Risk & returns around FOMC press conferences: a novel perspective from computer vision

TL;DR

This paper introduces a computer vision-based method to measure discussion complexity during FOMC press conferences, revealing links between communication patterns and market outcomes, thus providing new insights beyond traditional transcript analysis.

Contribution

It develops a novel computer vision approach to quantify nonverbal communication in press conferences, offering a new perspective on market reactions to Federal Reserve communications.

Findings

Complex discussions correlate with higher equity returns.

More complex discussions are associated with lower realized volatility.

Video-based analysis captures nonverbal cues not present in transcripts.

Abstract

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.