| Since the2008financial crisis, China’s enterprises have experienced a series ofchallenges, and many enterprises are facing the risk of bankruptcy, especially thehigh-tech enterprises. Because of this, we expand the research on early warningabout risk-based high-tech enterprises in the financial crisis.In the research of domestic risk early warning model, most scholars focus on theinspection of what financial indicators more accurate in prediction. Their domesticdata directly substituted into the existing model, ignoring the different environmentat home and abroad. The scientific nature of the model has to be verified.At thesame time, because of the lack of model portfolio prediction idea, they rarely build atraditional statistical analysis methods combined forecasting model.In the sampl ing,most of the studies are directly selected listed companies ST enterprise, not furthersubdivided. And the majority of research is to study the financial indicators whileother risks of the enterprise are not measured.This article draws on the research experience of the scholars in the past, to builda combination model of the principal component analysis and logistic regressionanalysisof warning model, and apply the significance test analysis indicatorsapplicable. Thus, it is possible to greatly improve the accuracy of the model. Theintroduction of non-financial indicators in the index system construction and toconsider the macro-environmental factors and corporate governance factors in thefinancial crisismakes high-tech enterprise risk research more targeted.The principal component logistic regression early warning model can change thestatic regulatory and after regulation, to form a dynamic early warningmechanism.For their own degree of financial risk, high-tech enterprises can adoptreasonable and effective measures to avoid financial crisis.Therefore, early warningmodel can help China’s high-tech enterprises to enhance their ability to resist risks.This thesis is divided into five parts.Chapter1is Introduction.This chapter includes research the background,significance, domestic and international literature review, research ideas, methodsand research framework.In the literature review section, it reviews the researchstatus, including risk early warning system and put forward the point of view of this articlebased on a critical analysis of these results,which is established a risk warningprincipal component logistic regression analysis model of high-tech enterprises.The second chapter is the theory of high-tech enterprises in the financial crisisand risk warning.It introduces the related concepts of high-tech enterprises and thedevelopment status of the financial crisis and the financial crisis on the impact ofhigh-tech enterprises.Finally, it explains in detail the theory of risk warning.The third chapter is to build risk warning model based on logistic technology.Itincludes the basic idea of the logistic model and related concepts and the specificdefinition of the samples in this study.Finally, it describes the principal componentanalysis and selection of indicators.The fourth chapter is the empirical analysis.It selected listed companies inShanghai and Shenzhen as an analytical sample.Consider the characteristics of thehigh-tech enterprises, it selected21financial variables, including solvency indicatorsTrading indicators, profitability indicators, the market price ratio, the level of riskindicators, development capacity indicators, and four non-financial indicatorsincluding corporate governance goals and overall economic indicators. After verifiedthe sensitivity of the21financial indicators of risk warning by significance test, itscreened the13strong sensitivity financial indicators, and then principal componentanalysis integrated into five principal components factor.First divide high-techenterprises into the ST group and non-ST group, and take principal component factorinto the logistic regression model for model fitting, then form the final warningmodel.Finally, take prediction test and fitting goodness of inspection on the earlywarning modelto demonstrate the practical value of early warning modelThe model contains financial indicators’ prediction accuracy is94.2%.the modelcontains financial indicators and non-financial indicators’ prediction accuracy is88.9%.The model predicted better results, indicating that the early warning modelcan be applied in practice, and has high practical value.The fifth chapter is the conclusion and outlook.This chapter summarizes theresults of this study, the inadequacies and research prospects... |
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