  The effectiveness of central bank communication is a crucial aspect of
monetary policy transmission. While recent research has examined the influence
of policy communication by the chairs of the Federal Reserve on various
financial variables, much of the literature relies on rule-based or
dictionary-based methods in parsing the language of the chairs, leaving nuanced
information about policy stance contained in nonverbal emotion out of the
analysis. In the current study, we propose the Fine-Grained Monetary Policy
Analysis Framework (FMPAF), a novel approach that integrates large language
models (LLMs) with regression analysis to provide a comprehensive analysis of
the impact of the press-conference communications of chairs of the Federal
Reserve on financial markets. We conduct extensive comparisons of model
performance under different levels of granularity, modalities, and
communication scenarios. Based on our preferred specification, a one-unit
increase in the sentiment score is associated with an increase of the price of
S\&P 500 Exchange-Traded Fund by approximately 500 basis points, a
15-basis-point decrease in the policy interest rate, while not leading to a
significant response in exchange rates.
