A hybrid device for the solution of sampling bias problems in the forecasting of firms%26apos; bankruptcy

作者：Sanchez Lasheras Fernando; de Andres Javier^*; Lorca Pedro; de Cos Juez Francisco Javier

来源：Expert Systems with Applications, 2012, 39(8): 7512-7523.

DOI：10.1016/j.eswa.2012.01.135

• Bankruptcy
• Self Organized Maps (SOM)
• Multivariate Adaptive Regression Splines (MARS)
• Construction
• Sampling bias

This paper proposes a new approach to the forecasting of firms%26apos; 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 Once the companies in clusters have been replaced by director vectors, we estimate a classification model through Multivariate Adaptive Regression Splines (MARS).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.

• 出版日期2012-6-15