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Early warning models against bankruptcy risk for Central European and Latin American enterprises


  • Korol, Tomasz


This article is devoted to the issue of forecasting the bankruptcy risk of enterprises in Latin America and Central Europe. The author has used statistical and soft computing methods to program the prediction models. It compares the effectiveness of twelve different early warning models for forecasting the bankruptcy risk of companies. In the research conducted, the author used data on 185 companies listed on the Warsaw Stock Exchange and 60 companies listed on Stock Exchange markets in Mexico, Argentina, Peru, Brazil and Chile. This population of firms was divided into learning and testing setdata. Each company was analyzed using the absolute values of 14 financial ratios and the dynamics of change of these ratios.

Suggested Citation

  • Korol, Tomasz, 2013. "Early warning models against bankruptcy risk for Central European and Latin American enterprises," Economic Modelling, Elsevier, vol. 31(C), pages 22-30.
  • Handle: RePEc:eee:ecmode:v:31:y:2013:i:c:p:22-30
    DOI: 10.1016/j.econmod.2012.11.017

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    References listed on IDEAS

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    5. Edward I Altman & Tara K N Baidya & Luis Manoel Ribeiro Dias, 1979. "Assessing Potential Financial Problems for Firms in Brazil," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 10(2), pages 9-24, June.
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    7. Arindam Bandyopadhyay, 2006. "Predicting probability of default of Indian corporate bonds: logistic and Z-score model approaches," Journal of Risk Finance, Emerald Group Publishing, vol. 7(3), pages 255-272, May.
    8. Emel, Ahmet Burak & Oral, Muhittin & Reisman, Arnold & Yolalan, Reha, 2003. "A credit scoring approach for the commercial banking sector," Socio-Economic Planning Sciences, Elsevier, vol. 37(2), pages 103-123, June.
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    Cited by:

    1. Muñoz-Izquierdo, Nora & Segovia-Vargas, María Jesús & Camacho-Miñano, María-del-Mar & Pascual-Ezama, David, 2019. "Explaining the causes of business failure using audit report disclosures," Journal of Business Research, Elsevier, vol. 98(C), pages 403-414.
    2. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    3. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
    4. Li, Hui & Hong, Lu-Yao & He, Jia-Xun & Xu, Xuan-Guo & Sun, Jie, 2013. "Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment," Economic Modelling, Elsevier, vol. 33(C), pages 747-761.
    5. Petra Marešová & Lukáš Peter & Jan Honegr & Lukáš Režný & Marek Penhaker & Martin Augustýnek & Hana Mohelská & Blanka Klímová & Kamil Kuča, 2020. "Complexity Stage Model of the Medical Device Development Based on Economic Evaluation—MedDee," Sustainability, MDPI, Open Access Journal, vol. 12(5), pages 1-27, February.
    6. Spyridou, Anastasia, 2019. "Evaluating Factors of Small and Medium Hospitality Enterprises Business Failure: a conceptual approach," MPRA Paper 93997, University Library of Munich, Germany.
    7. Santosh Kumar Shrivastav & P. Janaki Ramudu, 2020. "Bankruptcy Prediction and Stress Quantification Using Support Vector Machine: Evidence from Indian Banks," Risks, MDPI, Open Access Journal, vol. 8(2), pages 1-22, May.
    8. Fatima Zahra Azayite & Said Achchab, 2019. "A hybrid neural network model based on improved PSO and SA for bankruptcy prediction," Papers 1907.12179,
    9. Támara Ayús, Armando Lenin, 2018. "Modelación del riesgo de insolvencia en empresas del sector salud empleando modelos logit || Modeling of Insolvency Risk in Health Sector Companies Using Logit Models," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 128-145, Diciembre.
    10. Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2019. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(2), pages 1-17, April.

    More about this item


    Bankruptcy prediction; Early warning model; Financial crisis; Artificial intelligence;

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications


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