Expert systems for bond rating: a comparative analysis of statistical, rule‐based and neural network systems

An important problem in financial investment is the classification of bonds based on the likelihood that the issuing company may default on the promised payments. Much effort has been invested into simulating the bond rating process using statistical tools. A weakness of these tools is the requirement of statistical assumptions which may not be appropriate for the bond rating problem. In this paper we present results of a study comparing an artificial neural network system, a rule‐based expert system and statistical techniques applied to the bond rating problem. The bond rating process is simulated by using published financial data.