Working Paper: NBER ID: w28592
Authors: Yuriy Gorodnichenko; Tho Pham; Oleksandr Talavera
Abstract: We develop a deep learning model to detect emotions embedded in press conferences after the meetings of the Federal Open Market Committee 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, positive tone in the voices of Fed Chairs leads to statistically significant and economically large increases in share prices. In other words, how policy messages are communicated can move the stock market. In contrast, the bond market appears to take few vocal cues from the Chairs. Our results provide implications for improving the effectiveness of central bank communications.
Keywords: Monetary Policy; Emotion Detection; Financial Markets; Deep Learning
JEL Codes: D84; E31; E58; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Positive tone of voice (Y20) | Increase in share prices (G19) |
| Positive tone of voice (Y20) | Decrease in expected inflation (E31) |
| Positive tone of voice (Y20) | Increase in S&P 500 returns (G12) |
| Negative tone of voice (E43) | Null result in bond market reactions (G19) |