A new VIKOR-based in-sample-out-of-sample classifier with application in bankruptcy prediction
Abstract
Nowadays, business analytics has become a common buzzword in a range of industries, as companies are increasingly aware of the importance of high quality predictions to guide their pro-active planning exercises. The financial industry is amongst those industries where predictive analytics techniques are widely used to predict both continuous and discrete variables. Conceptually, the prediction of discrete variables comes down to addressing sorting problems, classification problems, or clustering problems. The focus of this paper is on classification problems as they are the most relevant in risk-class prediction in the financial industry. The contribution of this paper lies in proposing a new classifier that performs both in-sampleandout-of-samplepredictions,wherein-samplepredictionsaredevisedwithanew VIKOR-based classifier and out-of-sample predictions are devised with a CBR-based classifier trained on the risk class predictions provided by the proposed VIKOR-based classifier. The performance of this new non-parametric classification framework is tested on a dataset of firms in predicting bankruptcy. Our findings conclude that the proposed new classifier can deliver a very high predictive performance, which makes it a real contender in industry applications in finance and investment.
Citation
Ouenniche , J , Bouslah , K B H , Perez-Gladish , B & Xu , B 2019 , ' A new VIKOR-based in-sample-out-of-sample classifier with application in bankruptcy prediction ' , Annals of Operations Research , vol. First online . https://doi.org/10.1007/s10479-019-03223-0
Publication
Annals of Operations Research
Status
Peer reviewed
ISSN
0254-5330Type
Journal article
Rights
© The Author(s) 2019 This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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