Measuring decentralised finance regulatory uncertainty
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- Master Thesis 
develop and compare two DeFi regulatory uncertainty indexes based on the specialised media coverage frequency. By applying active learning in combination with SVM to identify uncertainty-related news articles, I show how regulatory uncertainty can be captured for emerging industries with limited data intervals. Both indexes display rising levels of DeFi regulatory uncertainty in recent years with the highest index spikes appearing during the first wave of the COVID-19 pandemic. By constructing a structural VAR model, I identified the negative effects of regulatory uncertainty shocks on total value locked in smart contracts of decentralised financial services. The negative response is consistent among the leading DeFi categories, such as lending services and decentralised exchanges. However, uncertainty shocks cause an increase of total value locked in derivatives and payment protocols.