Financial Risk Forecasting Of Information Service Industry Listed Company Research Based On SVM Model

Under the circumstance of "Internet+" and "Big Data",Modern Information Service Industries embrace a golden opportunity for rapid development. As a typical kind of enterprise in China, listed company has important status and influence in its field. With the deepen of its field,list of information service companies faces a series of challenges,lack of information resources, diversified and irrational investments,low exploitation and utilization ratio of information resources, small service area, etc. With the continuous development of industry and enhancement of service quality demand, the problems are highlighting and risks are increasing. The financial early forecasting model is beneficial to detect problems in management timely, thus strengthen the ability to resist risks and to accustom the varying management environment. Hence, it is necessary to forecast the financial risk of information service industry listed companies.This paper consists of 6 parts. The first part mainly gives the background of the research, concludes papers regarding information services, data mining and financial risk forecasting and clarifies the subject,methodology and innovations. The second part introduces knowledge about data mining and financial risk. The third part mainly analyze the financial risks of list of information service companies in China, defines the concept of information service industry and interprets the characteristics of this industry and its special financial risk. The fourth is mainly about model building and data selection. This part selects 338 listed companies from Shanghai and Shenzhen boards and obtain data from the annual financial reports. The sample consists of 66 companies. There are 24 indices in the financial early forecasting mode. The fifth is about empirical analysis. The last gives a conclusion of this paper, proposes the deficiency and expection.This paper chooses Support Vector Machine in model building after examining the regarding research findings. This is due to the unrestraint of variable and the generalization ability. This model can give precise results even under small sample. It should be noticed that, this paper selects 17 new ST companies during 2012 to 2016. In Accordance with the rule of the same sort and the same size, this paper pairs 17 non-ST companies. In order to keep the correspondence of the sample, 22 companies are chosen from the other companies. Hence,the sample consists of 66 companies in total. The predictability increases with the approach of the risk. This greatly enhances the practicability of this model and give support to managers in risk control.