A new VIKOR-based in-sample-out-of-sample classifier with application in bankruptcy prediction
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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.
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
Annals of Operations Research
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