Abstract: Social media is increasingly becoming an important arena for financial information dissemination and investor decision-making interactions.Leveraging massive investor behavior data to analyze financial market dynamics has emerged as a frontier direction in the intersection of behavioral finance and computational social science.Information diffusion within social networks profoundly influences asset pricing efficiency.However,existing studies largely rely on structured data,such as financial factors,ma-king it difficult to capture the dynamic evolution of investors’ network structures and emotional interactions.This study focuses on the relationship between investor social networks and asset pricing efficiency.Based on large-scale social media data,it constructs an investor-following network and uses the short-text posts published by users as node features.A stock price synchroni-city prediction model is then developed using a graph attention network,and interpretability methods are introduced to identify key textual features and typical network structures that drive synchronicity increases.Empirical results show that integrating investor network structures with textual information significantly improves the accuracy of stock price synchronicity prediction.Furthermore,interpretability analyses reveal key words and subgraph patterns that exert substantial influence on synchronicity prediction.This research provides a new data-driven and interpretable analytical perspective for asset pricing and market efficiency,and extends the intersection of deep learning and network science into financial research.

Key words: Stock price synchronicity, Social network, Investor sentiment, Graph attention network, Explainability analysis