A new classifier based on the reference point method with application in bankruptcy prediction

Jamal Ouenniche*, Kais Bouslah, Jose Manuel Cabello, Francisco Ruiz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
3 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)1653-1660
Number of pages8
JournalJournal of the Operational Research Society
Issue number10
Early online date21 Jun 2017
Publication statusPublished - 12 Jan 2018


  • Bankruptcy
  • In-sample prediction
  • k-nearest neighbour classifier
  • Out-of-sample prediction
  • Reference point method classifier
  • Risk class prediction


Dive into the research topics of 'A new classifier based on the reference point method with application in bankruptcy prediction'. Together they form a unique fingerprint.

Cite this