The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Managerial Applications of Neural Networks: The Case of Bank Failure Predictions
Abstract
This paper introduces a neural-net approach to perform discriminant analysis in business research. A neural net represents a nonlinear discriminant function as a pattern of connections between its processing units. Using bank default data, the neural-net approach is compared with linear classifier, logistic regression, kNN, and ID3. Empirical results show that neural nets is a promising method of evaluating bank conditions in terms of predictive accuracy, adaptability, and robustness. Limitations of using neural nets as a general modeling tool are also discussed.
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Cites
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Cited by
Forecasting with artificial neural networks:
Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998) · International Journal of Forecasting · doi:10.1016/s0169-2070(97)00044-7 · Cited by 4,229
Neural network credit scoring models
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Credit rating analysis with support vector machines and neural networks: a market comparative study
Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H., & Wu, S. (2004) · Decision Support Systems · doi:10.1016/s0167-9236(03)00086-1 · Cited by 1,017
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
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Neural networks and statistical techniques: A review of applications
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Metadata
- Authors
- Tam, K. Y., & Kiang, M. Y.
- Venue
- Management Science
- Published
- 1992-07
- Volume
- 38
- Issue
- 7
- Pages
- 926-947
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W2016207894
- Cited by
- 1,118
- References
- 32
- FWCI
- 93.78
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
