Signal or Noise in Social Media Discussions: The Role of Network Cohesion in Predicting the Bitcoin Market
成果类型：
Article
署名作者：
Xie, Peng; Chen, Hailiang; Hu, Yu Jeffrey
署名单位：
California State University System; California State University East Bay; University of Hong Kong; University System of Georgia; Georgia Institute of Technology
刊物名称：
JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
ISSN/ISSBN：
0742-1222
DOI：
10.1080/07421222.2020.1831762
发表日期：
2020
页码：
933-956
关键词：
摘要：
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 Bitcointalk.org 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.