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A Global Model for Bankruptcy Prediction

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  • David Alaminos
  • Agustín del Castillo
  • Manuel Ángel Fernández

Abstract

The recent world financial crisis has increased the number of bankruptcies in numerous countries and has resulted in a new area of research which responds to the need to predict this phenomenon, not only at the level of individual countries, but also at a global level, offering explanations of the common characteristics shared by the affected companies. Nevertheless, few studies focus on the prediction of bankruptcies globally. In order to compensate for this lack of empirical literature, this study has used a methodological framework of logistic regression to construct predictive bankruptcy models for Asia, Europe and America, and other global models for the whole world. The objective is to construct a global model with a high capacity for predicting bankruptcy in any region of the world. The results obtained have allowed us to confirm the superiority of the global model in comparison to regional models over periods of up to three years prior to bankruptcy.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0166693
    DOI: 10.1371/journal.pone.0166693
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    References listed on IDEAS

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    Cited by:

    1. Rafael Becerra-Vicario & David Alaminos & Eva Aranda & Manuel A. Fernández-Gámez, 2020. "Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry," Sustainability, MDPI, Open Access Journal, vol. 12(12), pages 1-15, June.
    2. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, Open Access Journal, vol. 12(18), pages 1-17, September.
    3. Bagheri, Oveis & Ranjbaran Jalili, Mona, 2020. "Accruals Quality and Bankruptcy in Shirata Model (Case Study: Tehran Stock Exchange)," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 15(4), pages 381-402, October.
    4. Svabova Lucia & Durica Marek & Podhorska Ivana, 2018. "Prediction of Default of Small Companies in the Slovak Republic," Economics and Culture, Sciendo, vol. 15(1), pages 88-95, June.
    5. Zura Kakushadze & Juan Andrés Serur, 2018. "151 Trading Strategies," Springer Books, Springer, number 978-3-030-02792-6, September.
    6. Adit Chopra & Abhi Bansal & Aryaman Wadhwa, 2020. "Evidence of Predicting Early Signs of Corporate Bankruptcy Using Financial Ratios in the Indian Landscape," Papers 2008.04782, arXiv.org.
    7. Misankova Maria & Zvarikova Katarina & Kliestikova Jana, 2017. "Bankruptcy Practice in Countries of Visegrad Four," Economics and Culture, Sciendo, vol. 14(1), pages 108-118, June.
    8. Jakub Horak & Tomas Krulicky & Zuzana Rowland & Veronika Machova, 2020. "Creating a Comprehensive Method for the Evaluation of a Company," Sustainability, MDPI, Open Access Journal, vol. 12(21), pages 1-23, November.
    9. Zazueta, Jorge & Heredia, Andrea Chavez & Zazueta-Hernández, Jorge, 2021. "Endogenous Prediction of Bankruptcy using a Support Vector Machine," SocArXiv ehpt7, Center for Open Science.

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