  I propose a new tool to characterize the resolution of uncertainty around
FOMC press conferences. It relies on the construction of a measure capturing
the level of discussion complexity between the Fed Chair and reporters during
the Q&A sessions. I show that complex discussions are associated with higher
equity returns and a drop in realized volatility. The method creates an
attention score by quantifying how much the Chair needs to rely on reading
internal documents to be able to answer a question. This is accomplished by
building a novel dataset of video images of the press conferences and
leveraging recent deep learning algorithms from computer vision. This
alternative data provides new information on nonverbal communication that
cannot be extracted from the widely analyzed FOMC transcripts. This paper can
be seen as a proof of concept that certain videos contain valuable information
for the study of financial markets.
