Issue |
RAIRO-Oper. Res.
Volume 50, Number 4-5, October-December 2016
Special issue - Advanced Optimization Approaches and Modern OR-Applications
|
|
---|---|---|
Page(s) | 749 - 765 | |
DOI | https://doi.org/10.1051/ro/2016030 | |
Published online | 03 November 2016 |
Automated Credit Rating Prediction in a competitive framework
1 Operations Research and Business
Informatics Saarland University, 66123
Saarbrücken, Germany.
cg@orbi.uni-saarland.de, rd@orbi.uni-saarland.de, ds@orbi.uni-saarland.de
2 Luxembourg Institute of Science and
Technology, 4362
Esch-sur-Alzette, Luxembourg.
claude.gangolf@list.lu, thomas.tamisier@list.lu
3 University of Cape Town, Department
of Finance and Tax, Cape
Town, South Africa.
Received:
15
May
2015
Accepted:
23
April
2016
Automated credit rating prediction (ACRP) algorithms are used to predict the ratings of bonds without having to trust one rating agency, like Moody’s, Fitch or S&P. Nevertheless, for the moment, the accuracy of ACRP algorithms is investigated by empirical tests. In this paper, the framework for a competitive analysis is set and afterwards in this framework, the definition of competitive ACRP algorithms and its demonstration is given. In this way, for a competitive ACRP algorithm, a worst-case guarantee concerning the misclassification error is offered. Furthermore, several ACRP algorithms from the literature are compared according their competitiveness.
Mathematics Subject Classification: 49-02
Key words: Automated credit rating prediction / competitive analysis / financial bond credit rating
© EDP Sciences, ROADEF, SMAI 2016
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