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dc.contributor.authorOuenniche, Jamal
dc.contributor.authorPérez-Gladish, Blanca
dc.contributor.authorBouslah, Kais
dc.identifier.citationOuenniche , J , Pérez-Gladish , B & Bouslah , K 2018 , ' An out-of-sample framework for TOPSIS-based classifiers with application in bankruptcy prediction ' , Technological Forecasting and Social Change , vol. 131 , pp. 111-116 .
dc.identifier.otherPURE: 250153868
dc.identifier.otherPURE UUID: ae39f5ec-1fab-4134-b5f4-00b03bb3aa1f
dc.identifier.otherScopus: 85020478107
dc.identifier.otherWOS: 000430519300010
dc.identifier.otherORCID: /0000-0001-8407-8929/work/82179574
dc.descriptionThis work was conducted while Prof. Pérez-Gladish was a visitant researcher at the Business School of The University of Edinburgh. She would like to thank the Spanish Ministry of Education Culture and Sport for its financial support within the framework of its International Mobility Program for Senior Researchers “Salvador de Madariaga” (Reference PRX16-0169).en
dc.description.abstractSince 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.
dc.relation.ispartofTechnological Forecasting and Social Changeen
dc.rights© 2017 Elsevier Ltd. All rights reserved. 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 may differ slightly from the final published version. The final published version of this work is available at
dc.subjectOut-of-sample predictionen
dc.subjectTOPSIS classifieren
dc.subjectK-nearest neighbour classifieren
dc.subjectRisk class predictionen
dc.subjectHA Statisticsen
dc.subjectHB Economic Theoryen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.titleAn out-of-sample framework for TOPSIS-based classifiers with application in bankruptcy predictionen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews.School of Managementen
dc.contributor.institutionUniversity of St Andrews.Centre for the Study of Philanthropy & Public Gooden
dc.contributor.institutionUniversity of St Andrews.Centre for Responsible Banking and Financeen
dc.description.statusPeer revieweden

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