Cryptocurrency Twitter Signals, News Coverage, and Price Dynamics

Summary

This paper studies whether trading signals expressed in cryptocurrency influencers' and news outlets' Twitter posts are associated with market prices. Large language models identify buy and non-buy signals in posts, which are then compared with prices for nine major cryptocurrencies. The analysis uses vector autoregression, Granger causality tests, and cross-correlation to examine timing and relationships between aggregated signals and price movements.

Results vary across cryptocurrencies and periods, and the paper also reports differences in how influencers and news outlets cover the assets. For the three cryptocurrencies most represented in the posts, the aggregated signal over the prior 24 hours Granger-causes price fluctuations with a lag of at least six hours. This is evidence of temporal predictive association under the study's approach, not proof that posts cause prices or that the signals produce profitable trades. The summary does not describe trading costs, execution, or a strategy backtest.

Key ideas

• Large language models classify Twitter posts from crypto influencers and news outlets as buy or non-buy signals.
• The study relates detected signals to prices for nine major cryptocurrencies using VAR, Granger causality, and cross-correlation analysis.
• Results differ across assets and time periods.
• For the three most frequently mentioned cryptocurrencies, prior 24-hour signals Granger-cause price fluctuations with a lag of at least six hours.
• The analysis reports differences between influencer and news outlet coverage, but does not establish trading profitability.

Tags

# Exploring Relationships Between Cryptocurrency News Outlets and Influencers' Twitter Activity and Market Prices


# Exploring Relationships Between Cryptocurrency News Outlets and Influencers' Twitter Activity and Market Prices









Academics increasingly acknowledge the predictive power of social media for a wide variety of events and, more specifically, for financial markets. Anecdotal and empirical findings show that cryptocurrencies are among the financial assets that have been affected by news and influencers' activities on Twitter. However, the extent to which Twitter crypto influencer's posts about trading signals and their effect on market prices is mostly unexplored. In this paper, we use LLMs to uncover buy and not-buy signals from influencers and news outlets' Twitter posts and use a VAR analysis with Granger Causality tests and cross-correlation analysis to understand how these trading signals are temporally correlated with the top nine major cryptocurrencies' prices. Overall, the results show a mixed pattern across cryptocurrencies and temporal periods. However, we found that for the top three cryptocurrencies with the highest presence within news and influencer posts, their aggregated LLM-detected trading signal over the preceding 24 hours granger-causes fluctuations in their market prices, exhibiting a lag of at least 6 hours. In addition, the results reveal fundamental differences in how influencers and news outlets cover cryptocurrencies.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.