Grammatical Evolution (GE) is a novel data driven,
model induction tool, inspired by the biological
genetoprotein mapping process. This study provides an
introduction to GE, and demonstrates the methodology by
applying it to model 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.
%0 Journal Article
%1 Brabazon:2006:I
%A Brabazon, Anthony
%A O'Neill, Michael
%D 2006
%J Informatica
%K Metoda Povzetek: algorithms, evolucije evolution, genetic gramaticne grammatical je klasificiranje kreditov. programming, uporabljena za
%N 3
%P 325--335
%T Credit Classification Using Grammatical Evolution
%U http://ai.ijs.si/informatica/PDF/30-3/07_Brabazon_Credit%20Classification%20Using.pdf
%V 30
%X Grammatical Evolution (GE) is a novel data driven,
model induction tool, inspired by the biological
genetoprotein mapping process. This study provides an
introduction to GE, and demonstrates the methodology by
applying it to model 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.
@article{Brabazon:2006:I,
abstract = {Grammatical Evolution (GE) is a novel data driven,
model induction tool, inspired by the biological
genetoprotein mapping process. This study provides an
introduction to GE, and demonstrates the methodology by
applying it to model 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.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Brabazon, Anthony and O'Neill, Michael},
biburl = {https://www.bibsonomy.org/bibtex/2cb7d7deb3a89f923882a6f932efb31e4/brazovayeye},
interhash = {7bed42ecb627a2c6178bc7ecdf4bf877},
intrahash = {cb7d7deb3a89f923882a6f932efb31e4},
issn = {0350-5596},
journal = {Informatica},
keywords = {Metoda Povzetek: algorithms, evolucije evolution, genetic gramaticne grammatical je klasificiranje kreditov. programming, uporabljena za},
number = 3,
pages = {325--335},
size = {11 pages},
timestamp = {2008-06-19T17:36:52.000+0200},
title = {Credit Classification Using Grammatical Evolution},
url = {http://ai.ijs.si/informatica/PDF/30-3/07_Brabazon_Credit%20Classification%20Using.pdf},
volume = 30,
year = 2006
}