Towards the prediction of business failure via computational intelligence techniques

Abstract

This is a pioneer study that presents two branches of computational intelligence techniques, namely linear genetic programming (LGP) and radial basis function (RBF) neural network to build models for bankruptcy prediction. The main goal is to classify samples of 140 bankrupt and non-bankrupt Iranian corporations by means LGP and RBF. Another important contribution of this paper is to identify the effective predictive financial ratios based on an extensive bankruptcy prediction literature review and a sequential feature selection analysis. In order to benchmark the proposed models, a log-log regression analysis is further performed. A comparative study on the classification accuracy of the LGP, RBF and regression-based models is conducted. The results indicate that the proposed models effectively let estimate any enterprise in the aspect of bankruptcy. The LGP models have a significantly better prediction performance in comparison with the RBF and regression models. © 2011 Blackwell Publishing Ltd.