Predicting stock price movement using social network analytics: Posts are sometimes less useful
研究发现社交网络上的投资者对话特征（论点相似性、情绪相似性、对话规模）与股价突变显著相关，监控对话动态能提升预测能力，但需警惕社交传染的负面影响。
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. • Applying social contagion theory to study stock returns prediction by social media posts. • Examining cognitive and emotional contagion and extensity of social contagion. • Cautioning against over-reliance on social media data for stock returns prediction. • Emphasizing the significance of conversation-level metrics in social media analysis.