%0 Conference Paper %1 brabazon:evows04 %A Brabazon, Anthony %A O'Neill, Michael %B Applications of Evolutionary Computing, EvoWorkshops2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC %C Coimbra, Portugal %D 2004 %E Raidl, Guenther R. %E Cagnoni, Stefano %E Branke, Jurgen %E Corne, David W. %E Drechsler, Rolf %E Jin, Yaochu %E Johnson, Colin R. %E Machado, Penousal %E Marchiori, Elena %E Rothlauf, Franz %E Smith, George D. %E Squillero, Giovanni %I Springer Verlag %K algorithms, computation evolution, evolutionary genetic grammatical programming, %P 270--279 %T Bond-Issuer Credit Rating with Grammatical Evolution %V 3005 %X 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. %@ 3-540-21378-3

@inproceedings{brabazon:evows04, 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)% 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Coimbra, Portugal}, author = {Brabazon, Anthony and O'Neill, Michael}, biburl = {https://www.bibsonomy.org/bibtex/20ca9dc8e86998d9411614b2facdf37ac/brazovayeye}, booktitle = {Applications of Evolutionary Computing, EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}}, editor = {Raidl, Guenther R. and Cagnoni, Stefano and Branke, Jurgen and Corne, David W. and Drechsler, Rolf and Jin, Yaochu and Johnson, Colin R. and Machado, Penousal and Marchiori, Elena and Rothlauf, Franz and Smith, George D. and Squillero, Giovanni}, interhash = {f837f8d2f594de57317ead796eb42d89}, intrahash = {0ca9dc8e86998d9411614b2facdf37ac}, isbn = {3-540-21378-3}, keywords = {algorithms, computation evolution, evolutionary genetic grammatical programming,}, month = {5-7 April}, notes = {EvoWorkshops2004}, pages = {270--279}, publisher = {Springer Verlag}, publisher_address = {Berlin}, series = {LNCS}, timestamp = {2008-06-19T17:36:51.000+0200}, title = {Bond-Issuer Credit Rating with Grammatical Evolution}, volume = 3005, year = 2004 }