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Statistics & Risk Modeling

with Applications in Finance and Insurance

Editor-in-Chief: Stelzer, Robert

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Volume 34, Issue 1-2


Company rating with support vector machines

Russ A. Moro
  • Corresponding author
  • Department of Economics and Finance, Brunel University London, Uxbridge UB8 3PH, United Kingdom
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  • Other articles by this author:
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/ Wolfgang K. Härdle
  • Center for Applied Statistics and Economics, Humboldt-Universität zu Berlin, Spandauer Str. 1, 10178 Berlin, Germany
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/ Dorothea Schäfer
Published Online: 2017-04-13 | DOI: https://doi.org/10.1515/strm-2012-1141


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.

Keywords: Bankruptcy; company rating; probability of default; support vector machines

MSC 2010: 62-07; 62G05; 62P05


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About the article

Received: 2012-10-18

Revised: 2016-12-01

Accepted: 2017-03-02

Published Online: 2017-04-13

Published in Print: 2017-06-01

Funding Source: Deutsche Forschungsgemeinschaft

Award identifier / Grant number: SFB 649 “Economic Risk”

The authors gratefully acknowledge the support of this project by the Risk Management Institute of the National University of Singapore (RMI NUS). We thank RMI NUS for providing access to their database of financial statements and default events. Russ A. Moro was financially supported by the RMI NUS and Wolfgang K. Härdle by Deutsche Forschungsgemeinschaft through SFB 649 “Economic Risk”.

Citation Information: Statistics & Risk Modeling, Volume 34, Issue 1-2, Pages 55–67, ISSN (Online) 2196-7040, ISSN (Print) 2193-1402, DOI: https://doi.org/10.1515/strm-2012-1141.

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