Predicting bond ratings using publicly available information

Authors:
Kee S. Kim
Affiliations:
Finance Department, Southwest Missouri State University, 901 S. National Ave, Springfield, MO 65804, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2005

Citing 2
Cited 4

A comparative analysis of rule-based, neural network, and statistical classification systems for the bond rating problem

A comparative analysis of rule-based, neural network, and statistical classification systems for the bond rating problem
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Application of support vector machines to corporate credit rating prediction

Expert Systems with Applications: An International Journal
Are rating agencies' assignments opaque? Evidence from international banks

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Municipal credit rating modelling by neural networks

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A note comparing support vector machines and ordered choice models' predictions of international banks' ratings

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Abstract

This paper developed a model to predict bond ratings using financial and non-financial variables although the rating agencies believe that bond ratings could not be replicated quantitatively by using a computer model. The model used an artificial intelligence (AI) technique that is non-parametric and designed to capture a dynamic relationship between input and output variables. The results showed that bond rating could be assessed quite accurately and critical variables were successfully identified. In addition the investment grade bonds were successfully distinguished from the speculative bonds.