The CoRisk-Index: A Data-Mining Approach to Identify Industry-Specific Risk Assessments Related to COVID-19 in Real-Time

18 Pages Posted: 7 Jun 2020

See all articles by Fabian Stephany

Fabian Stephany

University of Oxford - Oxford Internet Institute; Bruegel; Alexander von Humboldt Institute for Internet and Society

Niklas Stoehr

University College London - Department of Computer Science

Philipp Darius

Hertie School - Centre for Digital Governance

Leonie Neuhäuser

Hertie School of Governance

Ole Teutloff

University of Copenhagen - Copenhagen Center for Social Data Science

Fabian Braesemann

University of Oxford - Oxford Internet Institute

Date Written: April 27, 2020

Abstract

While the coronavirus spreads, governments are attempting to reduce contagion rates at the expense of negative economic effects. Market expectations plummeted, foreshadowing the risk of a global economic crisis and mass unemployment. Governments provide huge financial aid programmes to mitigate the economic shocks. To achieve higher effectiveness with such policy measures, it is key to identify the industries that are most in need of support.

In this study, we introduce a data-mining approach to measure industry-specific risks related to COVID-19. We examine company risk reports filed to the U. S. Securities and Exchange Commission (SEC). This alternative data set can complement more traditional economic indicators in times of the fast-evolving crisis as it allows for a real-time analysis of risk assessments. Preliminary findings suggest that the companies’ awareness towards corona-related business risks is ahead of the overall stock market developments. Our approach allows to distinguish the industries by their risk awareness towards COVID-19. Based on natural language processing, we identify corona-related risk topics and their perceived relevance for different industries.

The preliminary findings are summarised as an up-to-date online index. The CoRisk-Index tracks the industry-specific risk assessments related to the crisis, as it spreads through the economy. The tracking tool is updated weekly. It could provide relevant empirical data to inform models on the economic effects of the crisis. Such complementary empirical information could ultimately help policymakers to effectively target financial support in order to mitigate the economic shocks of the crisis.

Keywords: COVID-19, Coronavirus, Economic risk, Risk reports, SEC filings, Data mining, Natural language processing, Social data science

JEL Classification: C52, C82, H12, L0

Suggested Citation

Stephany, Fabian and Stoehr, Niklas and Darius, Philipp and Neuhäuser, Leonie and Teutloff, Ole and Braesemann, Fabian, The CoRisk-Index: A Data-Mining Approach to Identify Industry-Specific Risk Assessments Related to COVID-19 in Real-Time (April 27, 2020). Available at SSRN: https://ssrn.com/abstract=3607228 or http://dx.doi.org/10.2139/ssrn.3607228

Fabian Stephany

University of Oxford - Oxford Internet Institute ( email )

1 St. Giles
University of Oxford
Oxford OX1 3PG Oxfordshire, Oxfordshire OX1 3JS
United Kingdom

Bruegel ( email )

Rue de la Charité 33
B-1210 Brussels Belgium, 1210
Belgium

Alexander von Humboldt Institute for Internet and Society ( email )

Bebelplatz 1 | 10099
Berlin
Germany

Niklas Stoehr

University College London - Department of Computer Science ( email )

United Kingdom

Philipp Darius

Hertie School - Centre for Digital Governance ( email )

Friedrichstr. 180
Berlin, 10117
Germany

Leonie Neuhäuser

Hertie School of Governance ( email )

Friedrichstraße 180
Berlin, 10117
Germany

Ole Teutloff

University of Copenhagen - Copenhagen Center for Social Data Science ( email )

Øster Farimagsgade 5A
University of Copenhagen
Copenhagen, 1353
Denmark

HOME PAGE: http://https://sodas.ku.dk/

Fabian Braesemann (Contact Author)

University of Oxford - Oxford Internet Institute ( email )

1 St Giles
University of Oxford
Oxford, Oxfordshire OX1 3JS
United Kingdom

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