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• W2034654759 abstract "This paper proposes a new approach to the forecasting of firms’ bankruptcy. Our proposal is a hybrid method in which sound companies are divided in clusters using Self Organized Maps (SOM) and then each cluster is replaced by a director vector which summarizes all of them. Once the companies in clusters have been replaced by director vectors, we estimate a classification model through Multivariate Adaptive Regression Splines (MARS). For the test of the model we considered a real setting of Spanish enterprises from the construction sector. With this procedure we intend to overcome the sampling-bias problems that matched-pairs models often suffer. We estimated two benchmark models: a back propagation neural network and a simple MARS model. Our results show that the proposed hybrid approach is much more accurate than the benchmark techniques for the identification of the bankrupt companies." @default.
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• W2034654759 date "2012-06-01" @default.
• W2034654759 modified "2023-09-23" @default.
• W2034654759 title "A hybrid device for the solution of sampling bias problems in the forecasting of firms’ bankruptcy" @default.
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• W2034654759 doi "https://doi.org/10.1016/j.eswa.2012.01.135" @default.
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