The New Exploration Of Listed Company's Financial Crisis Prediction

Since December 19,1990 and July 3,1991,the Shanghai Stock Exchange and Shenzhen Stock Exchange set up to provide stock trading service,the Chinese security market has experienced rapid development course. In this rapid development process,the number of listed companies with special treatment has been increasing,because of financial crisis.In order to protect the interests of stakeholders, and enhance listed company's ability to resist risk of financial crisis,the research of listed company's financial crisis prediction model has been the focus of theory and practition community. As the indicator of business operation, the research of financial crisis not only has high academic value, but also has great practical significance.Prediction of enterprise financial crisis is through the selection of indicators with information content, using certain methods to build prediction models.Among them how accurately choose the predictor and analysis method is the key to the research, however, the traditional indicators of screening methods (such as T tests,stepwise regression, etc.) and prediction models have some defects,hindering further development of enterprise financial crisis prediction.For the interest on these two issues and concern about the value of their applications,based on the review and sum up of the literature at home and abroad,this paper constructed a measure of corporate long—term solvency indicator,introducted signal-noise method and through the improvement of Naive Bayesian analysis,etc.Building linear and nonlinear prediction models,and comparing level of prediction accuracy of each model.This paper take manufacturing sector as example, using SAS statistical software arid the Data Miner software,constructed different financial crisis prediction models.-In this paper,by screening traditional indicator choosing methods (T test and stepwise regression),finding defects in T test are that it can not give a single indicator specific predictive value,and with huge number indicators,affecting the model building effect,increasing the cost of model using.The drawback of stepwise regression method derived from the indicators estimated coefficients can not account for the relationship between targets and indicators,meanwhile still unable to predict the amount of information given in a single indicator,this paper introduces the signal to noise ananlysis to solve these problems.The article also used indicators selected by the stepwise regression and signal to noise analysis constructing multivariate linear discriminant analysis model and a naive Bayesian model, empirical results show that the prediction accuracy of models using indicators selected by signal to noise analysis is higher than that of stepwise regression,and the decision tree model constructed by indicators selected by signal to noise analysis has a high predictive ability,confirmed the signal to noise analysis'advantage. Past studies have generally used the company's cross-sectional data to build models,different study samples and research data may get different predictor variables,resulting in poor extrapolation power of predictive models,and is difficult to reflect the gradual process for business changing to financial crisis.This paper analyzes the reasons for companies in financial crisis,proposing a new predictor variable,and the use of financial statements for time-series data and Bayesian inference methods to estimate the variable,the single-variable forecasting models and multi-variable forecasting models constructed by it achieving higher prediction accuracy,providing a reference for financial crisis prediction research.