A Genetic Fuzzy Neural Network for Bankruptcy Prediction in Chinese Corporations

The use of neural networks (NNs) for financial applications is quite common because of their excellent performances of treating non-linear data with self-learning capability. Often arises the problem of a black-box approach,i.e. after having trained neural networks for a particular problem, it is almost impossible to analyse them for how they work. The Fuzzy Neural Networks(FNN) allow to add rules to neural networks. This avoids the black-box but lacks of effective learning capability. To overcome these drawbacks, in this study a Integration of Genetic Algorithm and fuzzy neural networks (GFNN) are proposed to forecast corporation bankruptcy. The results indicate that the predictive accuracies obtained from GFNN are much higher than the ones obtained from NNs. To make this clearer, an illustrative example is also demonstrated in this study.