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Nexus between Twitter-based sentiment and tourism sector performance amid COVID-19 pandemic

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  • K Shiljas
  • Dilip Kumar
  • Hajam Abid Bashir

Abstract

The outbreak of the COVID-19 pandemic and the steps taken to contain its spread resulted in a decline in tourism sector stock prices. Using linear and quantile regressions, we examine the impact of Twitter-based investor sentiment for COVID-19 and Twitter-based sentiment towards uncertainty on the performance of tourism stocks. The findings indicate a heterogenous effect of tweets and Twitter economic uncertainty on tourism sector equity returns with a major impact on the lower quantiles.

Suggested Citation

  • K Shiljas & Dilip Kumar & Hajam Abid Bashir, 2023. "Nexus between Twitter-based sentiment and tourism sector performance amid COVID-19 pandemic," Tourism Economics, , vol. 29(8), pages 2200-2205, December.
  • Handle: RePEc:sae:toueco:v:29:y:2023:i:8:p:2200-2205
    DOI: 10.1177/13548166221123102
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    References listed on IDEAS

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    1. Darolles, Serge & Le Fol, Gaëlle & Mero, Gulten, 2017. "Mixture of distribution hypothesis: Analyzing daily liquidity frictions and information flows," Journal of Econometrics, Elsevier, vol. 201(2), pages 367-383.
    2. Ding, Wenzhi & Levine, Ross & Lin, Chen & Xie, Wensi, 2021. "Corporate immunity to the COVID-19 pandemic," Journal of Financial Economics, Elsevier, vol. 141(2), pages 802-830.
    3. Warwick McKibbin & Roshen Fernando, 2021. "The Global Macroeconomic Impacts of COVID-19: Seven Scenarios," Asian Economic Papers, MIT Press, vol. 20(2), pages 1-30, Summer.
    4. Smales, L.A., 2021. "Investor attention and global market returns during the COVID-19 crisis," International Review of Financial Analysis, Elsevier, vol. 73(C).
    5. Nofer, Michael & Hinz, Oliver, 2015. "Using Twitter to Predict the Stock Market: Where is the Mood Effect?," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77140, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    6. HaiYue Liu & Yile Wang & Dongmei He & Cangyu Wang, 2020. "Short term response of Chinese stock markets to the outbreak of COVID-19," Applied Economics, Taylor & Francis Journals, vol. 52(53), pages 5859-5872, November.
    7. Michael Nofer & Oliver Hinz, 2015. "Using Twitter to Predict the Stock Market," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 57(4), pages 229-242, August.
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