摘要：We compare the ability of ordinal choice models and support vector machines to model and predict international bank ratings. Although support vector machines can identify the significant determinants of ratings we argue that ordered choice models are more reliable for this purpose. Our findings suggest that ratings reflect a bank’s financial position, the timing of when the rating was made and a bank’s country of origin. Accounting for country effects in the model was found to be particularly important not least because they substantially improve the predictive performance of the models. We find that support vector machines can produce considerably better insample predictions of international bank ratings than the standard method currently used for this purpose, ordered choice models. This appears to be 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 small sample size. Given that the primary purpose of modelling ratings is prediction this is an important result.
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