Research on financial crisis prediction model based on Rough Sets and Neural Network

With the development of the Rough Sets and Neural Network, dynamic prediction research on financial crisis has become as a developing trend. Based on this situation, this paper makes use of the Rough Sets´ attribute reduction technique to reduce the financial index firstly, then imposes the Neural Network to train network so as to establish financial crisis alarming model to drop out enterprise´s crisis. According to the analysis, we find the model´s prediction accuracy is very high, therefore, it can provide the effective investment basis for the listed companies´ investors.