Research On Financial Performance Evaluation Of Listed Online Education Companies Basing On BP Neural Network

With the economy of China develops continuously,enterprises faces with the double challenge of structural and cyclical factors.And the uncertainty faced by enterprises is strengthened along with it,which makes the enterprise's financial situation is unprecedented concerned by all the stakeholders.Especially for emerging industries such as online education.Therefore,how to scientifically and rationally evaluate the financial performance of online education enterprises and find out the problems in the process of business operation has great significance on their own sustainable development.The purpose of this thesis is to take online education enterprises as study sample,basing on BP neural network together with literature research,data collection and statistical analysis and other methods,to evaluate the listed online education enterprises' financial performance comprehensively,and find out key factors that make bad financial performance,and purpose relevant countermeasures and suggestions for them.Thus the evaluation system for listed online education enterprises' financial performance is build.The research is basing on the Principal-agent Theory,Contingency Theory,Theory of BP Neural Networks and System Theory.Firstly,the framework for listed online education enterprises' financial performance research is build.Then,on the guide of the framework,the evaluation index system for listed online education enterprises' financial performance is build basing on principle of index system building and analysis of the financial management characteristics and financial management problems of online education enterprises,and it evaluates and forecasts the sample enterprises' financial performance using BP Neural Networks.It also intends to reversely analysis the typical simulation company's performance to find out its operations advantages and disadvantages and put forward some optimization measures.Lastly,it summarizes the main conclusions and innovations of this research,and prospects the follow-up study.The main conclusions of this thesis are as follows: Firstly,through the principal component analysis,it is determined that the "cash flow growth rate per share of operating activity" and "net cash flow per share of fundraising activity" have higher contribution rate.They can be used as the input variable of the financial performance evaluation of the online education industry.Secondly,a BP neural network is constructed,in which the traingdx function is selected as the training function,and the number of hidden nodes is decided as 10.Thirdly,through the reverse analysis of the input indicators of the simulation samples with poor performance,it is found that,for the typical sample,the financial fluidity is too high and the net cash flow per share of the business activities is decreasing,and resource allocation efficiency is low and so on.And relevant countermeasures and suggestions for these problems are put forward.The research results above have some reference value for other financial performance evaluation management in online education field.