Moderating Uncertainty with Sentiment: The Case of the Federal Reserve Chair Communication

Can a single personal communication have a significant effect on the uncertainty of the monetary policy process? We estimate the personal communication risk profile of the U.S. Federal Reserve (Fed) Chair by using a new dataset of the sentiment revealed by their public statements during their tenure. We develop a new identification method using the implicit probability of the change of the federal fund rate, which is used to construct a new measure of entropic uncertainty of the monetary policy (MPEU) and analyze the impact of the Fed communication’s sentiment risk profile on the market price discovery process of interest rates, in the aftermath of the release of the Fed Chair public statements. To measure the sentiment risk we use a machine learning method (Naïve Bayes) on the statements of the Fed Chair. After controlling for the evolving state of the economy surrounding the meetings, we find that, based on the heterogeneity across Chairs and their personal traits, there is a significant statistical and economic difference in the communications’ sentiment, which is likely to affect the market’s reaction to monetary policy announcements. Specifically, the sentiment in the Chairs’ communications plays an important role in moderating the potential surprises in the Fed announcements, and it can be effectively used as a tool for controlling and measuring monetary policy shocks.