Financial Distress Prediction Study with Adaptive Genetic Fuzzy Neural Networks on Listed Corporations of China

Neural networks (NNs) have been widely used to predict financial distress because of their excellent performances of treating non-linear data with self-learning capability. However, the shortcoming of NNs is also significant due to a “black box” syndrome. Moreover, in many situations NNs more or less suffer from the slow convergence and occasionally involve in a local optimal solution, which strongly limited their applications in practice. In this paper, a hybrid system combining Fuzzy Neural Network and Adaptive Genetic Algorithm — Adaptive Genetic Fuzzy Neural Network (AGFNN) is proposed to overcome NN’s drawbacks. Furthermore, the new model has been applied to financial distress analysis based on the data collected from a set of Chinese listed corporations, and the results indicate that the performance of AGFNN model is much better than the ones of BPNN model and FNN model.