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Prediction of financial distress for multinational corporations: Panel estimations across countries

Author

Listed:
  • Nicholas Apergis
  • Mita Bhattacharya
  • John Inekwe
Abstract
This research predicts ex-ante financial distress and analyses the link between financial distress, performance, employment, `and research and development (R&D) investment in the case of multinational companies (MNCs). The conditional logit and hazard models are employed to predict financial distress, while a conditional mixed process model is employed to obtain consistent and efficient estimates. Financial distress generates contractions in performance, employment, and R&D investment. Hedging against risk mitigates the effect of financial distress on R&D. Our findings vary across countries, for example, we find MNCs in Canada, Israel and the U.S. benefit from hedging against risk. The findings also indicate that ex-ante financial distress is detrimental to employment for Canada, the U.K., the Netherlands and the U.S. The findings indicate the MNCs play different roles across countries in contributing jobs, investment in R&D during the distress period.

Suggested Citation

  • Nicholas Apergis & Mita Bhattacharya & John Inekwe, 2019. "Prediction of financial distress for multinational corporations: Panel estimations across countries," Applied Economics, Taylor & Francis Journals, vol. 51(39), pages 4255-4269, August.
    • Handle: RePEc:taf:applec:v:51:y:2019:i:39:p:4255-4269
    DOI: 10.1080/00036846.2019.1589646
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    Cited by:

    1. Sefa Awaworyi Churchill & John Inekwe & Kris Ivanovski, 2023. "Has the COVID-19 pandemic converged across countries?," Empirical Economics, Springer, vol. 64(5), pages 2027-2052, May.
    2. Chih‐Chun Chen & Chun‐Da Chen & Donald Lien, 2020. "Financial distress prediction model: The effects of corporate governance indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1238-1252, December.
    3. Yinghua Song & Minzhe Jiang & Shixuan Li & Shengzhe Zhao, 2024. "Class‐imbalanced financial distress prediction with machine learning: Incorporating financial, management, textual, and social responsibility features into index system," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 593-614, April.

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