Abstract:
We analyze how financial stability concerns discussed during Federal Open Market Committee (FOMC) meetings influence the Federal Reserve's monetary policy imple- mentation and communication. Utilizing large language models (LLMs) to analyze FOMC minutes from 1993 to 2022, we measure both mandate-related and financial stability-related sentiment within a unified framework, enabling a nuanced examina- tion of potential links between these two objectives. Our results indicate an increase in financial stability concerns following the Great Financial Crisis, particularly dur- ing periods of monetary tightening and the COVID-19 pandemic. Outside the zero lower bound (ZLB), heightened financial stability concerns are associated with a reduc- tion in the federal funds rate, while within the ZLB, they correlate with a tightening of unconventional measures. Methodologically, we introduce a novel labeled dataset that supports a contextualized LLM interpretation of FOMC documents and apply explainable AI techniques to elucidate the model's reasoning.