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-sample and out-of-sample predictions, where in-sample predictions are devised with a new 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.
Highlights
Nowadays, the use of analytical methods in extracting intelligence from data, in general, and business-related data, in particular, to support decision-making is increasing gaining popularity amongst practitioners
We extend the toolbox of non-parametric predictive methods by proposing a new integrated classifier that performs both in-sample and out-of-sample predictions, where in-sample predictions are devised with a first VIKOR-based classifier and out-of-sample predictions are devised with a case-based reasoning (CBR)-based classifier trained on the risk class predictions provided by the proposed VIKOR-based classifier—see Fig. 1 for a snapshot of the design of the proposed prediction framework
In order to assess the performance of the proposed framework, we considered a sample of 6605 firm-year observations consisting of non-bankrupt and bankrupt UK firms listed on the London Stock Exchange (LSE) during 2010–2014 excluding financial firms and utilities as well as those firms with less than 5 months lag between the reporting date and the fiscal year
Summary
Business School, University of Edinburgh, 29 Buccleuch Place, Edinburgh EH8 9JS, UK 2 School of Management, University of St Andrews, Gateway Building, North Haugh, St. Andrews KY16 9RJ, UK 3 Faculty of Economics and Business, University of Oviedo, 33006 Oviedo, Asturias, Spain 4 School of Social Sciences, Heriot-Watt University Edinburgh, Mary Burton Building G.54, Edinburgh EH14 4AS, UK
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