IDEAS home Printed from https://ideas.repec.org/a/igg/jban00/v9y2022i1p1-19.html
   My bibliography  Save this article

Stock Market Responses to the COVID-19 Health Crisis: Evidence From the World's Largest Economies

Author

Listed:
  • Abdul Wajid

    (Galgotias University, Greater Noida, India)

  • Kanishka Gupta

    (Symbiosis International University (Deemed), India)

Abstract

The outbreak of the novel COVID-19 pandemic emerged as a major black swan event which has caused shock waves and severely hurt the sentiments of market participants. The pandemic has raised uncertainties and risks all over the world, impacting substantially the world's 20 largest economies. While the stock markets' intense reaction to the official news of the pandemic is well known, the reaction of largest world economies during the initial phases of the outbreak until 11th March 2020 is not very well established. Therefore, the present study investigates how stock markets in world's 20 largest economies have reacted to major events and press releases associated with disease from the beginning of the pandemic (i.e., 31st December 2020 till 11th March 2020). The results of the study suggest that the declaration of the novel COVID-19 as a pandemic was the most devastating event for stock markets. This was confirmed by using various parametric and non-parametric tests. In addition, the last event was further analyzed by observing CARs of various indices individually.

Suggested Citation

  • Abdul Wajid & Kanishka Gupta, 2022. "Stock Market Responses to the COVID-19 Health Crisis: Evidence From the World's Largest Economies," International Journal of Business Analytics (IJBAN), IGI Global, vol. 9(1), pages 1-19, January.
  • Handle: RePEc:igg:jban00:v:9:y:2022:i:1:p:1-19
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJBAN.303114
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
    2. Vidgen, Richard & Shaw, Sarah & Grant, David B., 2017. "Management challenges in creating value from business analytics," European Journal of Operational Research, Elsevier, vol. 261(2), pages 626-639.
    3. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    4. Desirée Knoppen & Melek Akın Ateş & Alistair Brandon-Jones & Davide Luzzini & Erik van Raaij & Finn Wynstra, 2015. "A comprehensive assessment of measurement equivalence in operations management," International Journal of Production Research, Taylor & Francis Journals, vol. 53(1), pages 166-182, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christoph Keding, 2021. "Understanding the interplay of artificial intelligence and strategic management: four decades of research in review," Management Review Quarterly, Springer, vol. 71(1), pages 91-134, February.
    2. Martin Eling & Davide Nuessle & Julian Staubli, 2022. "The impact of artificial intelligence along the insurance value chain and on the insurability of risks," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(2), pages 205-241, April.
    3. Morimura, Fumikazu & Sakagawa, Yuji, 2023. "The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    4. Vinicius Luiz Ferraz Minatogawa & Matheus Munhoz Vieira Franco & Izabela Simon Rampasso & Rosley Anholon & Ruy Quadros & Orlando Durán & Antonio Batocchio, 2019. "Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations," Sustainability, MDPI, vol. 12(1), pages 1-29, December.
    5. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    6. Andrea De Mauro & Marco Greco & Michele Grimaldi, 2019. "Understanding Big Data Through a Systematic Literature Review: The ITMI Model," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1433-1461, July.
    7. Pan, Qiaohong & Luo, Wenping & Fu, Yi, 2022. "A csQCA study of value creation in logistics collaboration by big data: A perspective from companies in China," Technology in Society, Elsevier, vol. 71(C).
    8. Kim, Jaemin & Dibrell, Clay & Kraft, Ellen & Marshall, David, 2021. "Data analytics and performance: The moderating role of intuition-based HR management in major league baseball," Journal of Business Research, Elsevier, vol. 122(C), pages 204-216.
    9. Božič, Katerina & Dimovski, Vlado, 2019. "Business intelligence and analytics for value creation: The role of absorptive capacity," International Journal of Information Management, Elsevier, vol. 46(C), pages 93-103.
    10. Purva Grover & Arpan Kumar Kar & Yogesh K. Dwivedi, 2022. "Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions," Annals of Operations Research, Springer, vol. 308(1), pages 177-213, January.
    11. Conboy, Kieran & Mikalef, Patrick & Dennehy, Denis & Krogstie, John, 2020. "Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda," European Journal of Operational Research, Elsevier, vol. 281(3), pages 656-672.
    12. Amit Kumar & Bala Krishnamoorthy, 2020. "Business Analytics Adoption in Firms: A Qualitative Study Elaborating TOE Framework in India," International Journal of Global Business and Competitiveness, Springer, vol. 15(2), pages 80-93, December.
    13. Reis, Carolina & Ruivo, Pedro & Oliveira, Tiago & Faroleiro, Paulo, 2020. "Assessing the drivers of machine learning business value," Journal of Business Research, Elsevier, vol. 117(C), pages 232-243.
    14. Philipp Korherr & Dominik Kanbach, 2023. "Human-related capabilities in big data analytics: a taxonomy of human factors with impact on firm performance," Review of Managerial Science, Springer, vol. 17(6), pages 1943-1970, August.
    15. Omar, Yamila M. & Minoufekr, Meysam & Plapper, Peter, 2019. "Business analytics in manufacturing: Current trends, challenges and pathway to market leadership," Operations Research Perspectives, Elsevier, vol. 6(C).
    16. Hossain, Md Afnan & Akter, Shahriar & Yanamandram, Venkata, 2020. "Revisiting customer analytics capability for data-driven retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    17. Francisco, Eduardo de Rezende & Kugler, José Luiz & Kang, Soong Moon & Silva, Ricardo & Whigham, Peter Alexander, 2019. "Além da tecnologia: Desafios gerenciais na era do Big Data," RAE - Revista de Administração de Empresas, FGV-EAESP Escola de Administração de Empresas de São Paulo (Brazil), vol. 59(6), December.
    18. Candice WALLS & Brian BARNARD, 2020. "Success Factors of Big Data to Achieve Organisational Performance: Theoretical Perspectives," Expert Journal of Business and Management, Sprint Investify, vol. 8(1), pages 1-16.
    19. Tabesh, Pooya & Mousavidin, Elham & Hasani, Sona, 2019. "Implementing big data strategies: A managerial perspective," Business Horizons, Elsevier, vol. 62(3), pages 347-358.
    20. Ludivine Ravat & Aurélie Hemonnet-Goujot & Sandrine Hollet-Haudebert, 2023. "Data-driven innovation capability of marketing: an exploratory study of its components and underlying processes," Post-Print hal-04151199, HAL.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jban00:v:9:y:2022:i:1:p:1-19. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.