Research On Financial Early Warning Of Chip-listed Companies

In 2018,the US government announced a chip embargo policy for Chinese companies,and the chip once again became the focus of hot discussion.The chip is not only the core of electronic products,but also related to national information security.Last year,China consumed more than half of the world's chips,and the self-sufficiency rate was less than 20%.As the national ministries and local governments continued to introduce support policies for the chip industry and the upcoming fundraising of the second phase of the big fund,more and more capital will be Will flood into the chip industry.For the investment in the chip industry,opportunities and risks coexist.Financial risks are an important factor affecting the gro.Arth and growth of a company.Therefore,establishing a dynamic financial early warning model is not only conducive to timely adjustment of development strategies,but also beneficial to investment.For investors to give advice.This paper constructs a financial early warning model for China's chip listed companies Firstly,the Wind database was used to obtain 17 financial data of 26 chip listed companies from 2015 to 2018 from the five financial aspects of solvency,operation ability,growth ability,profitability and cash flow,and then use SPSS software for significance test.And related inspections,screened a financial indicator that can comprehensively reflect the financial situation of chip listed companies,including asset-liability ratio,accounts receivable turnover rate,operating income growth rate,sales gross profit margin,and operating cash ratio.Secondly,according to the selected index data,26 samples are divided into training group and test group,BP neural network model is established by training group samples,and then tested whether it is trained through simulation.Thirdly,in the MATLAB software,the gray model is constructed to predict the financial data of the next time node,and the predicted result is substituted into the BP neural network model to realize dynamic financial warning.In the empirical part,this paper predicts the financial situation of the 18-year annual report.Combined with the published 18-year annual report forecast data,it is verified that the gray BP neural network model constructed in this paper has high prediction accuracy and has certain practical application value.