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Abstract: We study soft information contained in congressional testimonies by the Federal Reserve Chairs and analyze its effects on financial markets. Using machine learning, we construct high-frequency measures of Fed Chairs' and Congress members' emotions expressed via their words, voice and face. Increases in the Chair's text-, voice-, or face-emotion indices during the testimony generally raise the S&P500 index and lower the VIX. Stock prices are particularly sensitive to both the members' questions and the Fed Chair's answers about issues directly related to monetary policy. These effects add up and propagate after the testimony, reaching magnitudes comparable to those after a policy rate cut. Our findings resonate with the view in psychology that communication is much more than words and underscore the need for a holistic approach to central bank communication.
Keywords: Central bank communications, Financial markets, High-frequency identification, Facial emotion recognition, Vocal signal processing, Textual Analysis
JEL Classification: E52; E58; E71