Application Of Logit Model With Fuzzy Mathematics And Information Diffusion In Credit Rating

With the development of economic globalization, people concerned about financial risks problems more and more. In the financial risks, the most concerned by the people is credit risk. China entered the WTO, for the entry of foreign companies, domestic banks, state-owned enterprises and listed companies had to increase the importance of the issue of financial risk, or can not meet the global challenges of multinational corporations.Financial risk assessment model mainly through the following phases: identification model, logical model and artificial intelligence model, the foreign back in the early twentieth century began to research the financial risks. Our development is relatively slow, until the twentieth century, scholars of China reached the level which can be applied, but in recent years, for scholars and major companies's more attention, the domestic level also has made substantial progress.This paper is structured as follows:The first chapter describes the introduction of financial risks and the research background and research status. The second chapter describes the form of Logit model and parameter estimates systemly.Chapter III describes the basic knowledge of fuzzy mathematics amd information diffusion.Chapter IV describes the Logit model with fuzzy mathematics and information diffusion, and the basic form and application in credit rating and financial warning system. Section 4.3 introduces the preliminary knowledge of fuzzy Logit model and parameter estimation methods. However, this method is too complicated, and there is no statistical software that can be used, so the computing process is cumbersome.Finally, the paper selected the 70 listed company's financial data, the use of fuzzy mathematics and information diffusion Logit model was constructed.Make Fuzzy mathematics and information diffusion into Logit model to estimate the results, the results of the model is not just a certain value, but a membership function. It gives the sample to be predicted for the membership of each credit rating, making people know what level of probability sample is large, the extent of large, what level is the possibility of small, small to what extent. This result is clearly more with the requirements of the people in line.