Financial Risk Early Warning Of Communications Equipment Manufacturing Enterprises Based On Bayesion Network

Communications equipment manufacturing industry, the backbone enterprises of China’s electronic industry, having a huge industrial scale and still maintaining a high growth rate, attract a lot of capital and technology. Once an enterprise financial risk happening, risk will spread rapidly, which will bring significant losses for stakeholders. Therefore, the enterprise and its stakeholders are very concerned about the financial risks. The establishment of financial risk early warning model is of great practical significance.Through the experience and achievements of the financial risk prediction research on domestic and international research, financial risk is defined as a decline in profitability leding to that EBIT can’t compensate for interest expense over the same period. Based on this definition, the enterprise in financial risks has the following three forms:First, results of the audit show that the fiscal year net profit are negative business; second, in the most recent fiscal year, shareholders’ equity is less than the registered capital or per share net assets of less than 1 yuan; thirdly, consecutive losses in the last two years of business. After determining the meaning of financial risk, financial risk warning indicator system is built, by analyzing the features of communications equipment and the reason of financial risk. The index system consists of four financial indicators and four non-financial indicators and one financial situation.In the data preparation phase, this paper selects data of 240 communications equipment manufacturing enterprise, which had listed in2013. There are 21 enterprise is determined to be financial risk. Then using sampling techniques to get whole sample volume sample of this article. In the next, we will use data of 2013 to predict whether companies will fall into financial risk in 2014.In the empirical phase, we use TAN Bayesian network to build financial risk early warning model, and achieved good results. The overall correctly predicted rate is 97.5% in the training sample. In test sample, it is 95.5%. Finally, according to the financial risk early warning model, this paper analyze the characteristics and proposed suggestions in building early warning system and preventing financial risks.