Regression Research Between Credit Risk Measurement And Historical Financial Ratios

How to measure credit risk is one of the tough topics in credit risk management. It has the direct and substantial relationship with financial situation. This dissertation makes a multiple regression between default rate of listed companies and financial ratios to connect future credit development and historical financial situation, which helps banks to identify credit crisis, but also use as a reference to non-listed companies'credit risk measurement.First, the process of default rate model based on option-pricing theory was introduced with detailed discussion on parameters'selection.10 ST companies in 2005 and the same amounts of non-ST companies listed in Shanghai Stock Exchange which issue A shares as the sample to compute the one-year default rate with 31st December 2004 as the basic date. The compared graph and Paired-Sample T Test analyze the applicability of this model in China. The result shows that the default rates are obviously different between ST and non-ST companies with KMV model based on option-pricing theory, in other words this model could conduct a good prediction to default rate of listed companies.Then, the default rates of 15 companies in retail field are computed and 20 related financial indicators are selected referred by researches on financial distress at domestic and abroad. Afterward, relationship between default rate and financial indicators is produced by stepwise regression. During that process, some problems should be dealt with, that is case-wise diagnostics, collinear, heteroscedasticity and hypothesis of the model. At last there are 7 indicators in the regression equation; they are Total Debts/ Total Asset, Current Liability/ Total Debts, Main Business Income/ Total Asset, Increase Rate of Total Asset, Current Asset/ Main Business Income, scale of Asset and Operating Profits/ Main Business Income.Finally, 5 listed companies of retail field as a test sample analyze the effectiveness of the regression equation. The result shows that financial ratios play a positive role in prediction and puts forward a new direction to credit risk measurement of non-listed companies.