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.
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Notes
- 1.
One common drawback of NLP methods in finance/economics is the need to create a dictionary of positive and negative words. The choice of which words belong to which set is somehow subjective. Another problem with more advanced methods is the necessity to label the data which might have to be chosen by the researcher.
- 2.
I remove the conference from the \(15^{\text {th}}\) of March 2020 simply because there is no video available (it is only audio).
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Marchal, A. (2022). Risk and Returns Around FOMC Press Conferences: A Novel Perspective from Computer Vision. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-82196-8_54
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