The Predictive Sample Reuse Method with Applications
Geisser, S. (1975) · Journal of the American Statistical Association · doi:10.1080/01621459.1975.10479865 · Cited by 2,748 · see citing works
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Neural networks and statistical techniques: A review of applications
Paliwal, M., & Kumar, U. A. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.10.005 · Cited by 792 · see citing works
Forecasting and operational research: a review
Fildes, R., Nikolopoulos, K., Crone, S. F., & Syntetos, A. A. (2008) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2602597 · Cited by 287 · see citing works
Rahman, M. S., Hossain, M. A., & Abdel Fattah, F. A. M. (2022) · Journal of Enterprise Information Management · doi:10.1108/jeim-05-2020-0185 · Cited by 74 · see citing works
Zizi, Y., Jamali-Alaoui, A., El Goumi, B., Oudgou, M., & El Moudden, A. (2021) · Risks · doi:10.3390/risks9110200 · Cited by 47 · see citing works
A Neural Network Approach to Estimating the Allowance for Bad Debt
Joyner, D. (2011) · VCU Libraries · doi:10.25772/h720-xy24 · Cited by 2 · see citing works
- Authors
- Zhang, G., Y. Hu, M., Eddy Patuwo, B., & C. Indro, D.
- Affiliations
- Georgia State University (US)
- ORCID
- Zhang, Guoqiang
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 1999-07
- Volume
- 116
- Issue
- 1
- Pages
- 16-32
- ISSN
- 0377-2217 (ISSN-L)
- OpenAlex
- W1988221687
- Cited by
- 677
- References
- 3
- Citations by year
- 2026: 14 · 2025: 20 · 2024: 19 · 2023: 25 · 2022: 30 · 2021: 46 · 2020: 31 · 2019: 34 · 2018: 40 · 2017: 40 · 2016: 33 · 2015: 22 · 2014: 38 · 2013: 36 · 2012: 40
- FWCI
- 28.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- European Journal of Operational Research (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- bankruptcy prediction, artificial neural networks, cross-validation, Bayesian classification, logistic regression, classification accuracy, sample variation, financial distress
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.
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