Zusammenfassung:
We measure soft information contained in the congressional testimonies of U.S. Federal Reserve Chairs and analyze its effect on financial markets. Our measures of Fed Chairs' emotions expressed in words, voice and facial expressions are created using machine learning. Increases in the Chair's text-, voice-, or face-emotion indices during these testimonies generally raise the S&P500 index and lower the VIX-indicating that these cues help shape market responses to Fed communications. These effects add up and propagate after the testimony, reaching magnitudes comparable to those after a policy rate cut. Markets respond most to the Chair's emotions expressed about issues related to monetary policy.