TY - JOUR
T1 - Twitter financial community sentiment and its predictive relationship to stock market movement
AU - Yang, Steve Y.
AU - Mo, Sheung Yin Kevin
AU - Liu, Anqi
N1 - Publisher Copyright:
© 2015 Taylor & Francis.
PY - 2015/10/3
Y1 - 2015/10/3
N2 - Twitter, one of the several major social media platforms, has been identified as an influential factor for financial markets by multiple academic and professional publications in recent years. The motivation of this study hinges on the growing popularity of the use of Twitter and the increasing prevalence of its influence among the financial investment community. This paper presents empirical evidence of the existence of a financial community on Twitter in which users’ interests align with financial market-related topics. We establish a methodology to identify relevant Twitter users who form the financial community, and we also present the empirical findings of network characteristics of the financial community. We observe that this financial community behaves similarly to a small-world network, and we further identify groups of critical nodes and analyse their influence within the financial community based on several network centrality measures. Using a novel sentiment analysis algorithm, we construct a weighted sentiment measure using tweet messages from these critical nodes, and we discover that it is significantly correlated with the returns of the major financial market indices. By forming a financial community within the Twitter universe, we argue that the influential Twitter users within the financial community provide a proxy for the relationship between social sentiment and financial market movement. Hence, we conclude that the weighted sentiment constructed from these critical nodes within the financial community provides a more robust predictor of financial markets than the general social sentiment.
AB - Twitter, one of the several major social media platforms, has been identified as an influential factor for financial markets by multiple academic and professional publications in recent years. The motivation of this study hinges on the growing popularity of the use of Twitter and the increasing prevalence of its influence among the financial investment community. This paper presents empirical evidence of the existence of a financial community on Twitter in which users’ interests align with financial market-related topics. We establish a methodology to identify relevant Twitter users who form the financial community, and we also present the empirical findings of network characteristics of the financial community. We observe that this financial community behaves similarly to a small-world network, and we further identify groups of critical nodes and analyse their influence within the financial community based on several network centrality measures. Using a novel sentiment analysis algorithm, we construct a weighted sentiment measure using tweet messages from these critical nodes, and we discover that it is significantly correlated with the returns of the major financial market indices. By forming a financial community within the Twitter universe, we argue that the influential Twitter users within the financial community provide a proxy for the relationship between social sentiment and financial market movement. Hence, we conclude that the weighted sentiment constructed from these critical nodes within the financial community provides a more robust predictor of financial markets than the general social sentiment.
KW - Financial community
KW - Network analysis
KW - Network centrality
KW - Regression analysis
KW - Sentiment analysis
KW - Twitter
KW - Volatility
UR - http://www.scopus.com/inward/record.url?scp=84941784126&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84941784126&partnerID=8YFLogxK
U2 - 10.1080/14697688.2015.1071078
DO - 10.1080/14697688.2015.1071078
M3 - Article
AN - SCOPUS:84941784126
SN - 1469-7688
VL - 15
SP - 1637
EP - 1656
JO - Quantitative Finance
JF - Quantitative Finance
IS - 10
ER -