Fuzzy Comprehensive Evaluation Method In The Financial Early Warning Model

Enterprise financial early-warning research hasalways been a widely noted issue; it has high academic value and greatapplication value. With the global economic integration step speeding up,while the enterprise of our country meets the opportunities of thedevelopment, we will face more intense market competition. There is acompetition; there is the existence of the risk. When the enterprise faces avariety of risk accumulation to a certain degree, it would be in a financialcrisis if taking unsuitable measures. It must be related to the investors andcreditors’ relevant interest by checking the company’s financial state, sothe study of its financial condition extremely important.In this paper, the classical financial distress prediction models bothfrom the domestic and international are reviewed. Based on the summaryof the previous research methods and research results, we do the analysisabout the present situation of the ST company from A-share listedcompanies, through selecting five kinds industries that the financial statusare anomaly and be given special treatment (the communicationsequipment manufacturing, chemical raw materials and chemical productsmanufacturing, textile industry, non-metallic mineral manufacturing,communication and culture industry). Selecting the representative five STcompanies as research samples, and according to the industry and scalematching standard, choosing the5listed companies in the good financialshape constitute sample set. We establish the multivariate financialdistress prediction model by using fuzzy comprehensive evaluation; theexample is used to verify the model’s correctness and effectiveness on thebasis of qualitative analysis do quantitative analysis.The purpose of this paper is constructing Chinese enterprisefinancial early warning model using modern methods, on the based offinancial early warning model in the domestic and overseas. It is neededto analyze the difference and sameness between theoretical and empirical about variety of financial early warning model. Finally we screen out afeasible and accurate financial early warning model.