The Effects of Ego Network Structure on Market Reactions: A Social Network Analysis Perspective of Twitter Cashtag Networks and Earnings Announcement Events
Overview
This one-hour webinar will present recent academic research that has been funded by CPA Ontario and the Schulich CPA Ontario Centre in Digital Financial Information.
Abstract
Networks pervade our social and professional lives, ranging from golfing partnerships and lunch ties to inter-locking board-of-director relations, Facebook friendships, and LinkedIn connections. A core premise of the field of social network analysis is that information flows are heavily influenced by network structure, including the extent to which the network is dominated by a few key actors, how inter-connected network members are, and the prevalence of sub-groups and cliques. We apply this logic to the stock market, analyzing the ego network structure of Twitter cashtag discussion networks for stocks in the S&P 1,500 index. We posit that the structure of these Twitter cashtag networks – specifically, their centralization, density, clustering, and prevalence of isolates – affects market reactions to quarterly earnings announcements. We find that abnormal market returns are negatively associated with the degree of centralization and density and positively associated with the extent of sub-groups or cliques and the prevalence of unconnected isolates. These results highlight the importance of understanding network structures in predicting stock market movements, offering novel insights into the behavioral aspects of financial markets.
Webinar participants will receive an official verified confirmation of participation after the webinar that can be used toward CPA professional development requirements.
This one-hour webinar will present recent academic research that has been funded by CPA Ontario and the Schulich CPA Ontario Centre in Digital Financial Information.
Abstract
Networks pervade our social and professional lives, ranging from golfing partnerships and lunch ties to inter-locking board-of-director relations, Facebook friendships, and LinkedIn connections. A core premise of the field of social network analysis is that information flows are heavily influenced by network structure, including the extent to which the network is dominated by a few key actors, how inter-connected network members are, and the prevalence of sub-groups and cliques. We apply this logic to the stock market, analyzing the ego network structure of Twitter cashtag discussion networks for stocks in the S&P 1,500 index. We posit that the structure of these Twitter cashtag networks – specifically, their centralization, density, clustering, and prevalence of isolates – affects market reactions to quarterly earnings announcements. We find that abnormal market returns are negatively associated with the degree of centralization and density and positively associated with the extent of sub-groups or cliques and the prevalence of unconnected isolates. These results highlight the importance of understanding network structures in predicting stock market movements, offering novel insights into the behavioral aspects of financial markets.
Webinar participants will receive an official verified confirmation of participation after the webinar that can be used toward CPA professional development requirements.
