Network Cohesion and Predictive Power of Social Media in the Bitcoin Market

46 Pages Posted: 9 Jan 2017 Last revised: 12 Feb 2019

See all articles by Peng Xie

Peng Xie

Georgia Institute of Technology - Scheller College of Business

Hailiang Chen

The University of Hong Kong - Faculty of Business and Economics

Yu Jeffrey Hu

Georgia Institute of Technology - Scheller College of Business

Date Written: February 2019

Abstract

Prior studies have shown that social media discussions can be helpful in predicting stock price movements. Given the large amount of social media data, how to effectively distinguish value-relevant information from noise remains an important question. We study this question from a network perspective and investigate the role of discussion network cohesion in affecting its return predictability. Building on theoretical arguments of information correlation, information free-riding, and hidden profile effect, we hypothesize that less cohesive social media discussion networks are more accurate in predicting future price movements. We empirically test our hypothesis using data from the Bitcoin market. Due to its highly speculative nature, social media discussions are likely to play a more significant role, and it is thus an ideal setting for our analyses. We find results consistent with our hypothesis, and they are robust after controlling for traditional media coverage and topic coverage in social media discussions.

Keywords: financial technology, social media analytics, network cohesion, Bitcoin, topic modeling

Suggested Citation

Xie, Peng and Chen, Hailiang and Hu, Yu Jeffrey, Network Cohesion and Predictive Power of Social Media in the Bitcoin Market (February 2019). Georgia Tech Scheller College of Business Research Paper No. 17-5. Available at SSRN: https://ssrn.com/abstract=2894089 or http://dx.doi.org/10.2139/ssrn.2894089

Peng Xie

Georgia Institute of Technology - Scheller College of Business ( email )

800 West Peachtree St.
Atlanta, GA 30308
United States
4043696131 (Phone)

Hailiang Chen (Contact Author)

The University of Hong Kong - Faculty of Business and Economics ( email )

K. K. Leung Building
University of Hong Kong
Hong Kong

HOME PAGE: http://www.fbe.hku.hk/people/academic/hailiang-chen

Yu Jeffrey Hu

Georgia Institute of Technology - Scheller College of Business ( email )

800 West Peachtree St.
Atlanta, GA 30308
United States

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