A note comparing support vector machines and ordered choice models' predictions of international banks' ratings

Bellotti, Tony, Matousek, Roman, Stewart, Chris (2011) A note comparing support vector machines and ordered choice models' predictions of international banks' ratings. Decision Support Systems, 51 (3). pp. 682-687. ISSN 0167-9236. (doi:10.1016/j.dss.2011.03.008) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:39090)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL: http://dx.doi.org/10.1016/j.dss.2011.03.008

Abstract

We find that support vector machines can produce notably better predictions of international bank ratings than the standard method currently used for this purpose, ordered choice models. This appears due to the support vector machine's ability to estimate a large number of country dummies unrestrictedly, which was not possible with the ordered choice models due to the low sample size. Â© 2011 Elsevier B.V. All rights reserved.

Item Type:	Article
DOI/Identification number:	10.1016/j.dss.2011.03.008
Additional information:	Unmapped bibliographic data: AD - Department of Mathematics, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom [Field not mapped to EPrints] AD - Centre for EMEA Banking, Finance and Economics, London Metropolitan Business School, London Metropolitan University, 84 Moorgate, London, EC2M 6SQ, United Kingdom [Field not mapped to EPrints] AD - London Metropolitan Business School, London Metropolitan University, 84 Moorgate, London, EC2M 6SQ, United Kingdom [Field not mapped to EPrints] JA - Decis Support Syst [Field not mapped to EPrints]
Uncontrolled keywords:	International bank ratings, Ordered choice models, Support vector machines, Bank ratings, Choice model, Sample sizes, Standard method, Forecasting, Gears, Vectors, Support vector machines
Subjects:	H Social Sciences > HG Finance
Divisions:	Divisions > Kent Business School - Division > Kent Business School (do not use)
Depositing User:	Tracey Pemble
Date Deposited:	08 Apr 2014 14:50 UTC
Last Modified:	05 Nov 2024 10:23 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/39090 (The current URI for this page, for reference purposes)

University of Kent Author Information

Matousek, Roman.

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