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

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 Once the companies in clusters have been replaced by director vectors, we estimate a classification model through Multivariate Adaptive Regression Splines (MARS).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

► Hybrid methods are superior to single models for financial classification tasks.
► Our method clusters companies through Self-Organizing Maps.
► Then each cluster is replaced by a director vector.
► A Multivariate Adaptive Regression Splines (MARS) model is estimated.
► In a real setting, our system outperforms both a single MARS and a neural network.