Showing papers by "Maik Schmeling published in 2022"
TL;DR: The authors used natural language processing techniques to measure topic-specific central bank stance and found that markets react to news on the topics of rate guidance, economic activity, and monetary conditions more closely than on others.
Abstract: We `decipher' monetary policy shocks by directly connecting them to the stance a central bank expresses in its communication about different topics. To measure topic-specific central bank stance, we apply natural language processing techniques to press conference statements of the European Central Bank (ECB). Using three sets of shocks established in the literature, that is high-frequency market reactions in interest rates, the entire term structure, or the joint response in interest rates and stock prices, we find that markets react to news on the topics `rate guidance', `economic activity', and `financial \& monetary conditions'. Our text-based results suggest that markets listen to the ECB's remarks on some topics more closely than on others, an insight that should be useful for the design of policy communication strategies.