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COVID-19 news and the US equity market interactions: An inspection through econometric and machine learning lens

Author

Listed:
  • Rabin K. Jana

    (Indian Institute of Management Raipur)

  • Indranil Ghosh

    (Institute of Management Technology Hyderabad)

  • Fredj Jawadi

    (Univ. Lille, ULR 4999 - LUMEN)

  • Gazi Salah Uddin

    (Linköping University)

  • Ricardo M. Sousa

    (University of Minho
    LSE Alumni Association)

Abstract

This study investigates the impact of COVID-19 on the US equity market during the first wave of Coronavirus using a wide range of econometric and machine learning approaches. To this end, we use both daily data related to the US equity market sectors and data about the COVID-19 news over January 1, 2020-March 20, 2020. Accordingly, we show that at an early stage of the outbreak, global COVID-19s fears have impacted the US equity market even differently across sectors. Further, we also find that, as the pandemic gradually intensified its footprint in the US, local fears manifested by daily infections emerged more powerfully compared to its global counterpart in impairing the short-term dynamics of US equity markets.

Suggested Citation

  • Rabin K. Jana & Indranil Ghosh & Fredj Jawadi & Gazi Salah Uddin & Ricardo M. Sousa, 2025. "COVID-19 news and the US equity market interactions: An inspection through econometric and machine learning lens," Annals of Operations Research, Springer, vol. 345(2), pages 575-596, February.
  • Handle: RePEc:spr:annopr:v:345:y:2025:i:2:d:10.1007_s10479-022-04744-x
    DOI: 10.1007/s10479-022-04744-x
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    References listed on IDEAS

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    1. Mensi, Walid & Reboredo, Juan C. & Ugolini, Andrea, 2021. "Price-switching spillovers between gold, oil, and stock markets: Evidence from the USA and China during the COVID-19 pandemic," Resources Policy, Elsevier, vol. 73(C).
    2. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
    3. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    4. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    5. Abuzayed, Bana & Bouri, Elie & Al-Fayoumi, Nedal & Jalkh, Naji, 2021. "Systemic risk spillover across global and country stock markets during the COVID-19 pandemic," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 180-197.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. O’Donnell, Niall & Shannon, Darren & Sheehan, Barry, 2021. "Immune or at-risk? Stock markets and the significance of the COVID-19 pandemic," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    8. Jana, Rabin K. & Ghosh, Indranil & Das, Debojyoti & Dutta, Anupam, 2021. "Determinants of electronic waste generation in Bitcoin network: Evidence from the machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    9. Zebende, G.F., 2011. "DCCA cross-correlation coefficient: Quantifying level of cross-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 614-618.
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    More about this item

    Keywords

    COVID-19; The US equity market; Co-integration; Detrended cross-correlation analysis; Machine learning;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G1 - Financial Economics - - General Financial Markets
    • I1 - Health, Education, and Welfare - - Health

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