Exploring Relationships Between Cryptocurrency News Outlets and Influencers' Twitter Activity and Market Prices
Meysam Alizadeh, Yasaman Asgari, Zeynab Samei, Sara Yari, Shirin, Dehghani, Mael Kubli, Darya Zare, Juan Diego Bermeo, Veronika Batzdorfer, and, Fabrizio Gilardi

TL;DR
This study investigates how Twitter activity from crypto influencers and news outlets, analyzed with large language models and statistical methods, correlates with and potentially predicts market price movements of major cryptocurrencies.
Contribution
It introduces a novel approach using LLMs to detect trading signals from social media posts and analyzes their temporal relationship with cryptocurrency prices.
Findings
Aggregated signals from top cryptocurrencies can Granger-cause price fluctuations with a 6-hour lag.
Different coverage patterns are observed between influencers and news outlets.
The relationship between social media signals and market prices varies across cryptocurrencies.
Abstract
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…
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Taxonomy
TopicsDigital Marketing and Social Media
