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Variable precision rough set theory and data discretisation: an application to corporate failure prediction


  • Beynon, Malcolm J.
  • Peel, Michael J.


Since the seminal work of Pawlak (International Journal of Information and Computer Science, 11 (1982) 341-356) rough set theory (RST) has evolved into a rule-based decision-making technique. To date, however, relatively little empirical research has been conducted on the efficacy of the rough set approach in the context of business and finance applications. This paper extends previous research by employing a development of RST, namely the variable precision rough sets (VPRS) model, in an experiment to predict between failed and non-failed UK companies. It also utilizes the FUSINTER discretisation method which neglates the influence of an 'expert' opinion. The results of the VPRS analysis are compared to those generated by the classical logit and multivariate discriminant analysis, together with more closely related non-parametric decision tree methods. It is concluded that VPRS is a promising addition to existing methods in that it is a practical tool, which generates explicit probabilistic rules from a given information system, with the rules offering the decision maker informative insights into classification problems.

Suggested Citation

  • Beynon, Malcolm J. & Peel, Michael J., 2001. "Variable precision rough set theory and data discretisation: an application to corporate failure prediction," Omega, Elsevier, vol. 29(6), pages 561-576, December.
  • Handle: RePEc:eee:jomega:v:29:y:2001:i:6:p:561-576

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    References listed on IDEAS

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    1. Apostolos G. Christopoulos & Ioannis G. Dokas & Iraklis Kollias & John Leventides, 2019. "An implementation of Soft Set Theory in the Variables Selection Process for Corporate Failure Prediction Models. Evidence from NASDAQ Listed Firms," Bulletin of Applied Economics, Risk Market Journals, vol. 6(1), pages 1-20.
    2. Malcolm J. Beynon & Mark A. Clatworthy & Michael John Jones, 2004. "The prediction of profitability using accounting narratives: a variable‐precision rough set approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(4), pages 227-242, October.
    3. Boratyńska, Katarzyna & Grzegorzewska, Emilia, 2018. "Bankruptcy prediction in the agribusiness sector: Lessons from quantitative and qualitative approaches," Journal of Business Research, Elsevier, vol. 89(C), pages 175-181.
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    6. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    7. Beynon, Malcolm J., 2005. "A novel technique of object ranking and classification under ignorance: An application to the corporate failure risk problem," European Journal of Operational Research, Elsevier, vol. 167(2), pages 493-517, December.
    8. Philippe Jardin & David Veganzones & Eric Séverin, 2019. "Forecasting Corporate Bankruptcy Using Accrual-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 7-43, June.
    9. Wee-Kheng Tan & Wei-Cheng Liu & Yu-Ning Hu, 2013. "Finding the crucial factors for sustainable development of rural-based tourist destinations: using Nanzhuang, Taiwan as a case study," Service Business, Springer;Pan-Pacific Business Association, vol. 7(4), pages 623-640, December.
    10. Su, Chao-Ton & Hsu, Jyh-Hwa, 2006. "Precision parameter in the variable precision rough sets model: an application," Omega, Elsevier, vol. 34(2), pages 149-157, April.
    11. Beynon, Malcolm J. & Peel, Michael J. & Tang, Yu-Cheng, 2004. "The application of fuzzy decision tree analysis in an exposition of the antecedents of audit fees," Omega, Elsevier, vol. 32(3), pages 231-244, June.
    12. I Y-F Huang & W-W Wu & Y-T Lee, 2008. "Simplifying essential competencies for Taiwan civil servants using the rough set approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(2), pages 259-265, February.
    13. Maiya Anokhina & Henry Penikas & Victor Petrov, 2014. "Identifying SIFI Determinants for Global Banks and Insurance Companies: Implications for D-SIFIs in Russia," DEM Working Papers Series 085, University of Pavia, Department of Economics and Management.
    14. Shyng, Jhieh-Yu & Shieh, How-Ming & Tzeng, Gwo-Hshiung & Hsieh, Shu-Huei, 2010. "Using FSBT technique with Rough Set Theory for personal investment portfolio analysis," European Journal of Operational Research, Elsevier, vol. 201(2), pages 601-607, March.
    15. Grigor Sariyski, 2008. "Evaluation of the Financial Reliability of the Firm," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 26-48.
    16. Vadlamani Ravi & Vadlamani Madhav, 2020. "Optimizing the reliability of a bank with Logistic Regression and Particle Swarm Optimization," Papers 2004.11122,


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