An out-of-sample framework for TOPSIS-based classifiers with application in bankruptcy prediction

Jamal Ouenniche, Blanca Pérez-Gladish, Kais Bouslah

Research output: Contribution to journalArticlepeer-review

Abstract

Since the publication of the seminal paper by Hwang and Yoon (1981) proposing Technique for Order Performance by the Similarity to Ideal Solution (TOPSIS), a substantial number of papers used this technique in a variety of applications requiring a ranking of alternatives. Very few papers use TOPSIS as a classifier (e.g. Wu and Olson, 2006; Abd-El Fattah, 2013) and report a good performance as in-sample classifiers. However, in practice, its use in predicting discrete variables such as risk class belonging is limited by the lack of an out-of-sample evaluation framework. In this paper, we fill this gap by proposing an integrated in-sample and out-of-sample framework for TOPSIS classifiers and test its performance on a UK dataset of bankrupt and non-bankrupt firms listed on the London Stock Exchange (LSE) during 2010-2014. Empirical results show an outstanding predictive performance both in-sample and out-of-sample and thus opens a new avenue for research and applications in risk modelling and analysis using TOPSIS as a non-parametric classifier and makes it a real contender in industry applications in banking and investment. In addition, the proposed framework is robust to a variety of implementation decisions.
Original languageEnglish
Pages (from-to)111-116
JournalTechnological Forecasting and Social Change
Volume131
Early online date7 Jun 2017
DOIs
Publication statusPublished - 30 Jun 2018

Keywords

  • out-of-sample prediction
  • TOPSIS classifier
  • k-nearest neighbour classifier
  • bankruptcy
  • risk class prediction

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