In emerging markets characterized by signiffcant information asymmetry, mitigat-ing firm-level risk is paramount for market stability. While the governance role ofinstitutional investors is known, the impact of their direct, on-the-ground engagementremains underexplored. This study’s objective is to investigate how institutionalinvestor site visits, a crucial hands-on governance mechanism, affect stock returnvolatility. Using a sample of Chinese-listed A-share firms from 2012 to 2022, wefind that frequent site visits significantly reduce firm-level stock return volatility.This risk-reduction effect is more pronounced for firms with greater agency problems,poorer ESG performance, and higher expropriation risk. Our analysis, robust toendogeneity concerns, indicates this effect is driven by improved external oversight.We conclude that direct institutional engagement is a vital channel for reducinginformation asymmetry, enhancing corporate governance, and ultimately promotingmarket stability by lowering investment risk.

We examine price discovery after irregularly scheduled macroeconomic announce-ments. Exploiting time variation in Chinese macro announcements released outside regular trading hours, this paper isolates the role of elapsed non-trading time in facilitating investor learning and price discovery upon market reopening. We show that longer non-trading intervals generate more efficient post-announcement price discovery, reduce information asymmetry, and diminish subsequent intraday return reversals. The mechanism operates through enhanced retail investor learning: during non-trading hours, retail investors actively acquire information, subsequently trade more aggressively, earn higher profits, and face reduced informational disadvantages at market opening. Our findings highlight that retail investor learning during non-trading hours levels the informational playing field among heterogeneous investors and improves price quality around irregularly timed macroeconomic announcements. These results have broader implications for emerging markets, which similarly feature irregular announcement timing and large populations of uninformed retail investors.

Contrary to the U.S., Chinese stock markets exhibit negative overnight returns, which further decrease with information asymmetry. We demonstrate that China’s "T+1" trading rule, which prohibits same-day selling, exacerbates adverse selection for uninformed buyers by limiting them to react to post-trade information. Prices are hence initially discounted at opening and recovered by the market close, generating negative overnight returns that are inversely related to information asymmetry risks. Consistent with adverse selection, empirical evidence reveals lower overnight returns during market declines and high-volatility periods, with robust negative associations between overnight returns and information asymmetry proxied by ffrm size, analyst coverage, and earnings announcement proximity. A model is introduced to rationalize our findings. The framework also sheds light on China’s "opening return puzzle", the phenomenon that intraday price rises concentrate predominantly in the initial 30 minutes of trading, by showing how reduced adverse selection enables rapid price recovery during opening session.

This study examines the effect of artificial intelligence (AI) adoption on stock price crash risk using panel data from Chinese A-share listed firms from 2001 to 2022. We find that higher levels of AI application significantly reduce crash risk, primarily by enhancing information transparency, easing financial constraints, and promoting innovation. Notably, AI improves transparency within supply chains by reducing information asymmetry between upstream and downstream firms, thereby enhancing information flow and reducing market frictions. Among AI types, machine learning proves most effective in lowering crash risk due to its data-processing and forecasting capabilities, while natural language processing and computer vision show weaker effects. The impact of AI is particularly pronounced in non-government-regulated industries and high-tech firms. Moreover, its risk-mitigating effect becomes increasingly significant over time. These results are robust to instrumental variable estimation and staggered difference-in-differences (DID) designs. These findings highlight the strategic role of AI in risk management and offer practical implications for firms and policymakers aiming to enhance transparency, financial resilience, and long-term value creation.

Strengthening disclosure regulation and improving disclosure quality are central to China's transition to a full registration system and crucial for preventing capital market risks. Using prospectuses disclosed by IPOs on the STAR Market, ChiNext, and the Beijing Stock Exchange from 2019 to 2023, this study constructs four textual indicators from prospectuses—length, sentence complexity, technical term density, and uncertainty—and examines how they affect resource allocation efficiency under the registration system. We find that text length and sentence complexity improve resource allocation efficiency, consistent with an information effectiveness effect. In contrast, technical term density and uncertainty reduce efficiency, reflecting information redundancy. Further analysis shows that the registration system reform enhances the comprehensiveness and complexity of disclosures, but its net effect on efficiency depends on the balance between information effectiveness and redundancy. This study contributes to the international literature on “institutional environment—disclosure—resource allocation” with evidence from an emerging market, while also extending theories of information asymmetry and impression management. Our findings support Chinese regulators in optimizing prospectus standards and strengthening review oversight, and provide policy insights for other emerging markets seeking to improve capital allocation through more effective disclosure design.

This study examines the role of negative peer events, specifically initial bond defaults, in driving leverage manipulation of non-defaulting firms within the same region. Controlling for firm-specific time-varying characteristics, we find that initial bond defaults within a province are associated with an increase in leverage manipulation among non-defaulting firms. Two potential mechanisms underlying this relationship include increased financial constraints for these firms and elevated investor risk perception of the local bond market. The positive impact of bond defaults on leverage manipulation is more pronounced for financially constrained firms, firms with severe information asymmetry, and those affected by high-rated bond and principal defaults. We further show that companies that manipulate their debt ratios experience higher default risk. Our findings have important implications for transparent disclosure and highlight the negative effect of regional bond defaults on corporate financial reporting practices.

We compile a Chinese counterpart to the TRACE dataset and provide the first trade-level analysis of China’s wholesale corporate bond market—the second largest in the world. In contrast to the dealer-dominated, core–periphery networks typical of over-the-counter markets in developed economies, China’s corporate bond market shows limited dealer intermediation. Designated dealers are reluctant to intermediate trades,and non-dealers supply the majority of liquidity, leading to wide price dispersion and low trading activity. This weak dealer participation is not driven by information asymmetry but stems from balance sheet constraints among smaller dealers and large state-owned banks’ privileged access to profitable lending opportunities.

This study investigates the impact of RegTech adoption in the M&A regulatory review process on deal performance. Leveraging the staggered implementation of the SOEs Online Supervision System (SOSS) by China’s State-Owned Assets Supervision and Administration Commission (SASAC) across its central and 31 provincial offices from 2018 to 2021, we find that SOSS directly enhances SASAC’s decision-making efficiency and improves its capacity to screen and approve higher-quality M&A deals. More importantly, SOE-led M&A transactions exhibit higher announcement returns as well as improved long-run stock and operating performance following the system’s implementation. The positive impact of SOSS is more pronounced for acquirers with stronger technological infrastructure, in transactions characterized by low transparency and weak governance, and in provinces with more stringent external scrutiny. Overall, by addressing regulator-firm information asymmetry and reinforcing managerial accountability, SOSS improves regulatory effectiveness in overseeing major investment activities among SOEs.

Contrary to the US, Chinese stock markets exhibit negative overnight returns that appear to be highly affected by the extent of information asymmetry. China's "T+1" trading rule, which prohibits same-day selling, exacerbates adverse selection for uninformed buyers by limiting them to react to post-trade information. An information asymmetry-driven price discount thus emerges at market open, generating negative overnight returns, which further decrease with information asymmetry. Consistent with adverse selection, empirical evidence reveals lower overnight returns during market declines and high-volatility periods, with robust negative relationship between overnight returns and information asymmetry proxied by firm size, analyst coverage, and earnings announcement proximity. A model is introduced to rationalize our findings. This framework also sheds light on China's "opening return puzzle", the phenomenon that prices rise rapidly in the initial 30 minutes of trading, by showing how reduced adverse selection enables rapid price recovery during opening session.

We conducted a nationwide field experiment with 4,800+ Chinese-listed companies, randomly raising ESG concerns to their management teams via high-visibility and high-stakes online platforms. Tracking the full impact-generating process, we find that companies respond to our concerns by providing high-quality answers, publishing ESG reports, and making commitments to investors. Over time, Environmental (E) inquiries boost stock valuations, while Governance (G) concerns prompt skepticism. Productive and opaque firms are more likely to respond, consistent with a signaling model where costly ESG actions signal firm quality under information asymmetry. Overall, ESG actions are likely driven by profit-oriented signaling rather than values-based motives.