Author ORCID Identifier
Pengcheng Zhang: https://orcid.org/0000-0003-0640-1131
Jiayin Qi: https://orcid.org/0000-0001-7162-4898
Abstract
This study explores how different types of firm-generated online content (FGOC) on social media affect stock performance. Employing signaling theory and limited attention theory, we analyze stock market data from 141 companies in the S&P 500 index and categorize FGOC on Twitter into distinct signal types through semantic analysis. Using econometric models, we estimate the relationships between these FGOC signals and abnormal stock returns. Our findings reveal that disseminating a greater number of strong image-enhancing FGOC signals, particularly those related to new products and financial matters, significantly enhances stock performance, resulting in higher abnormal stock returns. In contrast, weak image-enhancing FGOC signals not only fail to improve stock performance but also diminish the positive relationship between strong image-enhancing signals, especially those pertaining to financial information, and stock performance. This study contributes to the literature by illuminating the interplay between different types of FGOC, addressing the need for research on how varying informational elements interact in social media contexts. It provides practical guidance for managers on managing digital communication strategies to enhance investor engagement and optimize market outcomes.
Recommended Citation
Zhang, P., Luo, X., Qi, J., & Li, J. (In press). Firm-Generated Online Content in Social Media and Stock Performance: An Event Window Study of Twitter and the S&P 500. Communications of the Association for Information Systems, 56, pp-pp. Retrieved from https://aisel.aisnet.org/cais/vol56/iss1/5
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