Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/319627 
Erscheinungsjahr: 
2025
Schriftenreihe/Nr.: 
Deutsche Bundesbank Discussion Paper No. 13/2025
Verlag: 
Deutsche Bundesbank, Frankfurt a. M.
Zusammenfassung: 
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.
Schlagwörter: 
Explainable Artificial Intelligence
Financial Stability
FOMC Deliberations
Monetary Policy Communication
Natural Language Processing
JEL: 
E44
E52
E58
ISBN: 
978-3-98848-034-7
Dokumentart: 
Working Paper
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