Risk & returns around FOMC press conferences: a novel perspective from computer vision
This paper offers a novel, incremental approach to understanding financial market reactions to FOMC events for investors and financial analysts, by extracting information from nonverbal cues in video data.
This paper introduces a new measure of discussion complexity during FOMC press conferences, derived from video analysis of the Fed Chair's reliance on internal documents. It finds that complex discussions correlate with higher equity returns and a decrease in realized volatility.
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.