Research on internal credit ratings for listed companies

Purpose - The purpose of this paper is to simulate internal credit ratings based on stock market data and gain the credit information about listed companies. Design/methodology/approach - According to the concept of default distance, default probability of listed companies is obtained from stock's price process based on generalized autoregressive conditionally heteroscedastic-M model with the generalized error distribution, then credit ratings based on the default probability is built. Moreover, the model's validity is proved using the statistical tests and nonparametric receiver operating characteristic (RCC) curve method. Findings - Application of the proposed methodology on data from Chinese stock market illustrates that default probability model can identify the credit risk of listed companies effectively using the statistical tests and nonparametric ROC curve method. The results from simulating credit ratings based on default probability are positive correlated with the corresponding results from Xinhua Far East China Ratings. Originality/value - The internal credit ratings-based default probability can reflect the change of credit quality for listed companies according to market information. For listed companies, especially which possibly suffer from accounting manipulations, the ratings will help investors and supervisors gain their credit information in time.