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Impact of COVID-19 on Mergers, Acquisitions & Corporate Restructurings

Author

Listed:
  • Chokri Kooli

    (College of Commerce and Business, Lusail University, Zone 69, St 100, Building 333, Lusail, Doha 122104, Qatar)

  • Melanie Lock Son

    (University of Ottawa, Ottawa, ON K1N 6N5, Canada)

Abstract

Most economic downturns have stemmed from inefficiencies in the economic system. This research paper aims at investigating the impact of the COVID-19 pandemic—an exogeneous health crisis—on global mergers and acquisition (M&A) activity. By gathering statistical data about global transaction volume, value, and type, the study aims at getting a pulse of how mergers, acquisitions, and other restructuring activities have been utilized to support corporate objectives amidst these unprecedented times. While the full-fledged impact of COVID-19 cannot be fully captured at the moment (early 2021), the study attempts to illustrate how this change to economic stability caused a Schumpeterian creative destruction of industries. As firms prepare for the growth that will follow this downturn, M&A will enable companies to look into a future infused with technology and structurally different business models. This research paper thus captures the deliberate transformation occurring in the deal world to discuss the possible outlook of the M&A deal market in the post-pandemic world.

Suggested Citation

  • Chokri Kooli & Melanie Lock Son, 2021. "Impact of COVID-19 on Mergers, Acquisitions & Corporate Restructurings," Businesses, MDPI, vol. 1(2), pages 1-13, August.
    • Handle: RePEc:gam:jbusin:v:1:y:2021:i:2:p:8-114:d:615049
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    References listed on IDEAS

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    Cited by:

    1. Tian, Haowen & Wang, Junkai, 2024. "Navigating the storm: How the COVID-19 pandemic transformed the M&A landscape," Finance Research Letters, Elsevier, vol. 59(C).
    2. Sylwia Lorenc & Tomasz Leśniak & Arkadiusz Kustra & Maria Sierpińska, 2023. "Evolution of Business Models of Mining and Energy Sector Companies according to Current Market Trends," Energies, MDPI, vol. 16(13), pages 1-21, July.
    3. Giambattista Albora & Matteo Straccamore & Andrea Zaccaria, 2024. "Machine learning-based similarity measure to forecast M&A from patent data," Papers 2404.07179, arXiv.org.

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