Tourism Enterprises Credit Evaluation Method Study

With the rapidly growth of market economy, the immature credit system has turn into one of the most remarkable restriction factors. The opaque credit situation not only constrains the enterprise's financing, but also does harm to their long-term development, especially for the tourism company. It is the asymmetry information that the consumer not trust in them. This paper does some research on the credit evaluation in order to provide some references for the corporation credit system.First, finance information is the basic information to assess the company's credit situation. We can get the credit status through the finance ratio in the static analysis. We must extract the valuable information from the numerous finance indexes, the second chapter uses the multiple static analysis to avoid Linear correlation in traditional linear regression, and extract the main factors of credit evaluation through factor analysis, then assort them after imposing counterpart weight to main factors. This method can not only assess the comprehensive credit situation, but also analyze the factors separately. Finally, this paper makes classification to the tourism company through k-mean cluster method. In addition, this paper takes the improved traditional credit score model which already has been taken into business practice to validate the accuracy of multiple statistic models. The third chapter makes a positive analysis to Tourism Company by RISKCALC method.If the company has a continuum trade data in the open market, we can estimate the market value of the company and get the credit situation by the volatility of them. The fifth chapter analyses it by structure model. First, get the equity return's conditional standard volatility by GARCH model; Second, estimate the latent market value and its volatility in KMV-Merton model through improved iteration process, then evaluate the credit situation according their EDF and comparatively analyses two of default distances getting from two kind of models; Finally, empirically analyze the credit situation of the twenty tourism listed companies.In order to improve the accuracy of the credit judgment, we use the linear combination assess model with no negative weight to get the weight of each model. The above two methods are based on the hypothesis that the finance information and market information can reflect the credit situation, but one effective credit evaluation must include every aspect of the company. The fourth chapter makes use of the historical data and takes support vector machines to assort the credit condition of the tourism company. First of all, choose eleven basic indexes about the company. Next, estimate these parameters by using the training data. Then get the optimize one which brings about the least error. At last, we get a judge model.