In this study, we investigate whether and how risk disclosure in peer firms’ management discussion and analysis (MD&A) influences analyst earnings forecast accuracy. We find that peer MD&A risk disclosure significantly improves forecast accuracy, demonstrating a positive spillover effect. Moreover, the impact of peer MD&A risk disclosure on analysts’ forecast accuracy strengthens with the comparability and reliability of peer firms’ information, while weakens with the disclosure quality of the focal firm. Finally, peer MD&A risk disclosure also reduces stock price crash risk, providing further evidence that it improves information environment of the focal firm.

Previous studies on the impact of corporate information disclosure on stock price crash risk have largely focused on financial statements. In contrast, China’s unique monthly operating report disclosure system—featuring high frequency and realtime operational data—offers a distinct information channel. Using data from A-share listed firms from 2010 to 2021, we find that monthly operating report disclosures significantly reduce stock price crash risk by alleviating information asymmetry between firms and external stakeholders. The underlying mechanisms involve restraining managerial opportunism and correcting investor expectation biases. Further analysis shows that firms’ official responses to investor inquiries has no significant effect on crash risk once monthly operational disclosures are accounted for, underscoring that the quality of information disclosed is as important as its frequency. The risk-reducing effect is more pronounced among firms with greater business complexity, weaker internal controls, and lower institutional ownership.

We investigate the relationship between the intensity of intraday return reversals and future stock returns in the Chinese stock market. We find that a high frequency of positive overnight returns followed by negative daytime returns predicts one-month ahead returns positively. The analysis shows that daytime retail investors tend to overly sell their own rising stocks at market open, accepting lower stock prices in exchange for liquidity. As the price pressure attenuates, these stocks experience subsequent price increases, implying a positive relationship between return reversals and future returns.

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 paper examines the impact of ESG rating divergence on stock price delays in the context of the Chinese capital market. We find that ESG rating divergence significantly increases the stock price delays. Mechanism analysis results suggest that ESG rating divergence affects stock price delays by reducing information transparency and firm internal control quality. Heterogeneous analysis results indicate that the impact of ESG rating divergence on stock price delays is more pronounced in high-tech firms and when investor sentiment is high.

The stock price of a firm is dynamically influenced by its own factors as well as those of its peers. In this study, we introduce a Graph Attention Network (GAT) integrated with WaveNet architecture—termed the GAT-WaveNet model—to capture both time-series and spatial dependencies for forecasting the stock price crash risk of Chinese listed firms from 2012 to 2021. Utilizing node-rolling techniques to prevent overfitting, our results show that the GAT-WaveNet model significantly outperforms traditional machine learning models in prediction accuracy. Moreover, investment portfolios leveraging the GAT-WaveNet model substantially exceed the cumulative returns of those based on other models.

We study how financial markets integrate news about pollution abatement costs into firm values. Using China’s automation of pollution monitoring, we find that firms with factories in bad-news cities---cities that used to report much lower pollution than the automated reading---see significant declines in stock prices. This is consistent with the view that investors expect firms in high-pollution cities to pay significant adjustment and abatement costs to become “greener.” However, the efficiency with which such information is incorporated into prices varies widely---while the market reaction is quick in the Hong Kong stock market, it is considerably delayed in the mainland ones, resulting in a drift. The equity markets expect most of these abatement costs to be paid by private firms and not by state-owned enterprises, and by brown firms and not by green firms.

This paper revisits the default-crash risk relation in the context of China. We find that firms with higher default risk have lower stock price crash risk both in monthly and yearly frequencies. To identify the causal effect, we use the first-ever default event in China’s onshore bond market in 2014 as an exogenous shock to the strength of implicit guarantees. The negative relation arises from the active involvement of the government before 2014 and creditors after 2014 in corporate governance. Consistent with the external scrutiny mechanism, the impact of default risk on stock price crashes is stronger in situations in which creditors are more likely to engage in active monitoring (i.e., firms with higher liquidation costs, lower liquidation value, and higher levels of information asymmetry), with these effects primarily observed in the post-2014 period. Overall, our study highlights the role of the “invisible hand” in the absence of the “visible hand.”

This study highlights asymmetries in liquidity risk pricing from the perspective of heterogeneously informed traders facing changing levels of preference uncertainty. We hypothesize that higher illiquidity premium and liquidity risk betas may arise simultaneously in circumstances where investors are asymmetrically informed about their trading counterparts’ preferences and their financial firms’ timely valuations of assets . We first test the time-varying state transition patterns of IML, a traded liquidity factor of the return premium on illiquid-minus-liquid stocks, using a Markov regime-switching framework. We then investigate how the conditional price of the systematic risk of the IML fluctuate over time subject to changing levels of preference uncertainty. Empirical results from the Chinese stock market support our hypotheses that investors’ sensitivity to the IML systematic risk conditionally increase in times of higher preference uncertainty as proxied by the stock turnover and order imbalance. Further policy impact analyses suggest that China’s market liberalization efforts, contingent upon its recent stock connect and margin trading programs, reduce the conditional price of liquidity risk for affected stocks by helping the incorporation of information into stock prices more efficiently. Tighter macroeconomic funding conditions, on the contrary, conditionally increase the price of liquidity that investors require.

Utilizing a dataset from 2013 to 2022 on China’s listed companies, we explored whether a hedge fund network could help explain the occurrence of Chinese stock crash. First, this study constructs a hedge fund network based on common holdings. Then, from the perspective of network centrality, we examine the effect of hedge fund network on stock crash risk and its mechanism. Our findings show that companies with greater network centrality experience lower stock crash risk. Such results remain valid after alternating measures, using the propensity score matching method, and excluding other network effects. We further document that the centrality of hedge fund network reduces crash risk through three channels: information asymmetry, stock price information content and information delay. In addition, the negative effect of hedge fund network centrality on crash risk is more prominent for non-SOEs firms. In summary, our research shed light on the important role of hedge fund information network in curbing stock crash.