Bond-Issuer Credit Rating with Grammatical Evolution

  • Anthony Brabazon
  • Michael O’Neill
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3005)


This study examines the utility of Grammatical Evolution in modelling the corporate bond-issuer credit rating process, using information drawn from the financial statements of bond-issuing firms. Financial data, and the associated Standard & Poor’s issuer-credit ratings of 791 US firms, drawn from the year 1999/2000 are used to train and test the model. The best developed model was found to be able to discriminate in-sample (out-of-sample) between investment-grade and junk bond ratings with an average accuracy of 87.59 (84.92)% across a five-fold cross validation. The results suggest that the two classifications of credit rating can be predicted with notable accuracy from a relatively limited subset of firm-specific financial data, using Grammatical Evolution.


Total Asset Credit Rating Debt Ratio Bond Rating Grammatical Evolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Anthony Brabazon
    • 1
  • Michael O’Neill
    • 2
  1. 1.Dept. Of AccountancyUniversity College DublinIreland
  2. 2.Biocomputing-Developmental Systems Dept. Of Computer Science & Information SystemsUniversity of LimerickIreland

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