Company rating with support vector machines
-
Russ A. Moro
,
Wolfgang K. Härdle
Abstract
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.
Funding source: Deutsche Forschungsgemeinschaft
Award Identifier / Grant number: SFB 649 “Economic Risk”
Funding statement: 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”.
Acknowledgements
We are grateful to Jin-Chuan Duan and Oliver Chen of RMI NUS and Laura Auria and Ralf Körner of the Deutsche Bundesbank for their irreplaceable assistance and valuable suggestions. We also would like to thank participants of the International Risk Management Conferences for their comments and discussion.
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© 2017 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- A double clustering algorithm for financial time series based on extreme events
- Improved algorithms for computing worst Value-at-Risk
- Testing for asymmetry in betas of cumulative returns: Impact of the financial crisis and crude oil price
- Company rating with support vector machines
- Loan pricing under estimation risk
Articles in the same Issue
- Frontmatter
- A double clustering algorithm for financial time series based on extreme events
- Improved algorithms for computing worst Value-at-Risk
- Testing for asymmetry in betas of cumulative returns: Impact of the financial crisis and crude oil price
- Company rating with support vector machines
- Loan pricing under estimation risk
