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Research on internal credit ratings for listed companies

Xubiao He (College of Management, Huazhong University of Science and Technology, Wuhan, People's Republic of China)
Pu Gong (College of Management, Huazhong University of Science and Technology, Wuhan, People's Republic of China)
Chunxun Xie (Shanghai University of Engineering Science, People's Republic of China)

Kybernetes

ISSN: 0368-492X

Article publication date: 17 October 2008

936

Abstract

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 (ROC) 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.

Keywords

Citation

He, X., Gong, P. and Xie, C. (2008), "Research on internal credit ratings for listed companies", Kybernetes, Vol. 37 No. 9/10, pp. 1339-1348. https://doi.org/10.1108/03684920810907634

Publisher

:

Emerald Group Publishing Limited

Copyright © 2008, Emerald Group Publishing Limited

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