47 Pages Posted: 9 Jan 2017 Last revised: 9 Mar 2017
Date Written: March 7, 2017
Following the recent discovery of social media’s predictive power for financial markets, we try to advance the literature by evaluating the role of social media network structure in distinguishing between value-relevant information and noises. Using data from the Bitcoin market, we provide empirical evidence that loosely-connected social media discussion networks are more accurate in predicting future returns. Although social media information linkages cause information free riding and damage the overall network prediction accuracy, they nevertheless serve as landmarks for identifying informed social media participants: value-relevant information is more likely to be shared by authors who stimulate active discussions among their peers. We also document a positive relationship between network connectedness and future trading intensity. Our study highlights the importance of leveraging network structures to improve the prediction accuracy of social media analytics for financial markets.
Keywords: Social Media Analytics, Network Structure, Bitcoin, Financial Market, Prediction, Topic Model
Suggested Citation: Suggested Citation
Xie, Peng and Chen, Hailiang and Hu, Yu Jeffrey, Network Structure and Predictive Power of Social Media for the Bitcoin Market (March 7, 2017). 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