@proceedings{204901,
  author = {Denis Peskoff and Adam Visokay and Sander Schulhoff and Benjamin Wachspress and Alan Blinder and Brandon M. Stewart},
  title = {GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and Doves},
  abstract = {<p>Markets and policymakers around the world hang on the consequential monetary policy decisions made by the Federal Open Market Committee (FOMC). Publicly available textual documentation of their meetings provides insight into members{\textquoteright} attitudes about the economy. We use GPT-4 to quantify dissent among members on the topic of inflation. We find that transcripts and minutes reflect the diversity of member views about the macroeconomic outlook in a way that is lost or omitted from the public statements. In fact, diverging opinions that shed light upon the committee{\textquoteright}s {\textquotedblleft}true{\textquotedblright} attitudes are almost entirely omitted from the final statements. Hence, we argue that forecasting FOMC sentiment based solely on statements will not sufficiently reflect dissent among the hawks and doves.</p>
},
  year = {2023},
  journal = {Findings of the Association for Computational Linguistics: EMNLP 2023},
  pages = {6529{\textendash}6539},
  month = {12/2023},
  publisher = {Association for Computational Linguistics},
  address = {Singapore},
  url = {https://aclanthology.org/2023.findings-emnlp.434/},
}