| As we know, China has entered the post financial crisis era. Affected by theEuropean debt crisis in2010, a lot of export-oriented enterprises with small andmedium size have closed down, and the export has been hindered, which leads to theincrease of bank credit default cases and brings a major influence to the financialindustry. As the companies in the small and medium-sized board are set up in recentyears, their performance evaluation is difficult and they have higher possibility of theoutbreak of the market manipulation, insider trading behavior and financial crisis.Thus it is necessary to establish the financial early-warning model system to avoid thefinancial crisis, protect the interests of investors and maintain the stability of thecapital market, especially to solve the high rate of non-performing assets of banksproblem.Now the research on financial early warning model field in our country relieson the traditional financial indicators. Because the default database system in ourcountry has not yet been established, some domestic scholars attempt to study theintroduction of KMV model,but the research is mostly limited to the questionwhether KMV model can be effectively used in China and the latest researchachievements in credit risk measurement has not yet truly combined with the financialearly warning model of China’s enterprise.Based on the previous research, the textcombines the traditional financial early-warning model and credit risk measurementmodel together, introduce the index of KMV’s distance to default and theexpected default rate for the purpose of improving the prediction accuracy of thefinancial early warning model.Based on the elaboration of the Logistic regression model and KMV model, thetext introduce the index of the distance to default (DD) in KMV model to set up a newfinancial pre-warning model. Regarding the time factor as longitudinal factor,compares the difference in the prediction accuracy between two types of model pre-and post-the introduction of the index of distance to default,examines the impact onthe predictive and explanatory ability of financial early warning model and thenanalyzes the effect of distance to default on the model.Firstly, the text modifies parameters on the KMV model according to the actualsituation of China’s capital market.One is the market value of shareholders’ equity, China has split share structure with the common presence of tradable and nontradable shares. Mostly non tradable shares are in limited sales period of the splitshare structure reform. The price of non tradable shares is based on net asset pricingmethod. Two is the default point DP. When the company’s asset value is lower than acritical value, the default to creditors and the company occurs, and the value of assetcorresponding to the critical value is called the default point DP..Secondly, the text analyses and tests the output of the KMV model,the distanceto default and the expected default rates calculated on default distance. Throughcorrelation analysis, there is a negative correlation between DD and EDF andsignificant test showed that DD and EDF were significantly different in the0.05levelof significance.Thirdly, the text will screen the22basic indicators. This paper will use thenormality test and t-test to select the variables which can represent the model with agood argument, and then eliminate the variables with multicollinearity throughfactor analysis to selected the more representative variables.。Finally, the text constructs the final regression model. Based on the researchabove, the text then uses Logistic regression models for2010and2011to constructrespectively the early warning model and the default warning distance model and theformer is based on the financial indicators. Through the transverse and longitudinalanalysis of the two discriminant model, the text reaches its conclusions: theintroduction of KMV model can improve the explanation and discrimination ability ofthe financial early warning model and improve the forecast accuracy. |
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