Corisk Index

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The Corisk Index is the first[according to whom?] economic indicator of industry risk assessments related to COVID-19. In contrast to conventional economic climate indexes, e.g. the Ifo Business Climate Index or Purchasing Managers' Index, the CoRisk Index relies on automatically retrieved company filings.[1] The index has been developed by a team of researchers at the Oxford Internet Institute, University of Oxford, and the Hertie School of Governance in March 2020. It gained international media attention[2][3][4][5] as an up-to-date empirical source for policy makers and researchers[6][7][8][9][10][11] investigating the economic repercussions of the Coronavirus Recession.

Methodology

The index is calculated with the use of company 10-k risk reports filed to the U.S. Securities and Exchange Commission (SEC). The CoRisk Index is calculated industry-specific as a geometric mean of three measures: CoRiskIndex=k+n2,[1] where k refers to the average industry count of Corona-related keywords used in each report and n represents the average industry share of negative keywords in Corona-related sentences.

Criticism and limitations

The CoRisk Index is constructed using automated textual analysis of corporate 10-K risk disclosures filed with the U.S. Securities and Exchange Commission. As such, it measures industry-level risk perception as expressed in corporate reporting rather than realized economic outcomes or direct financial performance indicators.[12] Because the index relies on the frequency and sentiment of COVID-19-related language in company filings, its results may be influenced by disclosure practices, legal considerations, and reporting conventions, which can vary across firms and industries.

The methodology assumes that the intensity and tone of pandemic-related terminology correlate with underlying industry exposure. However, this proxy-based approach does not directly measure output, employment, revenues, or other macroeconomic variables, and therefore should be interpreted as an indicator of perceived risk rather than a forecast of economic performance.[13]

References

  1. ↑ 1.0 1.1 Stephany, Fabian; Neuhäuser, Leonie; Stoehr, Niklas; Darius, Philipp; Teutloff, Ole; Braesemann, Fabian (2022-02-02). "The CoRisk-Index: a data-mining approach to identify industry-specific risk perceptions related to Covid-19" (in en). Humanities and Social Sciences Communications 9 (1): 1–15. doi:10.1057/s41599-022-01039-1. ISSN 2662-9992. 
  2. ↑ "Analysis | Crisis begins to hit professional and public-sector jobs once considered safe" (in en-US). Washington Post. ISSN 0190-8286. https://www.washingtonpost.com/business/2020/04/30/jobless-claims-industry/. 
  3. ↑ Guldner, Jan (4 April 2020). "Rezession und Instabilität: So steht der Pegel der Corona-Angst" (in de). https://www.wiwo.de/erfolg/trends/rezession-und-instabilitaet-so-steht-der-pegel-der-corona-angst/25703386.html. 
  4. ↑ smartlighting (2020-05-06). "Nuevo índice online analiza preocupaciones comerciales ante COVID-19" (in es). https://smart-lighting.es/indice-online-analiza-preocupaciones-comerciales-covid-19/. 
  5. ↑ "New online index shows business concerns over COVID-19 | University of Oxford" (in en). 30 April 2020. https://www.ox.ac.uk/news/2020-04-30-new-online-index-shows-business-concerns-over-covid-19. 
  6. ↑ Latif, Siddique; Usman, Muhammad; Manzoor, Sanaullah; Iqbal, Waleed; Qadir, Junaid; Tyson, Gareth; Castro, Ignacio; Razi, Adeel et al. (2020). "Leveraging Data Science to Combat COVID-19: A Comprehensive Review". IEEE Transactions on Artificial Intelligence 1 (1): 85–103. doi:10.1109/TAI.2020.3020521. PMID 37982070. Bibcode: 2020ITAI....1...85L.  preprint
  7. ↑ Béland, Louis-Philippe; Brodeur, Abel; Wright, Taylor (2020-04-27). "The Short-Term Economic Consequences of Covid-19: Exposure to Disease, Remote Work and Government Response". SSRN 3584922.
  8. ↑ "DATA in the time of COVID-19" (in en). 2020-11-13. https://opendatawatch.com/whats-being-said-resource/data-in-the-time-of-covid-19/. 
  9. ↑ Brodeur, Abel; Clark, Andrew E.; Fleche, Sarah; Powdthavee, Nattavudh (2021-01-01). "COVID-19, lockdowns and well-being: Evidence from Google Trends" (in en). Journal of Public Economics 193. doi:10.1016/j.jpubeco.2020.104346. ISSN 0047-2727. PMID 33281237. PMC 7703221. https://halshs.archives-ouvertes.fr/halshs-03029872/file/dp1693.pdf. 
  10. ↑ Brodeur, Abel; Cook, Nikolai; Wright, Taylor (2021-03-01). "On the effects of COVID-19 safer-at-home policies on social distancing, car crashes and pollution" (in en). Journal of Environmental Economics and Management 106. doi:10.1016/j.jeem.2021.102427. ISSN 0095-0696. PMID 33583969. Bibcode: 2021JEEM..10602427B. 
  11. ↑ Davis, Steven J.; Hansen, Stephen; Seminario-Amez, Cristhian (September 2020). "Firm-Level Risk Exposures and Stock Returns in the Wake of COVID-19". NBER Working Paper Series (27867). doi:10.3386/w27867. https://www.nber.org/papers/w27867. 
  12. ↑ Stephany, Fabian; Neuhäuser, Leonie; Stoehr, Niklas; Darius, Philipp; Teutloff, Ole; Braesemann, Fabian (2022). "The CoRisk-Index: a data-mining approach to identify industry-specific risk perceptions related to Covid-19". Humanities and Social Sciences Communications 9. doi:10.1057/s41599-022-01039-1. https://www.nature.com/articles/s41599-022-01039-1. Retrieved 18 February 2026. 
  13. ↑ Stephany, Fabian; Stoehr, Niklas; Darius, Philipp; Neuhäuser, Leonie; Teutloff, Ole; Braesemann, Fabian (2020). "The CoRisk-Index: a data-mining approach to identify industry-specific risk assessments related to COVID-19 in real-time". arXiv:2003.12432 [econ.GN].




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