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


  • Michael Doumpos

    (Technical University of Crete, Greece)

  • Constantin Zopounidis

    (Technical University of Crete, Greece)


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. 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.
    3. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(4), pages 1-15, December.
    4. 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.
    5. 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.

    More about this item


    discrimination; financial distress; mathematical programming; multi-criteria decision aid;

    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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