Bond-Issuer Credit Rating with Grammatical Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @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
Citations