Empirical Research On Short-term Credit Risk Measurement Of Public Company Of China

With the fast development of economy in china, the public company, through each kind of investment form, such as annexation, joint venture, single proprietorship, expand their economic domain acceleratly. For making up the fund shortage caused by expantion, the public company expand their financing channels one after another. In recent years, with the standardization of band market in china, the public company gradually high on bond financing, such as the issuing of short-term finance bond and company bond. However, correspondingly, the amount of public company special treated by China Security Regulatory Commission increases year by year. This reflects some problems of public company, such as confusion of inner management, unbalance of governance structure and significant lapsus of capital investment decision, as a result, the public company faces the risk of "withdrawing market", also the potential interest of the creditor and investor faces enormous losses. In order to forcast the credit condition of bond-issuing company in two or three years earlier, so providing some helpful help for the public company in the recognition and controlment of short-term credit risk, the paper uses principal component logistic regression, from quantitative analysis and qualitative analysis, to carry on empirical research in short-term credit risk.At first, according to the principle of ST, the paper chooses 85 ST companies as bad-manage sample, and according to the principle of same scale, same industry and same period, chooses 85 no ST companies as well-manage sample, besides, the paper divides all samples into two kinds:estimate sample and forcast sample. Secondly, according to the factors affect the short-term credit risks of public company, combined with the choosing principle of index variables. At last, the paper builds principal component logistic regression model, and uses forcast samples to inspect order of accuracy.Test result shows, the forcasting exact rate of fore two years model reaches 90.48 percent, and fore three years model reaches 78.57 percent. According to the analysis result, the author propose several conclusions, such as, the principle component analysis can resolve the problem of multicollinearity in the logit regression model; the quantitative analysis and the qualitative analysis should be combined when analyse short-term credit risk; the closer to the period of ST, the higher of forcast predict rate, and so on.