Bond-Issuer Credit Rating with Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{brabazon:evows04,
  author =       "Anthony Brabazon and Michael O'Neill",
  title =        "Bond-Issuer Credit Rating with Grammatical Evolution",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
                 {EvoIASP}, {EvoMUSART}, {EvoSTOC}",
  year =         "2004",
  month =        "5-7 " # apr,
  editor =       "Guenther R. Raidl and Stefano Cagnoni and 
                 Jurgen Branke and David W. Corne and Rolf Drechsler and 
                 Yaochu Jin and Colin R. Johnson and Penousal Machado and 
                 Elena Marchiori and Franz Rothlauf and George D. Smith and 
                 Giovanni Squillero",
  series =       "LNCS",
  volume =       "3005",
  address =      "Coimbra, Portugal",
  publisher =    "Springer Verlag",
  publisher_address = "Berlin",
  pages =        "270--279",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, evolutionary computation",
  ISBN =         "3-540-21378-3",
  DOI =          "doi:10.1007/978-3-540-24653-4_28",
  abstract =     "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)percent 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.",
  notes =        "EvoWorkshops2004",
}

Genetic Programming entries for Anthony Brabazon Michael O'Neill

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