The DOI matches its Crossref record; 117 of the 117 references it cites were found in a registry or an index.
Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches
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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
Financial Ratios and the Probabilistic Prediction of Bankruptcy
Ohlson, J. A. (1980) · Journal of Accounting Research · doi:10.2307/2490395 · Cited by 5,989
Financial Ratios As Predictors of Failure
Beaver, W. H. (1966) · Journal of Accounting Research · doi:10.2307/2490171 · Cited by 4,882
Bates, J. M., & Granger, C. W. J. (1969) · Journal of the Operational Research Society · doi:10.1057/jors.1969.103 · Cited by 3,201
Methodological Issues Related to the Estimation of Financial Distress Prediction Models
Zmijewski, M. E. (1984) · Journal of Accounting Research · doi:10.2307/2490859 · Cited by 2,888
A Discriminant Analysis of Predictors of Business Failure
Deakin, E. B. (1972) · Journal of Accounting Research · doi:10.2307/2490225 · Cited by 1,166
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 1,118
Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review
Ravi Kumar, P., & Ravi, V. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.08.043 · Cited by 1,074
(2007) · Financial Management for Hospitality Decision Makers · doi:10.4324/9780080506456-11 · Cited by 1,022
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
MIN, J., & LEE, Y. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2004.12.008 · Cited by 871
Cited by
Hajek, P., & Henriques, R. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.05.001 · Cited by 334
Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks
López Iturriaga, F. J., & Sanz, I. P. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.11.025 · Cited by 199
Zizi, Y., Jamali-Alaoui, A., El Goumi, B., Oudgou, M., & El Moudden, A. (2021) · Risks · doi:10.3390/risks9110200 · Cited by 47
Explainable machine learning for corporate financial distress prediction in Vietnam
Le Nam, T., & Phan Huy, T. (2026) · Cogent Business & Management · doi:10.1080/23311975.2026.2670659 · Cited by 2
Metadata
- Authors
- Sun, J., Li, H., Huang, Q.-H., & He, K.-Y.
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2014-02
- Volume
- 57
- Pages
- 41-56
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2072768981
- Cited by
- 393
- References
- 117
- FWCI
- 49.47
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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- CoChat. (2026). Citation check: Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. https://app.cochat.ai/papers/10.1016/j.knosys.2013.12.006
- The paper
- Sun, J., Li, H., Huang, Q.-H., & He, K.-Y. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2013.12.006
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
