This study develops a comprehensive methodological framework for identifying and quantifying investor echo chambers in online stock discussion forums. Motivated by a dynamic model of endogenous echo chamber formation, which formalizes how investors optimally allocate attention and update beliefs under cognitive and informational constraints, we construct a two-layer multiplex investor network that integrates common-attention similarity and semantic similarity to jointly capture the informational and cognitive linkages among investors. This framework enables the systematic examination of how shared information sources and convergent opinions emerge within investor communities. We compute both community-level and individual-level (node-level) echo-chamber intensity by integrating measures of social homophily, semantic reinforcement, and community insularity. At the firm level, we further aggregate these micro-level indicators using attention-weighted indices, community concentration (HHI), and semantic polarization metrics to characterize how echo-chamber dynamics manifest in firm-related discussions. In addition, we propose a general empirical panel framework to examine the relationship between investor echo-chamber intensity and firm-level outcomes. Overall, this paper provides a methodological foundation for the broader Investors’ Echo Chamber Project, offering scalable tools for network-based behavioral analysis and laying the groundwork for future research linking online social dynamics, financial market efficiency, and corporate decision-making.

In this paper, we argue that investor sentiment is a more direct determinant for asset pricing than information, thus we propose the Sentiment Efficient Markets Hypothesis (S-EMH), complementary to the traditional Efficient Market Hypothesis (EMH), to provide a powerful instrument to interpret financial facts and anomalies inconsistent with the traditional EMH. Besides the theoretical argument, we also verify the hypothesis with a brand-new systematic index of investor sentiment, Gubasenti, derived from textual analysis on more than 200 million posts from an online Chinese stock forum. The examinations are implemented in both market-level and firm-level, and results show that investor sentiment has a significant impact on asset pricing in both levels. It demonstrates the proposed hypothesis.