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Artificial Intelligence and Firm Performance: Does Machine Intelligence Shield Firms from Risks?

Author

Listed:
  • Linh Tu Ho

    (Department of Financial and Business Systems, Faculty of Agribusiness & Commerce, Lincoln University, Christchurch 7647, New Zealand)

  • Christopher Gan

    (Department of Financial and Business Systems, Faculty of Agribusiness & Commerce, Lincoln University, Christchurch 7647, New Zealand)

  • Shan Jin

    (Department of Financial and Business Systems, Faculty of Agribusiness & Commerce, Lincoln University, Christchurch 7647, New Zealand)

  • Bryan Le

    (Department of Global Value Chains and Trade, Faculty of Agribusiness & Commerce, Lincoln University, Christchurch 7647, New Zealand)

Abstract

We estimate and compare the impact of the coronavirus pandemic (COVID-19) on the performance of Artificial Intelligence (AI) and conventional listed firms using stock market indices. The single-group and multiple-group Interrupted Time-Series Analyses (ITSA) with panel data were used with four interventions: when the news of COVID-19 spread and the pandemic entered the first, second, third, and fourth months (24 February 2020, 23 March 2020, 20 April 2020, and 18 May 2020, respectively). The results show that the negative impact of COVID-19 on the AI stock market was less severe than on the conventional stock market in the first month of the pandemic. The performance of the AI stock market recovered quicker than the conventional stock market when the pandemic went into its third month. The results suggest that the AI stocks were more resilient than conventional stocks when the financial market was exposed to uncertainty caused by the COVID-19 pandemic. The deployment of AI in firms serves as a resilient, crucial driver for sustainable performance in challenging environments. Observing the performance of AI-adopted firms is an interesting direction for technical and fundamental analysts. Investors and portfolio managers should consider an AI market index to minimize risk or invest in stocks of AI-adopted listed firms to maximize excess returns.

Suggested Citation

  • Linh Tu Ho & Christopher Gan & Shan Jin & Bryan Le, 2022. "Artificial Intelligence and Firm Performance: Does Machine Intelligence Shield Firms from Risks?," JRFM, MDPI, vol. 15(7), pages 1-20, July.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:7:p:302-:d:859590
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    References listed on IDEAS

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    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. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation, and Work," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 197-236, National Bureau of Economic Research, Inc.
    3. Cumby, Robert E & Huizinga, John, 1992. "Testing the Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressions," Econometrica, Econometric Society, vol. 60(1), pages 185-195, January.
    4. Erik Brynjolfsson & Kristina McElheran, 2016. "The Rapid Adoption of Data-Driven Decision-Making," American Economic Review, American Economic Association, vol. 106(5), pages 133-139, May.
    5. Philippe Aghion & Benjamin F. Jones & Charles I. Jones, 2018. "Artificial Intelligence and Economic Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 237-282, National Bureau of Economic Research, Inc.
    6. Ali, Mohsin & Alam, Nafis & Rizvi, Syed Aun R., 2020. "Coronavirus (COVID-19) — An epidemic or pandemic for financial markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    7. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    8. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Kyle J. Kost & Marco C. Sammon & Tasaneeya Viratyosin, 2020. "The Unprecedented Stock Market Impact of COVID-19," NBER Working Papers 26945, National Bureau of Economic Research, Inc.
    9. Baek, Seungho & Mohanty, Sunil K. & Glambosky, Mina, 2020. "COVID-19 and stock market volatility: An industry level analysis," Finance Research Letters, Elsevier, vol. 37(C).
    10. Hansen, Stephen & Davis, Steven & Seminario-Amez, Cristhian, 2020. "Firm-level Risk Exposures and Stock Returns in the Wake of COVID-19," CEPR Discussion Papers 15314, C.E.P.R. Discussion Papers.
    11. Robert Seamans & Manav Raj, 2018. "AI, Labor, Productivity and the Need for Firm-Level Data," NBER Working Papers 24239, National Bureau of Economic Research, Inc.
    12. Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2019. "Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 31-50, Spring.
    13. Kinkel, Steffen & Baumgartner, Marco & Cherubini, Enrica, 2022. "Prerequisites for the adoption of AI technologies in manufacturing – Evidence from a worldwide sample of manufacturing companies," Technovation, Elsevier, vol. 110(C).
    14. Hao Xiong & Zuofeng Wu & Fei Hou & Jun Zhang, 2020. "Which Firm-specific Characteristics Affect the Market Reaction of Chinese Listed Companies to the COVID-19 Pandemic?," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(10), pages 2231-2242, August.
    15. Ariel Linden, 2015. "Conducting interrupted time-series analysis for single- and multiple-group comparisons," Stata Journal, StataCorp LP, vol. 15(2), pages 480-500, June.
    16. Nunzio Casalino & Tommaso Saso & Barbara Borin & Enrica Massella & Flavia Lancioni, 2020. "Digital Competences for Civil Servants and Digital Ecosystems for More Effective Working Processes in Public Organizations," Lecture Notes in Information Systems and Organization, in: Rocco Agrifoglio & Rita Lamboglia & Daniela Mancini & Francesca Ricciardi (ed.), Digital Business Transformation, pages 315-326, Springer.
    17. Huayu Shen & Mengyao Fu & Hongyu Pan & Zhongfu Yu & Yongquan Chen, 2020. "The Impact of the COVID-19 Pandemic on Firm Performance," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(10), pages 2213-2230, August.
    18. Agrawal, Ajay & Gans, Joshua & Goldfarb, Avi (ed.), 2019. "The Economics of Artificial Intelligence," National Bureau of Economic Research Books, University of Chicago Press, number 9780226613338, December.
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