Financial Distress Prediction Of Chinese Listed Enterprises Of Manufacturing Industries Based On BP Neural Network

In view of the limitation of the statistical method which exists in the corporate finance distress prediction, this paper proposes the neural network superiority and potential. According to the concept of the financial distress, this paper discusses how to establish the models for predicting financial distress of corporations using BP neural network. In this paper, we chose the datum of Chinese listed companies of manufacturing industries which were classified and studied on industry and time from 2002 to 2005 as the modelling samples and the examination samples. Through training the samples repeatedly, we has obtained 92.86% accurate rate to the modelling samples and 92.5% accurate rate to the examination samples. Comparing to the domestic mainly studies of the methods of financial distress prediction, the predicting effects in this paper has the very big enhancement. In the end, we drew these conclusions by this paper: The BP neural network model overcomes some shortcoming of the statistical methods and has the higher accuracy than most models for financial distress predictions . It can be applied to forecast the financial crisis of the companies of manufacturing industries of our country. We can forecast the development of the BP neural network in financial prediction of manufacturing industries.