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

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  • Korol, Tomasz

Abstract

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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    2. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    3. Tomasz Korol, 2020. "Assessment of Trajectories of Non-bankrupt and Bankrupt Enterprises," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1113-1135.
    4. Chien-Min Kang & Ming-Chieh Wang & Lin Lin, 2022. "Financial Distress Prediction of Cooperative Financial Institutions—Evidence for Taiwan Credit Unions," IJFS, MDPI, vol. 10(2), pages 1-25, April.
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    6. Mirza, Nawazish & Rahat, Birjees & Naqvi, Bushra & Rizvi, Syed Kumail Abbas, 2023. "Impact of Covid-19 on corporate solvency and possible policy responses in the EU," The Quarterly Review of Economics and Finance, Elsevier, vol. 87(C), pages 181-190.
    7. 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.
    8. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
    9. Chao Liu & Fengfeng Gao & Mengwan Zhang & Yuanrui Li & Cun Qian, 2024. "Reference Vector-Based Multiobjective Clustering Ensemble Approach for Time Series Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 181-210, July.
    10. Duan, Yunlong & Mu, Chang & Yang, Meng & Deng, Zhiqing & Chin, Tachia & Zhou, Li & Fang, Qifeng, 2021. "Study on early warnings of strategic risk during the process of firms’ sustainable innovation based on an optimized genetic BP neural networks model: Evidence from Chinese manufacturing firms," International Journal of Production Economics, Elsevier, vol. 242(C).
    11. 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, vol. 12(5), pages 1-27, February.
    12. David Alaminos & Agustín del Castillo & Manuel Ángel Fernández, 2016. "A Global Model for Bankruptcy Prediction," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-18, November.
    13. Spyridou, Anastasia, 2019. "Evaluating Factors of Small and Medium Hospitality Enterprises Business Failure: a conceptual approach," MPRA Paper 93997, University Library of Munich, Germany.
    14. Santosh Kumar Shrivastav & P. Janaki Ramudu, 2020. "Bankruptcy Prediction and Stress Quantification Using Support Vector Machine: Evidence from Indian Banks," Risks, MDPI, vol. 8(2), pages 1-22, May.
    15. Ednawati Rainarli & Aurelius Aaron, 2015. "The Implementation of Fuzzy Logic to Predict the Bankruptcy of Company in Indonesia," International Journal of Business and Administrative Studies, Professor Dr. Bahaudin G. Mujtaba, vol. 1(4), pages 147-154.
    16. Fatima Zahra Azayite & Said Achchab, 2019. "A hybrid neural network model based on improved PSO and SA for bankruptcy prediction," Papers 1907.12179, arXiv.org.
    17. Tamara Ayœs, Armando Lenin & Villegas, Gladis Cecilia & Leones Castro, María Cristina & Salazar Bocanegra, Juan Antonio, 2018. "Modelaci—n 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.
    18. Yali Cao & Yue Shao & Hongxia Zhang, 2022. "Study on early warning of E-commerce enterprise financial risk based on deep learning algorithm," Electronic Commerce Research, Springer, vol. 22(1), pages 21-36, March.
    19. Safaa Mrani & Loulid Adil, 2023. "Corporate failure : A literature review [La défaillance des entreprises : Une revue de littérature]," Post-Print hal-04219260, HAL.
    20. Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2019. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?," JRFM, MDPI, vol. 12(2), pages 1-17, April.

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    More about this item

    Keywords

    Bankruptcy prediction; Early warning model; Financial crisis; Artificial intelligence;
    All these keywords.

    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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