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Analyzing Industry‐Level Vulnerability By Predicting Financial Bankruptcy

Author

Listed:
  • Katsuyuki Tanaka
  • Takuo Higashide
  • Takuji Kinkyo
  • Shigeyuki Hamori

Abstract

This study introduces a novel framework for building company bankruptcy models and a methodology for assessing the vulnerability of industrial economic activities. We consider the identification of bankruptcy as a classification problem and assume that bankruptcy criteria differ across industries. We build highly accurate industry bankruptcy models by constructing separate models for each industry. We also propose a method of analyzing the vulnerability of industrial economic activities in various countries and industries using new indicators we call “expected potential loss,” which we obtain using the predicted likelihood of bankruptcy and company information. (JEL G0, C0)

Suggested Citation

  • Katsuyuki Tanaka & Takuo Higashide & Takuji Kinkyo & Shigeyuki Hamori, 2019. "Analyzing Industry‐Level Vulnerability By Predicting Financial Bankruptcy," Economic Inquiry, Western Economic Association International, vol. 57(4), pages 2017-2034, October.
    • Handle: RePEc:bla:ecinqu:v:57:y:2019:i:4:p:2017-2034
    DOI: 10.1111/ecin.12817
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    Cited by:

    1. Solomon Y. Deku & Alper Kara & Artur Semeyutin, 2021. "The predictive strength of MBS yield spreads during asset bubbles," Review of Quantitative Finance and Accounting, Springer, vol. 56(1), pages 111-142, January.
    2. Wenting Zhang & Shigeyuki Hamori, 2020. "Do Machine Learning Techniques and Dynamic Methods Help Forecast US Natural Gas Crises?," Energies, MDPI, vol. 13(9), pages 1-22, May.
    3. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    4. Yulian Zhang & Shigeyuki Hamori, 2020. "Forecasting Crude Oil Market Crashes Using Machine Learning Technologies," Energies, MDPI, vol. 13(10), pages 1-14, May.
    5. Takuo Higashide & Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2021. "New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?," JRFM, MDPI, vol. 14(5), pages 1-18, May.
    6. Susanna Levantesi & Gabriella Piscopo, 2020. "The Importance of Economic Variables on London Real Estate Market: A Random Forest Approach," Risks, MDPI, vol. 8(4), pages 1-17, October.
    7. Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.

    More about this item

    JEL classification:

    • G0 - Financial Economics - - General
    • C0 - Mathematical and Quantitative Methods - - General

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