Cross-firm momentum effects via shared analyst coverage are well-documented in de-veloped markets, but their robustness remains unclear in emerging markets, where information diffusion is asymmetric and analyst coverage is highly concentrated. Our work revisits this effect in an environment of extreme informational frictions — the Chinese market. We reconstruct the information transmission channel within the an-alyst coverage network by introducing a novel weighting scheme based on strength centrality (SC). This measure identiffes inffuential leader firms that command dis-proportionate attention from both analysts and the market. Our results demonstrate that SC-weighted connected-firm returns robustly predict cross-sectional stock returns, yielding significant and persistent profits even under a rigorous stock filter. This per-formance cannot be subsumed by strategies based on alternative weighting schemes or by explanations such as intra-industry cross-firm momentum and information discreteness. Further analysis reveals that the superiority of the SC-based approach stems from its ability to effectively identify firms with stronger cross-period fundamental linkages. In addition, high-SC stocks are characterized by higher investor attention, more efficient information processing, lower arbitrage costs, and greater internationa exposures. With this evidence, we further confirm a directional spillover: cross-firm momentum effects flow exclusively from these high-SC leaders to low-SC laggards, and there is no reverse spillover. Our findings suggest that cross-firm momentum may be systematically underestimated in many international markets due to methodological limitations rather than economic irrelevance. The SC-based framework therefore of-fers a portable tool for global investors and researchers operating in environments with asymmetric information.

This study investigates the mean-reversion behavior of trading volume, using China’s A-share market as a representative setting characterized by dispersed retail investors, frequent public disclosures, and active policy interventions. We compare two competing interpretations:the stealth-trading hypothesis, in which persistent volume reflects order-splitting by informed investors, and the informational efficiency hypothesis, which links faster volume reversion to more effective information processing. Using the Ornstein–Uhlenbeck (OU) model, we estimate reversion speeds for over 3,000 stocks and relate these to firm- and industry-level characteristics. We find that trading volume is broadly mean-reverting, with over 98% of stocks exhibiting stationarity. The OU model forecasts reversion speed with less than 7% error. Faster reversion is associated with larger firm size, greater analyst coverage, lower volatility, and higher liquidity. Notably, reversion speed increased after accounting reforms but declined following capital access liberalization, suggesting that regulatory policy can both enhance and impair informational efficiency. These findings position reversion speed as an observable proxy for market responsiveness and highlight trading volume as a central variable in empirical market microstructure research.

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.

Against the backdrop of recurrent global crises, economic uncertainty, and mounting environmental and social pressures, corporate resilience—defined as a firm’s capability to withstand external systemic shocks—has emerged as a critical determinant of long-term sustainability. This study empirically exames the effect of ESG (Environmental, Social, and Governance) performance on corporate resilience in China’s A-share market, using the COVID-19 pandemic as a natural experiment to identify causal effects. The sample comprises 651 A-share listed firms, excluding financial institutions, real estate firms, and ST/*ST companies, over the period from January 20, 2020, when the pandemic was officially announced in China, to June 30, 2024. ESG performance is measured as the average of 2018–2019 ratings issued by three major domestic agencies, thereby capturing firms’ pre-shock conditions and mitigating concerns of reverse causality. Corporate resilience is evaluated along two dimensions: resistance, measured by the severity of losses in net income, revenue, and stock price, and recovery, measured by the time required for ROA, EBIT, stock price, and Tobin’s Q to return to pre-shock levels. To ensure the robustness of the findings, this study employs linear regression models with industry-clustered robust standard errors, an instrumental-variable approach using R&D intensity and analyst coverage as instruments, and a Cox accelerated failure time model to estimate recovery duration. The empirical results indicate that stronger pre-shock ESG performance significantly enhances corporate resistance and shortens recovery time. Mechanism analyses further reveal that ESG strengthens corporate resilience by improving total factor productivity, alleviating financing constraints, and enhancing corporate reputation. These findings remain robust to multicollinearity diagnostics and a range of additional robustness tests. Overall, this study provides empirical evidence of the value of ESG in strengthening corporate resilience and offers important implications for firms, policymakers, and investors.

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.

Between high-quality and high-efficiency green innovation, which can truly reduce stock price crash risk? We use data from Chinese listed companies from 2010 to 2022 to study the impact mechanism and effect of radical and incremental green innovation stock price crash risk. Results show that radical green innovation can significantly reduce stock price crash risk, and this effect is more evident than the incremental one. Radical green innovation can improve information efficiency and enhance risk management, thus reducing stock price crash risk. Besides, among companies held by trading institutions and with low analyst coverage, the inhibitory effect is more evident.

This study investigates the impact of analysts' information source diversity on forecast bias and investment returns. We combine the GPT-4o model and text similarity, to extract the names of information sources from the text of analyst in-depth reports. Using 349,200 sources, we calculate information diversity scores based on the variety of data sources to measure analysts’ ability of selecting relevant information. The findings reveal that higher information diversity significantly reduces forecast bias and enhances portfolio returns. The effect is particularly pronounced for large companies, state-owned enterprises, those with low analyst coverage, low firm-specific experience, and reports with positive forecast revisions. Institutional investors recognize the value of this skill, while retail investors remain largely unaware, which contributes to financial inequality. This study highlights the critical role of information diversity in analyst performance.

This study examines how classified boards affect workplace safety, an important dimension of employee welfare. Using comprehensive establishment-level injury data from the U.S. Occupational Safety and Health Administration and a novel classified board database, we document that firms with classified boards experience 12-13% lower workplace injury rates. To establish causality, we employ instrumental variable and difference-in-differences approaches exploiting staggered board declassifications. The safety benefits of classified boards operate through increased safety expenditures, reduced employee workloads, and enhanced external monitoring through analyst coverage. These effects are strongest in financially constrained firms and those with weaker monitoring mechanisms. Our findings support the bonding hypothesis that anti-takeover provisions facilitate long-term value creation by protecting stakeholder relationships and provide novel evidence that classified boards benefit rank-and-file employees, not just executives and major customers. The results reveal an important mechanism through which governance structures impact employee welfare and challenge the conventional view that classified boards primarily serve managerial entrenchment.

This study explores the proclivity of institutional investors in China towards highESG stocks amidst the growth of ESG investment funds. Using A-share data from 2015-2022 and a Tobit model analysis, it is found that these investors indeed favor such stocks, particularly under extensive analyst coverage and in non-state-owned firms. However, rating discrepancies can impact this preference. The attraction lies in reduced operational risks and improved net profits. Notably, independent investors show a stronger ESG preference, especially within high-pollution industries. Thus, fostering ESG investment among institutional investors can improve resource allocation in China's capital market, favoring eco-friendly companies.

This paper models the effect of corporate information preference on stock return volatility based on optimization problems of information decisions for firms and investors. Our model hypothesizes a positive correlation between corporate information preference and volatility. Utilizing the ideal institutional background of the Chinese stock market, we empirically confirm that corporate information preference has a positive impact on volatility, particularly for firms facing more severe financial distress, limited investor attention, and fewer analyst coverage. Our study provides a new perspective for analyzing the interaction between information supply and asset price dynamics.