A hybrid device for the solution of sampling bias problems in the forecasting of firms' bankruptcy

Authors:
Fernando Sánchez-Lasheras;Javier de Andrés;Pedro Lorca;Francisco Javier de Cos Juez
Affiliations:
University of Oviedo, Department of Construction and Manufacturing Engineering, Campus de Gijón, Edificio 5, 33204 Gijón, Spain;University of Oviedo, Faculty of Economy and Business, Department of Accounting, Avda. del Cristo s/n, 33006 Oviedo, Spain;University of Oviedo, Faculty of Economy and Business, Department of Accounting, Avda. del Cristo s/n, 33006 Oviedo, Spain;University of Oviedo, Department of Exploitation and Exploration of Mines, c/ Independencia No. 13, 33004 Oviedo, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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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 Once the companies in clusters have been replaced by director vectors, we estimate a classification model through Multivariate Adaptive Regression Splines (MARS).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.