Bond-Issuer Credit Rating with Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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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