The way investor sentiment engenders specific economic, social, or political ramifications represents the confluence of sociology, economics, and political science as interrelated academic disciplines. The accelerated progression of the Internet has significantly amplified the capacity for investor sentiment dissemination to exert a profound impact on economic and social dynamics. In particular, the emergence of social network platforms such as Twitter and StockTwits has enabled information transmission in virtual space to penetrate more quickly and deeply into the real world. This study analyzes whether investor sentiment on the Internet affects stock market in the real world based on StockTwits big data. The research explores the correlation between investor sentiment derived from social media platforms and stock market behavior, as both factors hold substantial importance in contemporary society. By building a time series model, I conducted an integrated type of analysis of social media data and market data. The results of this study are as follows. Firstly, based on the Granger causality test, investor sentiment within the preceding six trading days has been shown to precipitate stock market volatility, thereby indicating a robust lagging and unidirectional causal relationship between investor sentiment and stock price fluctuations. Second, based on the result of vector auto regression model, investor sentiment of stocks in different sectors is positively or negatively associated with stock market volatility.
Yulun Han (Thu,) studied this question.