The finance industry relies heavily on the risk modelling and analysis toolbox to assess the risk profiles of entities such as individual and corporate borrowers and investment vehicles. Such toolbox includes a variety of parametric and non-parametric methods for predicting risk class belonging. In this paper, we expand such toolbox by proposing an integrated framework for implementing a full classification analysis based on a reference point method; namely, in-sample classification and out-of-sample classification. The empirical performance of the proposed reference point method-based classifier is tested on a UK dataset of bankrupt and non-bankrupt firms. Our findings conclude that the proposed classifier can deliver a very high predictive performance, which makes it a real contender in industry applications in banking and investment. Three main features of the proposed classifier drive its outstanding performance; namely, its non-parametric nature, the design of our RPM score-based cut-off point procedure for in-sample classification, and the choice of a k-Nearest Neighbour as an out-of-sample classifier which is trained on the in-sample classification provided by the reference point method based classifier.
- in-sample prediction
- out-of-sample prediction
- reference point method classifier
- k-nearest neighbour classifier
- risk class prediction