Investor Information Interaction and Stock Price Co-Jumps

Co-jumps, defined as synchronized and abrupt price movements across multiple stocks within extremely short time intervals, have significant implications for financial markets, especially in asset pricing, portfolio management, and risk mitigation. Despite the recognized importance of co-jumps, there remains a notable gap in the academic literature investigating their causes. This article utilizes billion-level textual big data from the <ext-link>Eastmoney.com</ext-link> stock forum during 2020–2023 and employs a two-way fixed-effects panel regression model to assess the predictive impact of individual investor information interactions on stock price co-jumps, as well as the forecasting power of co-jumps for future stock price movements. Furthermore, the authors develop effective investment strategies that demonstrate significant outperformance compared to the Shanghai Stock Exchange Composite Index, providing valuable data-driven insights for investors in formulating trading strategies and assessing market risks.