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A Multicriteria Discrimination Method for the Prediction of Financial Distress: The Case of Greece

Author

Listed:
  • Michael Doumpos

    (Technical University of Crete, Greece)

  • Constantin Zopounidis

    (Technical University of Crete, Greece)

Abstract
Financial distress prediction is an essential issue in finance. Especially in emerging economies, predicting the future financial situation of individual corporate entities is even more significant, bearing in mind the general economic turmoil that can be caused by business failures. The research on developing quantitative financial distress prediction models has been focused on building discriminant models distinguishing healthy firms from financially distressed ones. Following this discrimination approach, this paper explores the applicability of a new non–parametric multicriteria decision aid discrimination method, called M.H.DIS, to predict financial distress using data concerning the case of Greece. A comparison with discriminant and logit analysis is performed using both a basic and a holdout sample. The results show that M.H.DIS can be considered as a new alternative tool for financial distress prediction. Its performance is superior to discriminant analysis and comparable to logit analysis.

Suggested Citation

  • Michael Doumpos & Constantin Zopounidis, 1999. "A Multicriteria Discrimination Method for the Prediction of Financial Distress: The Case of Greece," Multinational Finance Journal, Multinational Finance Journal, vol. 3(2), pages 71-101, June.
    • Handle: RePEc:mfj:journl:v:3:y:1999:i:2:p:71-101
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    References listed on IDEAS

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    Cited by:

    1. Yi Jiang & Stewart Jones, 2018. "Corporate distress prediction in China: a machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(4), pages 1063-1109, December.
    2. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2022. "Bankruptcy prediction for private firms in developing economies: a scoping review and guidance for future research," Management Review Quarterly, Springer, vol. 72(4), pages 927-966, December.
    3. du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
    4. Balcaen S. & Ooghe H., 2004. "Alternative methodologies in studies on business failure: do they produce better results than the classic statistical methods?," Vlerick Leuven Gent Management School Working Paper Series 2004-16, Vlerick Leuven Gent Management School.
    5. P. Du Jardin & E. Séverin, 2011. "Predicting Corporate Bankruptcy Using Self-Organising map: An empirical study to Improve the Forecasting horizon of financial failure model," Post-Print hal-00801878, HAL.
    6. Muqaddas Khalid & Qaisar Abbas & Fizzah Malik & Shahid Ali, 2020. "Impact of audit committee attributes on financial distress: Evidence from Pakistan," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 7(01), pages 1-19, March.
    7. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
    8. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    9. Ramon Oehninger & Michael J. Kendzia & Felix Scherrer, 2020. "Preventing Corporate Turnarounds through an Early Warning System," International Journal of Management, Knowledge and Learning, International School for Social and Business Studies, Celje, Slovenia, vol. 9(2), pages 185-205.
    10. Burcu Dikmen & Güray Küçükkocaoğlu, 2010. "The detection of earnings manipulation: the three-phase cutting plane algorithm using mathematical programming," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(5), pages 442-466.
    11. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
    12. Spathis, Charalambos & Doumpos, Michael & Zopounidis, Constantin, 2003. "Using client performance measures to identify pre-engagement factors associated with qualified audit reports in Greece," The International Journal of Accounting, Elsevier, vol. 38(3), pages 267-284.
    13. Selcuk Caner & Mehmet Baha Karan, 2012. "Screening Creditworthiness of SME's: The Case of Small Business Assistance in Turkey," Multinational Finance Journal, Multinational Finance Journal, vol. 16(1-2), pages 1-20, March - J.
    14. Silvia Angilella & Maria Rosaria Pappalardo, 2021. "Assessment of a failure prediction model in the energy sector: a multicriteria discrimination approach with Promethee based classification," Papers 2102.07656, arXiv.org.
    15. Yang Liu & Qingguo Zeng & Bobo Li & Lili Ma & Joaquín Ordieres‐Meré, 2022. "Anticipating financial distress of high‐tech startups in the European Union: A machine learning approach for imbalanced samples," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1131-1155, September.

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    More about this item

    Keywords

    discrimination; financial distress; mathematical programming; multi-criteria decision aid;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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