Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/310466 
Year of Publication: 
2011
Citation: 
[Journal:] Journal of Accounting and Management Information Systems (JAMIS) [ISSN:] 2559-6004 [Volume:] 10 [Issue:] 3 [Year:] 2011 [Pages:] 351-374
Publisher: 
Bucharest University of Economic Studies, Bucharest
Abstract: 
This paper proposes a hybrid approach to the forecasting of firms' bankruptcy of Spanish enterprises from the construction sector. Our proposal starts splitting the group of healthy companies into two subgroups: borderline and non-borderline companies. Borderline companies are healthy companies with marked financial similarities with bankrupt ones. Then, each subgroup is divided in clusters according to their financial similarities and then each cluster is replaced by a director vector which represents the companies included in the cluster. In order to do this, we use Self Organizing Maps (SOM). Once the companies in clusters have been replaced by director vectors, we estimate a classification model through Multivariate Adaptive Regression Splines (MARS). Our results show that the proposed hybrid approach is much more accurate for the identification of the companies that go bankrupt than other approaches such as a multi-layer perceptron neural network and a simple MARS model.
Subjects: 
Bankruptcy
Self Organized Maps (SOM)
Multivariate Adaptive Regression Splines (MARS)
Construction firms
JEL: 
G17
G33
C61
C81
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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