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Article
Forecasting credit ratings using an ANN and statistical techniques
International journal of business studies (2003)
  • Kuldeep Kumar, Bond University
  • John D. Haynes
Abstract
In a liberal environment the conceptual importance of credit rating has increased significantly. The objective of this study is to explore and find out the effect of the financial performance data of a firm relative to the credit rating of a debt issue of that firm. The study also proposes to capture the relationship, if any, between financial performance data and credit rating given by experts in an appropriate model. Financial data relevant to debt issue ratings are obtained from the publications of a premier credit rating agency in India. Data analysis involved the building of a model using conventional multiple linear discriminant analysis and Artificial Neural Network Systems. Artificial Neural Networks (ANN) model was found to be superior to the discriminant analysis model. The ANN model can be used to increase speed and efficiency of the rating process in practical applications. In addition, if experts provide better-input data, it can be relied upon to provide an automatic rating to a significant extent.
Keywords
  • credit rating,
  • rating methodology,
  • discriminant analysis,
  • Artificial Neural Network,
  • experts system
Publication Date
January 1, 2003
Publisher Statement
Published Version.

Kumar, K., & Haynes, J. D. (2003). Forecasting credit ratings using an ANN and statistical techniques. International journal of business studies, 11(1), 91-108.

Access the publisher's website.

2003 HERDC submission. FoR code: 1503

© Copyright International Journal of Business Studies

Reproduced with permission of the International Journal of Business Studies, Edith Cowan University.
Citation Information
Kuldeep Kumar and John D. Haynes. "Forecasting credit ratings using an ANN and statistical techniques" International journal of business studies Vol. 11 Iss. 1 (2003)
Available at: http://works.bepress.com/kuldeep_kumar/30/