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

Jamal Ouenniche, Blanca Pérez-Gladish and Kais Bouslah

Technological Forecasting and Social Change, 2018, vol. 131, issue C, 111-116

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 et al., 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.

Keywords: Out-of-sample prediction; TOPSIS classifier; K-nearest neighbour classifier; Bankruptcy; Risk class prediction (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:131:y:2018:i:c:p:111-116

DOI: 10.1016/j.techfore.2017.05.034

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