no. 4-5
Automated Credit Rating Prediction in a competitive framework
Gangolf, Claude1, 2 ; Dochow, Robert1 ; Schmidt, Günter1, 3 ; Tamisier, Thomas2
1 Operations Research and Business Informatics Saarland University, 66123 Saarbrücken, Germany.
2 Luxembourg Institute of Science and Technology, 4362 Esch-sur-Alzette, Luxembourg.
3 University of Cape Town, Department of Finance and Tax, Cape Town, South Africa.
RAIRO - Operations Research - Recherche Opérationnelle, Tome 50 (2016) no. 4-5, pp. 749-765.

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.

DOI : 10.1051/ro/2016030
Classification : 49-02
Mots clés : Automated credit rating prediction, competitive analysis, financial bond credit rating
Gangolf, Claude 1, 2 ; Dochow, Robert 1 ; Schmidt, Günter 1, 3 ; Tamisier, Thomas 2

1 Operations Research and Business Informatics Saarland University, 66123 Saarbrücken, Germany.
2 Luxembourg Institute of Science and Technology, 4362 Esch-sur-Alzette, Luxembourg.
3 University of Cape Town, Department of Finance and Tax, Cape Town, South Africa. 
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     title = {Automated {Credit} {Rating} {Prediction} in a competitive framework},
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Gangolf, Claude; Dochow, Robert; Schmidt, Günter; Tamisier, Thomas. Automated Credit Rating Prediction in a competitive framework. RAIRO - Operations Research - Recherche Opérationnelle, Tome 50 (2016) no. 4-5, pp. 749-765. doi : 10.1051/ro/2016030. http://archive.numdam.org/articles/10.1051/ro/2016030/

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