Working Paper: CEPR ID: DP16034
Authors: Francesco Bianchi; Howard Kung; Roberto Gomez Cram
Abstract: This paper examines the extent to which individual politicians affect asset prices using a high-frequency identification approach. We exploit the regular flow of viewpoints contained in a large volume of tweets from members of US Congress. Congressional tweets targeting individual firms are collected and classified based on their tone. Supportive (critical) tweets increase (decrease) stock prices of the targeted firm in minutes around the tweet. The price response persists for several days, during which analysts revise their forecasts about the firm cash flows. Selected politician tweets linked to legislation affect the stock prices of firms in the same industry as the targeted firm. The timeline of politician viewpoints within a particular bill exhibits surges in relevant news that predict roll call votes months before the signing of the bill. We highlight how the social media accounts of politicians are a valuable source of political news.
Keywords: Asset Pricing; High-Frequency Identification; Social Media; Partisanship
JEL Codes: D72; G14
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| supportive tweets (Y60) | increase in stock prices (G10) |
| critical tweets (Y30) | decrease in stock prices (G10) |
| tweets (Y60) | revisions in analysts' expectations about firm cash flows (D25) |
| tweets linked to legislation (K16) | affect stock prices in the same industry (L11) |
| supportive tweets (Y60) | revisions in analysts' expectations about firm cash flows (D25) |
| price response to tweets (D49) | persists for several days (C41) |