Research On The Financial Crisis Early Warning Model Of Listed Companies

While the dog-eat-dog market economy is pregnant with the opportunity for development,there are countless risk and crisis bided in it.As to listed company,the situation that it is titled with "ST" plate and even forced to quit listing because of financial crisis grows more and more seriously.It is not only the threat to existence and development of the company failing into the financial crisis,but also brings enormous loss to investors and creditors.So to establish a financial crisis early warning index system which is disrelated and covered amount of information,and build up a financial crisis early warning model,has become the important aspect of stabilifying the development of stock market,national economy,and society.In the empirical research part of this paper,we have separately used Logistic Early Warning Model,Genetic Algorithm and Neural Network to set up and test the early warning model.There are five parts in this paper.Firstly,background of research of financial risk and research significance is introduced,and the statue of research over the world is further introduced.Secondly,we have introduced the research theory of business financial crisis,and summarized the development of enterprises,meaning of corporate financial crisis and the formation of enterprise financial crisis.Thirdly,related theory of financial crisis early-waming is expatiated,and qualitative and quantitative methods of financial crisis early-warning are respectively reviewed.Fourthly,we selected the variables and samples to design the research,and extracted principal component factors.The fifth part builds up the financial crisis early warning models by Logistic Early Warning Model,BP Neural Network and GA-BP Neural Network after extracting principal component factors.And the empirical results are compared to get a conclusion.At last,we summarize the research conclusion and the shortcomings of our study and suggestments for further study.The main characteristic and innovation of this paper lie in two aspects.Firstly,the index system is more integrate for including non-financial indexes.Secondly,we build up a financial crisis early warning model,which combines Genetic Algorithm and BP Neural Network.Thirdly,we apply new matching method to select sample,and the applicability of the model increase evidently.