@article{StephanyNeuhaeuserStoehretal., author = {Stephany, Fabian and Neuh{\"a}user, Leonie and Stoehr, Niklas and Darius, Philipp and Teutloff, Ole and Braesemann, Fabian}, title = {The CoRisk-Index: A Data-Mining Approach to Identify Industry-Specific Risk Perceptions Related to Covid-19}, series = {Humanities and Social Sciences Communications}, volume = {9}, journal = {Humanities and Social Sciences Communications}, number = {1}, doi = {10.1057/s41599-022-01039-1}, 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.}, language = {en} } @techreport{StephanyStoehrDariusetal., type = {Working Paper}, author = {Stephany, Fabian and Stoehr, Niklas and Darius, Philipp and Neuh{\"a}user, Leonie and Teutloff, Ole and Braesemann, Fabian}, title = {The CoRisk-Index: A data-mining approach to identify industry-specific risk assessments related to COVID-19 in real-time}, series = {General Economics (econ.GN)}, journal = {General Economics (econ.GN)}, 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.}, language = {en} }