GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and Doves

Denis Peskoff, Adam Visokay, Sander Schulhoff, Benjamin Wachspress, Alan Blinder, Brandon Stewart


Abstract
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’ 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’s “true” 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.
Anthology ID:
2023.findings-emnlp.434
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6529–6539
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.434
DOI:
10.18653/v1/2023.findings-emnlp.434
Bibkey:
Cite (ACL):
Denis Peskoff, Adam Visokay, Sander Schulhoff, Benjamin Wachspress, Alan Blinder, and Brandon Stewart. 2023. GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and Doves. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6529–6539, Singapore. Association for Computational Linguistics.
Cite (Informal):
GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and Doves (Peskoff et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-emnlp.434.pdf