Journal of Management Information Systems

Volume 37 Number 4 2020 pp. 933-956

Signal or Noise in Social Media Discussions: The Role of Network Cohesion in Predicting the Bitcoin Market

Xie, Peng, Chen, Hailiang, and Hu, Yu Jeffrey


Prior studies have shown that social media discussions can be helpful in predicting price movements in financial markets. With the increasingly large amount of social media data, how to effectively distinguish value-relevant information from noise remains an important question. We study this question by investigating the role of network cohesion in the relationship between social media sentiment and price changes in the Bitcoin market. As network cohesion is associated with information correlation within the discussion network, we hypothesize that less cohesive social media discussion networks are better at predicting the next-day returns than more cohesive networks. Both regression analyses and trading simulations based on data collected from confirm our hypothesis. Our findings enrich the literature on the role of social media in financial markets and provide actionable insights for investors to trade based on social media signals.

Key words and phrases: social media analytics, network cohesion, financial technology, bitcoin, fintech