The global pandemic caused by the coronavirus disease (COVID-19) came mostly as a surprise and had a major effect on the global economy. This type of major events that can bring societies to nearly a total standstill are difficult to predict but have a significant impact on business activities. Nevertheless, weak signals might be possible to detect beforehand to enable preparation for the impact, both globally and locally. This study analyses the impact of the first wave of the COVID-19 pandemic on the Nasdaq Helsinki stock exchange by utilising large-scale media analytics. This entails gaining data through media monitoring over the entire duration of the pandemic by applying black-box algorithms and advanced analytics on real cases. The data analysis is carried out to understand the impact of a such global event in general, while aiming to learn from the potential weak signals to enable future market intelligence to prepare for similar events. A social media firestorm scale, similar to the Richter scale for earthquakes or Sapphir-Simpson scale for hurricanes, is utilised to support the analysis and assist in explaining the phenomenon. The results indicate that pandemics and their impact on markets can be studied as a subset of a media firestorms that produce a sharkfin type of pattern in analytics. The findings indicate that early signals from such events are possible to detect by means of media monitoring, and that the stock exchange behaviour is affected. The implications include highlighting the importance of weak signal detection from abundant data to have the possibility to instigate preventive actions and prepare for such events to avoid maximum negative business impact. The early reaction to this type of events requires a very streamlined connection between market intelligence and different business activities
