  In this study, we analyze documents published by central banks using text
mining techniques and propose a method to evaluate the policy tone of central
banks. Since the monetary policies of major central banks have a broad impact
on financial market trends, the pricing of risky assets, and the real economy,
market participants are attempting to more accurately capture changes in the
outlook for central banks' future monetary policies. Since the published
documents are also an important tool for the central bank to communicate with
the market, they are meticulously elaborated on grammatical syntax and wording,
and investors are urged to read more accurately about the central bank's policy
stance. Sentiment analysis on central bank documents has long been carried out,
but it has been difficult to interpret the meaning of the documents accurately
and to explicitly capture even the intentional change in nuance. This study
attempts to evaluate the implication of the zero-shot text classification
method for an unknown economic environment using the same model. We compare the
tone of the statements, minutes, press conference transcripts of FOMC meetings,
and the Fed officials' (chair, vice chair, and Governors) speeches. In
addition, the minutes of the FOMC meetings were subjected to a phase analysis
of changes in each policy stance since 1971.

Computation and Language