Research On Listed Information Technology Companies Financial Crisis Pre-warning Based On The Theories Of Grey Neural Network

The rapid development of economy in our country and the support of nationalpolicies promote information technology industry to develop rapidly. It has becomeone of the pillar industries of national economy. The development of informationtechnology industry has played a significant role in solving employment problems andpromoting economic growth. Series of date shows that the industry makes its owncontribution for our country’s economic development. Due to the good externalenvironment in the current phase, the operating condition of information technologylisted companies is very good. In the two share markets of Shenzhen and Shanghai,the ratio of the companies that are titled with “ST” because of financial crisis in totallisted companies of the industry is not very high. However, information technologyindustry is a typical of high-risk industry. High-risk along with high-return, but alsobrings some challenges to managers. One mistake could cause serious financial crisis.Besides, because of this industry technology level is far behind foreign country’s, thesituation of the later compete will become more and more severe. Enterprises thathave no core competitiveness and have no able to owe market and gain profits willappear financial crisis. Once company has financial problems and managers can notresolve on time, it will bring enormous loss to investors, creditors and otherstakeholders. So it has great significant to forecast financial crisis that combine withthe feature of information technology in our country on the basis of the research athome and abroad.In this paper, on the basis of reading many literatures at home and abroad, at first,we define the financial crisis of listed companies in the information technologyindustry and analyze the reasons for financial crisis. In the empirical part, we choose36listed companies of information technology industry form Shenzhen and Shanghaishare market for samples and preliminary selected21financial indicators and5non-financial indicators for warning indicators. After using grey correlation analysis,we screen8financial indicators for model. In order to dynamic pre-warn financialcrisis, we construct neural network at first. Then put the dates that from grey forestmodel into neural network that are finished. Grey neural model combines the strongpoints of grey forecasting model that can deal with poor information, having greydate’s samples and autonomous learning of neural network. The model is good byproved that judged the listed company’s financial crisis or financial health successfully. The model gives a basis to managers to understand the financialcondition of enterprise and take measures to compensate for the loss.The results show that:(1)Some financial indicators significantly reflectenterprise’s financial crisis.(2)Some non-financial indicators also have a great impacton financial condition of enterprises.(3) Pay attention to the trend of financialindicators that reflect financial situation in future.(4)Grey neural network thatcombines grey system and BP neural network is the good dynamic pre-warningmodel.