Self organizing neural networks for financial diagnosis
Decision Support Systems
Using Feature Construction to Improve the Performance of Neural Networks
Management Science
IWANN '97 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Biological and Artificial Computation: From Neuroscience to Technology
Choosing Bankruptcy Predictors Using Discriminant Analysis, Logit Analysis, and Genetic Algorithms
Choosing Bankruptcy Predictors Using Discriminant Analysis, Logit Analysis, and Genetic Algorithms
Learning About Failure: Bankruptcy, Firm Age, and the Resource-Based View
Organization Science
International Journal of Intelligent Systems in Accounting and Finance Management
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
Decision Support Systems
A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms
Expert Systems with Applications: An International Journal
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
Clustering and visualization of bankruptcy trajectory using self-organizing map
Expert Systems with Applications: An International Journal
Partial Least Square Discriminant Analysis for bankruptcy prediction
Decision Support Systems
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The aim of this study is to show how a Kohonen map can be used to increase the forecasting horizon of a financial failure model. Indeed, most prediction models fail to forecast accurately the occurrence of failure beyond 1year, and their accuracy tends to fall as the prediction horizon recedes. So we propose a new way of using a Kohonen map to improve model reliability. Our results demonstrate that the generalization error achieved with a Kohonen map remains stable over the period studied, unlike that of other methods, such as discriminant analysis, logistic regression, neural networks and survival analysis, traditionally used for this kind of task.