Effectiveness of neural networks for prediction of corporate financial distress in China

The study examines the effectiveness of two types of neural networks in predicting corporate financial distress in China. Back-propagation and LVQ neural networks are considered. The neural networks are compared against Logistic Regression, which is the most popular one among the traditional methods. The results show that the level of Type I and Type 11 errors varies greatly across techniques. The neural networks have low level of Type I error and high level of Type 11 error, while Logistic Regression has the reverse relationship. Since the cost of Type I is more expensive than that of Type 11 error in this field. We demonstrate that the performance of the neural networks tested is superior to Logistic Regression.