The Financial Instability - Monetary Policy Nexus: Evidence from the FOMC Minutes
Abstract
We analyze how financial stability considerations expressed in Federal Open Market Committee (FOMC) meetings are associated with U.S. monetary policy implementation and communication. Utilizing natural language processing and language modeling to analyze FOMC minutes, we measure the prevalence and sentiment of mandate-related and financial stability-related discussions. Financial stability discussions increase after the Global Financial Crisis, especially during tightening cycles and the COVID-19 pandemic. Outside the zero lower bound (ZLB), greater attention to financial stability is associated with lower policy rates while during the ZLB, a tighter unconventional monetary policy stance is observed. Financial stability discussions are not significantly associated with sentiment in subsequent press conferences. Methodologically, we introduce a labelled dataset and apply explainable AI to improve the transparency of our text analysis
Keywords
Explainable Artificial Intelligence; Financial Stability; FOMC Deliberations; Monetary Policy Communication; Natural Language Processing