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Public Concern and the Financial Markets during the COVID-19 outbreak

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  • Michele Costola
  • Matteo Iacopini
  • Carlo R. M. A. Santagiustina

Abstract

We measure the public concern during the outbreak of COVID-19 disease using three data sources from Google Trends (YouTube, Google News, and Google Search). Our findings are three-fold. First, the public concern in Italy is found to be a driver of the concerns in other countries. Second, we document that Google Trends data for Italy better explains the stock index returns of France, Germany, Great Britain, the United States, and Spain with respect to their country-based indicators. Finally, we perform a time-varying analysis and identify that the most severe impacts in the financial markets occur at each step of the Italian lock-down process.

Suggested Citation

  • Michele Costola & Matteo Iacopini & Carlo R. M. A. Santagiustina, 2020. "Public Concern and the Financial Markets during the COVID-19 outbreak," Papers 2005.06796, arXiv.org.
    • Handle: RePEc:arx:papers:2005.06796
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    References listed on IDEAS

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    1. Castelnuovo, Efrem & Tran, Trung Duc, 2017. "Google It Up! A Google Trends-based Uncertainty index for the United States and Australia," Economics Letters, Elsevier, vol. 161(C), pages 149-153.
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    4. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    5. Yu, Lean & Zhao, Yaqing & Tang, Ling & Yang, Zebin, 2019. "Online big data-driven oil consumption forecasting with Google trends," International Journal of Forecasting, Elsevier, vol. 35(1), pages 213-223.
    6. Bijl, Laurens & Kringhaug, Glenn & Molnár, Peter & Sandvik, Eirik, 2016. "Google searches and stock returns," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 150-156.
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    Cited by:

    1. Fu Qiao & Yan Yan, 2020. "How does stock market reflect the change in economic demand? A study on the industry-specific volatility spillover networks of China's stock market during the outbreak of COVID-19," Papers 2007.07487, arXiv.org.
    2. Costola, Michele & Hinz, Oliver & Nofer, Michael & Pelizzon, Loriana, 2023. "Machine learning sentiment analysis, COVID-19 news and stock market reactions," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2023. "Which COVID-19 information really impacts stock markets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    4. Szczygielski, Jan Jakub & Brzeszczyński, Janusz & Charteris, Ailie & Bwanya, Princess Rutendo, 2022. "The COVID-19 storm and the energy sector: The impact and role of uncertainty," Energy Economics, Elsevier, vol. 109(C).
    5. Szczygielski, Jan Jakub & Bwanya, Princess Rutendo & Charteris, Ailie & Brzeszczyński, Janusz, 2021. "The only certainty is uncertainty: An analysis of the impact of COVID-19 uncertainty on regional stock markets," Finance Research Letters, Elsevier, vol. 43(C).
    6. Osman Taylan & Abdulaziz S. Alkabaa & Mustafa Tahsin Yılmaz, 2022. "Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-30, December.
    7. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2022. "The impact and role of COVID-19 uncertainty: A global industry analysis," International Review of Financial Analysis, Elsevier, vol. 80(C).
    8. Jiang, Jie & Hou, Jack & Wang, Cangyu & Liu, HaiYue, 2021. "COVID-19 impact on firm investment—Evidence from Chinese publicly listed firms," Journal of Asian Economics, Elsevier, vol. 75(C).

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