Fed Transparency: A Sentiment Analysis Approach

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Abstract/Contents

Abstract: This paper studies the transparency of the Federal Open Market Committee (FOMC) in terms of its communication of sentiment. I use automated text analysis methods to produce topic and sentiment labels for all FOMC communications and documents, including meeting transcripts, meeting minutes, speeches, statements, press conferences, and staff briefings. I define transparency as the extent to which the sentiment of communications can predict meeting transcripts sentiment. I establish three key results. First, the sentiment of communications is consistently inflated relative to transcript sentiment. This is highly consistent with a model in which the FOMC receives asymmetric payoffs to market misinterpretation of communications. Second, despite sentiment inflation, communications can be used to very accurately predict transcript sentiment, with pseudo-R2 around 0.8 since 2000. Models using only communications sentiment can also predict future policy changes, with classification accuracy of 71%. Third, the FOMC's 1993 decision to publish its meeting transcripts sharply increased the transparency of the meeting minutes and led committee members to more closely adhere to the views of their staff, but at a potential cost of greater conformity among members.

Subject	Transparency
Subject	Sentiment analysis
Subject	United States. Federal Open Market Committee
Subject	Monetary policy
Genre	Text
Genre	Thesis

DOI	https://doi.org/10.25740/hh572kp6243
Location	https://purl.stanford.edu/hh572kp6243

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License: This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International license (CC BY-NC).

Preferred citation: Prazad, S. (2022). Fed Transparency: A Sentiment Analysis Approach. Stanford Digital Repository. Available at https://purl.stanford.edu/hh572kp6243

Stanford University, Department of Economics, Honors Theses

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