Tales of Turbulence: BERT-based Multimodal Analysis of FED Communication Dynamics Amidst COVID-19 Through FOMC Minutes

Abstract

AbstractThis study analyzes Federal Open Market Committee (FOMC) minutes using state-of-the-art Natural Language Processing techniques. We sought to investigate the effect of the global COVID-19 crisis on the FOMC minutes’ pattern and the strength of the Federal Reserve to influence inflation expectations through its primary press releases. To this end, we first quantified minutes leveraging domain-specific pre-trained Bidirectional Encoder Representations from Transformers models (FinBERTs). Then, we applied dynamic time warping to measure temporal sequence proximity over time. In addition, we built multivariable autoregressive integrated moving average models to verify our findings by injecting exogenous variables as indicator functions into the time series. The results suggest that the Federal Reserve has abstained from adjusting its tone and the forward-lookingness settings of its statements for the global pandemic. Therefore, the longstanding association of the FED's tone and forward-lookingness with consumers’ inflation expectations index has weakened during the global health crisis.