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Company rating with support vector machines
Department of Economics and Finance, Brunel University London, Uxbridge, United Kingdom.
Center for Applied Statistics and Economics, Humboldt-Universität zu Berlin, Berlin, Germany.
Jönköping University, Jönköping International Business School, JIBS, Economics. German Institute for Economic Research, Berlin, Germany.ORCID iD: 0000-0003-3879-7361
2017 (English)In: Statistics and Risk Modeling, ISSN 2193-1402, Vol. 34, no 1-2, p. 55-67Article in journal (Refereed) Published
Abstract [en]

This paper proposes a rating methodology that is based on a non-linear classification method, a support vector machine, and a non-parametric isotonic regression for mapping rating scores into probabilities of default. We also propose a four data set model validation and training procedure that is more appropriate for credit rating data commonly characterised with cyclicality and panel features. Tests on representative data covering fifteen years of quarterly accounts and default events for 10,000 US listed companies confirm superiority of non-linear PD estimation. Our methodology demonstrates the ability to identify companies of diverse credit quality from Aaa to Caa-C. 

Place, publisher, year, edition, pages
Walter de Gruyter, 2017. Vol. 34, no 1-2, p. 55-67
Keywords [en]
Bankruptcy, Company rating, Probability of default, Support vector machines
National Category
Economics
Identifiers
URN: urn:nbn:se:hj:diva-45451DOI: 10.1515/strm-2012-1141ISI: 000401809700004Scopus ID: 2-s2.0-85020485574OAI: oai:DiVA.org:hj-45451DiVA, id: diva2:1340782
Available from: 2019-08-06 Created: 2019-08-06 Last updated: 2019-08-06Bibliographically approved

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Schäfer, Dorothea

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