How Much Information Do Monetary Policy Committees Disclose? Evidence from the FOMC's Minutes and Transcripts

Abstract

The purpose of central bank minutes is to give an account of monetary policy meeting discussions to outside observers, thereby enabling them to draw informed conclusions about future policy. However, minutes are by necessity a shortened and edited representation of a broader discussion. Consequently, they may omit information that is predictive of future policy decisions. To investigate this, we compare the information content of the FOMC's minutes and transcripts, focusing on three dimensions which are likely to be excluded from the minutes: 1) the committee's degree of hawkishness; 2) the chairperson's degree of hawkishness; and 3) the level of agreement between committee members. We measure committee and chairperson hawkishness with a novel dictionary that is constructed using the FOMC's minutes and transcripts. We measure agreement by performing deep transfer learning, a technique that involves training a deep learning model on one set of documents -U.S. congressional debates -and then making predictions on another: FOMC transcripts. Our findings suggest that transcripts are more informative than minutes and heightened committee agreement typically precedes policy rate increases.

Forecasting sovereign risk in the Euro area via machine learning

“…For instance, “inflation accelerating” is classified as a hawkish modifier and “weak inflation” as a dovish modifier. Belly and Istrefi (2020)'s dictionary is a modified and enhanced version of the dictionary of Apel et al (2022), which is based on the language of the Federal Reserve FOMC's meeting transcripts and Minutes.…”

Can we Use High‐Frequency Data to Better Understand the Effects of Monetary Policy and its Communication? Yes and No!*

“…Apel and Grimaldi (2014) use text analysis to measure the sentiment and tone of the Swedish Central Bank's minutes and find that it can be used to improve predictions of future policy decisions. Apel et al (2022) develop a new dictionary constructed using the FOMC's minutes and transcripts to measure the committee's and chairperson's degree of hawkishness and use machine learning techniques for the purpose of analysing the level of agreement between committee members. Their findings suggest that transcripts are more informative than minutes and heightened committee agreement typically precedes policy rate increases.…”

“…These two types of shocks would have opposite effects on the macroeconomy and may net out when used in the SVAR. Combining the KMS approach with textbased approaches applied to the announcement text, such as Apel and Grimaldi (2014) and Apel et al (2022), could be a fruitful future direction, allowing for the differentiation of different types of path shocks.…”

The Predictive Power of Central Bank Communication: Evidence from Mexico

“…In parallel, using tone indicators derived from different validated dictionaries for cb sentiment analysis (see Loughran & Mcdonald, 2011Apel & Blix-Grimaldi, 2012Bennani & Neuenkirch, 2017;Gonzalez & Tadle, 2021;and Apel et al, 2022), several studies have constructed tone indicators to capture bias in cb communications. The dictionaries categorize the words used by the cbs as either hawkish or dovish with the goal of capturing the policy bias of central bankers, thus, they allow to assign a numerical value and create a measurement that accurately reflects this bias.…”