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The CoRisk-Index: a data-mining approach to identify industry-specific risk perceptions related to Covid-19

Author

Listed:
  • Fabian Stephany

    (Oxford Internet Institute, University of Oxford
    Humboldt Institute for Internet and Society Berlin
    Datenwissenschaftliche Gesellschaft Berlin)

  • Leonie Neuhäuser

    (RWTH Aachen University)

  • Niklas Stoehr

    (ETH Zürich)

  • Philipp Darius

    (Hertie School Berlin)

  • Ole Teutloff

    (Datenwissenschaftliche Gesellschaft Berlin
    Copenhagen Center for Social Data Science)

  • Fabian Braesemann

    (Oxford Internet Institute, University of Oxford
    Humboldt Institute for Internet and Society Berlin
    Datenwissenschaftliche Gesellschaft Berlin
    Saïd Business School, University of Oxford)

Abstract

The global spread of Covid-19 has caused major economic disruptions. Governments around the world provide considerable financial support to mitigate the economic downturn. However, effective policy responses require reliable data on the economic consequences of the corona pandemic. We propose the CoRisk-Index: a real-time economic indicator of corporate risk perceptions related to Covid-19. Using data mining, we analyse all reports from US companies filed since January 2020, representing more than a third of the US workforce. We construct two measures—the number of ‘corona’ words in each report and the average text negativity of the sentences mentioning corona in each industry—that are aggregated in the CoRisk-Index. The index correlates with U.S. unemployment rates across industries and with an established market volatility measure, and it preempts stock market losses of February 2020. Moreover, thanks to topic modelling and natural language processing techniques, the CoRisk data provides highly granular data on different dimensions of the crisis and the concerns of individual industries. The index presented here helps researchers and decision makers to measure risk perceptions of industries with regard to Covid-19, bridging the quantification gap between highly volatile stock market dynamics and long-term macroeconomic figures. For immediate access to the data, we provide all findings and raw data on an interactive online dashboard.

Suggested Citation

  • Fabian Stephany & Leonie Neuhäuser & Niklas Stoehr & Philipp Darius & Ole Teutloff & Fabian Braesemann, 2022. "The CoRisk-Index: a data-mining approach to identify industry-specific risk perceptions related to Covid-19," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-15, December.
    • Handle: RePEc:pal:palcom:v:9:y:2022:i:1:d:10.1057_s41599-022-01039-1
    DOI: 10.1057/s41599-022-01039-1
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