Investor disagreement is central to theories of trading, yet existing proxies are either low-frequency or depend on platform-specific investor labels. We develop a daily, replicable measure of disagreement for unlabeled social media and examine its relation to trading activity in China's A-share market. Using 52.3 million investor comments on CSI 300 constituents from 2016 to 2025, we implement an LLM-based stance-simulation framework in which neutral, bullish, and bearish agents generate annotated training data that are then scaled to the full corpus using three parallel pre-trained language model classifiers. This design yields two distinct disagreement measures: within-group disagreement, capturing dispersion within a shared stance-conditioned interpretation, and cross-group disagreement, capturing divergence across stance-conditioned interpretations. We find that within-group disagreement is negatively associated with abnormal trading volume, whereas cross-group disagreement is positively associated with it. The negative within-group relation strengthens during periods of high policy uncertainty, consistent with a value-of-waiting channel, while the positive cross-group relation is stronger when disagreement involves bearish interpretations and under favorable market conditions, consistent with gains from trade under competing valuation models. The results are robust to alternative assumptions about latent group weights, before-market-open measurement, alternative LLM annotation, and a post-release annotation sample. These findings show that the trading implications of disagreement depend on whether heterogeneity reflects within-stance uncertainty or cross-stance conflict. More broadly, the approach provides a scalable framework for measuring belief heterogeneity in unlabeled text environments.

We investigate how the loss of informative salient signals in financial markets influences stock return volatility, using the 2024 intraday disclosure reform of the mainland China-Hong Kong Stock Connect program as a natural experiment. The reform eliminated the real-time disclosure of northbound capital (NC) flows on trading platforms, rendering NC trading information invisible to Chinese investors during market hours. We find that the removal of NC signals induces increased investor belief dispersion and intensifies informed trading, thereby amplifying intraday volatility in NC-eligible stocks. Moreover, this effect is more pronounced for stocks with higher investor attention, indicating that attentive investors suffer stronger anchor loss when NC signals disappear. In contrast, lottery-type stocks and stocks with alternative NC trading clues exhibit weaker volatility responses, since the presence of strong alternative signals reduces the effect of NC signal loss. These findings highlight the informational role of insightful salient signals in stabilizing stock returns.

Traditional information asymmetry theories suggest that greater corporate transparency should reduce investor disagreement. Using Chinese mutual fund holdings, we document the opposite pattern: transparency amplifies disagreement among institutional investors. Mechanism tests show that transparency discourages herding while intensifying private information acquisition among fund managers. The effect is stronger for growth-oriented and high-skill funds, and during periods of elevated market sentiment, and among firms with lower credibility, excessive disclosure frequency, and greater investor attention. Further analysis indicates that this transparency-induced disagreement stems from informed trading rather than noise, thereby enhancing price informativeness and market efficiency. Overall, the evidence reveals the dual nature of transparency as both an informational input and a behavioral catalyst that increases disagreement in financial markets.

Using the data of Chinese listed firms from 2003-2020, this study applies a System GMM estimation approach to document that high local government ecological concerns increase a firm’s stock price crash risk. This finding remains consistent after addressing endogeneity issues and undergoing robustness checks. This study also reveals that the implementation of the new environmental protection law in 2015 mitigates the relationship between local government ecological concerns and stock price crash risk. Further analyses indicate that stricter environmental regulation and high subsidies, as well as enhanced corporate social responsibility and governance, can effectively alleviate the adverse effect of local government ecological concerns on stock price crash risk. In addition, we note that the influence of local government ecological concerns on stock price crash risk is more significant in the eastern region, heavily polluting industries, and non-SOEs. Lastly, the research identifies two potential channels through which local government ecological concerns can impact stock price crash risk by reducing the quality of information disclosure and intensifying investor disagreement.