Data and Code for: The Voice of Monetary Policy

Principal Investigator(s): View help for Principal Investigator(s) Yuriy Gorodnichenko, University of California-Berkeley; Tho Pham, University of Reading; Oleksandr Talavera, University of Birmingham

Version: View help for Version V1

Name	File Type	Size	Last Modified
codes			12/05/2022 12:59:PM
data			12/05/2022 01:00:PM

Project Description

Summary:

We develop a deep learning model to detect emotions embedded in press conferences after the Federal Open Market Committee meetings and examine the influence of the detected emotions on financial markets. We find that, after controlling for the Fed’s actions and the sentiment in policy texts, a positive tone in the voices of Fed chairs leads to significant increases in share prices. Other financial variables also respond to vocal cues from the chairs. Hence, how policy messages are communicated can move the financial market. Our results provide implications for improving the effectiveness of central bank communications.

Scope of Project

Subject Terms: Machine learning

JEL Classification:
      D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
      E50 Monetary Policy, Central Banking, and the Supply of Money and Credit: General
      G10 General Financial Markets: General (includes Measurement and Data)

Geographic Coverage: US

Data Type(s): aggregate data; audio: sound data; observational data; program source code; text

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Published Versions

V1 [2023-01-23]

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