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While the coronavirus spreads, governments are attempting to reduce contagionrates at the expense of negative economic effects. Market expectationsplummeted, foreshadowing the risk of a global economic crisis and massunemployment. Governments provide huge financial aid programmes to mitigate theeconomic shocks. To achieve higher effectiveness with such policy measures, itis key to identify the industries that are most in need of support. In thisstudy, we introduce a data-mining approach to measure industry-specific risksrelated to COVID-19. We examine company risk reports filed to the U.S.Securities and Exchange Commission (SEC). This alternative data set cancomplement more traditional economic indicators in times of the fast-evolvingcrisis as it allows for a real-time analysis of risk assessments. Preliminaryfindings suggest that the companies' awareness towards corona-related businessrisks is ahead of the overall stock market developments. Our approach allows todistinguish the industries by their risk awareness towards COVID-19. Based onnatural language processing, we identify corona-related risk topics and theirperceived relevance for different industries. The preliminary findings aresummarised as an up-to-date online index. The CoRisk-Index tracks theindustry-specific risk assessments related to the crisis, as it spreads throughthe economy. The tracking tool is updated weekly. It could provide relevantempirical data to inform models on the economic effects of the crisis. Suchcomplementary empirical information could ultimately help policymakers toeffectively target financial support in order to mitigate the economic shocksof the crisis.

The CoRisk-Index: A data-mining approach to identify industry-specific risk assessments related to COVID-19 in real-time