A new classifier based on the reference point method with application in bankruptcy prediction
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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 nonparametric 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 data set of bankrupt and nonbankrupt 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 nonparametric 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.
Ouenniche , J , Bouslah , K , Cabello , J M & Ruiz , F 2018 , ' A new classifier based on the reference point method with application in bankruptcy prediction ' Journal of the Operational Research Society , vol. 69 , no. 10 , pp. 1653-1660 . https://doi.org/10.1057/s41274-017-0254-z
Journal of the Operational Research Society
© The Operational Research Society 2017. This work has been made available online in accordance with the publisher’s policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1057/s41274-017-0254-z
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