文献类型：期刊文献

英文题名：Modeling excess comovement with information diffusion on social media

作者：Chen, Zhang-Hangjian[1,2];Gao, Xiang[3];Ren, Fei[1];Xiong, Xiong[4];Zhang, Wei[4]

机构：[1]East China Univ Sci & Technol, Sch Business, Shanghai, Peoples R China;[2]Anhui Univ, Sch Econ, Hefei, Peoples R China;[3]Shanghai Business Sch, Res Ctr Finance, Shanghai, Peoples R China;[4]Tianjin Univ, Coll Management & Econ, Tianjin, Peoples R China

年份：2026

外文期刊名：JOURNAL OF ECONOMIC INTERACTION AND COORDINATION

收录：;Scopus(收录号：2-s2.0-105043826591);WOS:【SSCI(收录号:WOS:001808443700001)】；

基金：This work was supported by the National Natural Science Foundation of China under Grant 71871094, 72141304, 71790594, and 72201003; the Young and Middle-Aged Teacher Training Action Program in Anhui Province Universities, China under Grant YQYB2024002; the Philosophy and Social Science Planning Project of Anhui Province, China under Grant AHSKQ2022D027; and the Research Project on Innovation and Development of Social Sciences in Anhui Province, China under Grant 2022CX031.

语种：英文

外文关键词：Excess comovement; Minority game; Financial modeling; Information diffusion; Common attention; Information interaction; C51; C79; G12

摘要：This paper proposes a new agent-based model grounded in the minority-game framework to reveal the underlying mechanism of excess comovement. We model two key information diffusion behaviors of investors on social media: common attention to different stocks and information interaction about a single stock. The simulation results show that both behaviors significantly influence excess comovement, but their roles differ contextually. For stock pairs with historically positive return correlations, the impact of common attention dominates excess comovement when information interactions are infrequent, and a higher ratio of co-investors amplifies this effect. In contrast, for pairs with historically negative correlations, information interaction becomes the dominant driver of excess comovement when the ratio of co-investors is low, especially during periods of high market herding. Furthermore, the model provides accurate forecasts of excess comovement for both the next day and week.