Do regulated interaction platforms enhance market integrity? Insider trading evidence from China’s A-share market

Purpose This study aims to examine how regulated investor-company dialogue on exchange-run platforms impacts insider trading profitability. It further investigates the conditions under which this disciplinary effect is strongest by analyzing the moderating roles of institutional ownership, analyst coverage and media attention. By exploring discussion topics and sentiment, this paper clarifies the dual function of these regulated platforms as both a transparency mechanism and a potential tool for market manipulation in an emerging market. Design/methodology/approach This study matches insider trades from Chinese A-share firms (2019–2023) with Q&A data from exchange-run interaction platforms. Interaction intensity is proxied by the log of question counts and word counts over a 360-day pre-trade window. Insider profitability is measured by 30-day abnormal returns from a Fama-French five-factor model. The main relationship is identified using fixed-effects regressions with two-way clustered standard errors. A battery of robustness checks, including alternative return windows, a three-factor model and latent dirichlet allocation topic modeling and sentiment analysis, confirms the results and explores underlying mechanisms. Findings Increased question volume and complexity on regulated platforms significantly reduce insider trading profits. This disciplinary effect is stronger in firms with lower institutional ownership and less analyst or media coverage, suggesting the platform acts as a substitute for traditional monitoring in opaque firms. Findings are robust to alternative specifications. Topic analysis reveals that discussions on strategy and projects are most effective at curbing insider gains. The evidence establishes these platforms as a credible external governance mechanism, although risks of strategic manipulation by insiders remain. Originality/value This paper provides novel empirical evidence linking China’s exchange-sponsored Q&A platforms to insider-trading outcomes. By pairing trading records with platform interaction data and applying NLP techniques, we move beyond traditional disclosure proxies to capture transparency in real time. The findings extend the literatures on insider trading, RegTech and corporate governance by quantifying the conditions under which this digital disclosure is most effective. For regulators, the evidence supports algorithmic surveillance of these platforms as a cost-effective tool to enhance market integrity, especially for firms in opaque information environments.