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

• Published in: Technological forecasting & social change 2018-06, Vol.131, p.111-116
• Main Authors: Ouenniche, Jamal, Pérez-Gladish, Blanca, Bouslah, Kais
• Format: Article
• Language: English
• Subjects: Banking; Bankruptcy; Classifiers; Decision making; Empirical analysis; Industrial applications; K-nearest neighbour classifier; Out-of-sample prediction; Performance prediction; Predictions; Research applications; Risk analysis; Risk assessment; Risk class prediction; Stock exchanges; Studies; TOPSIS classifier
• ISSN: 0040-1625; 1873-5509
• DOI: 10.1016/j.techfore.2017.05.034

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. •TOPSIS classifiers lack a proper framework for out-of-sample evaluation.•An in-sample-out-of-sample framework for TOPSIS classifiers is proposed.•Its performance is tested on a UK dataset of bankrupt and non-bankrupt firms.•Results show an outstanding predictive performance in-sample and out-of-sample.•Out-of-sample framework makes TOPSIS classifiers real contenders for practitioners.