TITLE:
Research on Financial Early Warning of Listed Corporation Based on SOM Fusion BP Neural Network
AUTHORS:
Qun Meng
KEYWORDS:
Financial Early Warning, SOM Network, BP Network, Short Term Forecast, Optimum Sample
JOURNAL NAME:
Modern Economy,
Vol.7 No.5,
May
27,
2016
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.