Predicting stock price movement using social network analytics: Posts are sometimes less useful

Abstract

Contemporary research has leveraged social network data as a predictive tool for decision-making process in the capital market. Yet, its effectiveness may be compromised by social contagion. This study addresses this problem by introducing conversation-level measures that capture how interactions among investors affect market predictions. Drawing on social contagion theory, we identified three conversation conditions—argument similarity, sentiment similarity, and conversation size—and examined their association with the likelihood of abrupt stock price changes, which indicate a loss of collective wisdom. Our analysis of 18 million StockTwits posts for 859 Initial Public Offerings (2008–2017) reveals that conversations with highly similar arguments, highly similar sentiments, and larger size are significantly associated with an increased likelihood of abrupt stock price changes in the subsequent week. Moreover, out-of-sample tests confirm that monitoring conversational dynamics enhances the predictive power of social network analytics, offering valuable guidance for investors and practitioners. Our study extends the theoretical framework of social contagion by highlighting the importance of the conversation level and provides practical recommendations for refining trading strategies based on social media data.