The nature of statistical learning theory
The nature of statistical learning theory
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Application of support vector machines to corporate credit rating prediction
Expert Systems with Applications: An International Journal
Predicting bond ratings using publicly available information
Expert Systems with Applications: An International Journal
A logical analysis of banks' financial strength ratings
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
We compare the ability of ordered choice models and support vector machines to model and predict international bank ratings. Although support vector machines can identify significant determinants we argue that ordered choice models are more reliable for this. Our findings suggest that ratings reflect a bank's financial position, the timing of rating assignment and a bank's country of origin. Accounting for country effects substantially improves predictive performance. We find that support vector machines can produce considerably better predictions of international bank ratings than ordered choice models due to the formers ability to estimate a large number of country dummies unrestrictedly.