Research on Financial Early Warning of Listed Corporation Based on SOM Fusion BP Neural Network

Abstract

Combining with the special environment of Chinese market, this paper defines the listed Corporation’s financial crisis, and analyzes the shortcomings of the existing financial early warning model. In order to further improve the accuracy of the financial early warning, and adaptively select optimal training samples, short-term forecasting model of listed corporations based on the SOM network fusion BP network is proposed. The model firstly extracts the initial training samples relying on the SOM network and obtains the optimum ST samples and non ST samples in all training samples. Furthermore, the extracted samples are utilized to construct the financial early warning system of five different levels based on SOM network. Finally, the model is compared with other model algorithms. The results show that the financial early-warning model proposed in this paper possesses higher recognition accuracy on short term forecasting and monitoring of enterprise finance compared with other recognition models. Moreover, smaller data size is needed in this model on the premise that the effectiveness is guaranteed. Therefore, the early warning model proposed in this paper can better realize enterprise financial monitoring, so as to effectively prevent and defuse financial risks and crises.