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.

This paper analyzes the impact of risk disclosures in bond prospectuses on the credit risk premium in the Chinese corporate bond market through six textual characteristics comprehensively. In the empirical analysis, the collected 5199 bond prospectuses and structured data concerning control variables from 2006 to 2021 are used to perform the fixed effect regression analysis. The results show that fewer Words, less Boilerplate, higher Fog Index, more HardInfoMix, more Redundancy, and higher Specificity of risk disclosures in bond prospectuses will lead to a higher credit risk premium. Further tests demonstrate that ceteris paribus, the negative impact of Words and Boilerplate will be strengthened by implicit government guarantees carried by a state-owned enterprise but be weakened by better corporate business performance. However, ceteris paribus, positive effects of the Fog Index, HardInfoMix, Redundancy, and Specificity will be weakened when the bond issuer is state-owned but be strengthened by better corporate business performance.

This paper investigates whether and how managers of highly polluting firms adjust their earnings forecast behaviors in response to the introduction of environmental legal institutions. Using the establishment of environmental courts in China as a quasi-natural experiment, our triple difference-in-differences (DID) estimation shows that environmental courts significantly increase the likelihood of management earnings forecasts for highly polluting firms compared to non-highly polluting firms. This association becomes more pronounced for firms with stronger monitoring power, higher environmental litigation risk, and greater earnings uncertainty. Additionally, we show that highly polluting firms improve the precision and accuracy of earnings forecasts following the establishment of environmental courts. Furthermore, we provide evidence that our results do not support the opportunistic perspective that managers strategically issue more positive earnings forecasts to inflate stakeholders‘ expectations subsequent to the implementation of environmental courts. Overall, our research indicates that environmental legal institutions make firms with greater environmental concerns to provide more forward-looking information, thereby alleviating stakeholders’ apprehensions regarding future profitability prospects.

We examine whether and how mandatory corporate social responsibility (CSR) disclosures affect corporate tax avoidance. Using a CSR disclosure mandate in China that requires a subset of firms to disclose their CSR activities as an exogenous shock to CSR disclosures, our difference-in-differences analyses show that firms affected by the disclosure mandate engage in less tax avoidance relative to control firms. Additional analyses indicate that increased public scrutiny following the disclosure mandate is the likely channel through which mandatory CSR disclosures constrain tax avoidance. Cross-sectional analyses suggest that the effect of the disclosure mandate varies with institutional environments. Overall, our results indicate that the CSR disclosure mandate constrains corporate tax avoidance, which is consistent with mandatory CSR disclosures nudging firms toward more socially desirable behavior.

The healthy operation of the stock market relies on sound liquidity. We utilize the semantic information from disclosure texts of listed companies on the China Science and Technology Innovation Board (STAR Market) to construct a daily corporate network. Through empirical tests and performance analyses of machine learning models, we elucidate the relationship between the similarity of company disclosure text contents and the temporal and spatial correlations of stock liquidity. Our liquidity indicators encompass trading costs, market depth, trading speed, and price impact, recognized across four dimensions. Furthermore, we reveal that the information loss caused by employing Minimum Spanning Tree (MST) topology significantly affects the explanatory power of network topology indicators for stock liquidity, with a more pronounced impact observed at the document level. Subsequently, by establishing a neural network model to predict next-day liquidity indicators, we demonstrate the temporal relationship of stock liquidity. We model a liquidity predicting task and train a daily liquidity prediction model incorporating Graph Convolutional Network (GCN) modules to solve it. Compared to models with the same parameter structure containing only fully connected layers, the GCN prediction model, which leverages company network structure information, exhibits stronger performance and faster convergence. We provide new insights for research on company disclosure and capital market liquidity.

Using unique account-level data from a leading Chinese fintech platform, we investigate how financial influencers, the key information intermediaries in social finance, attract followers through a process of social transmission bias. We document a robust performance-following pattern wherein retail investors overextrapolate influencers’ past returns rather than rational learning in the social network from their past performance. The transmission bias is amplified by two mechanisms: (1) influencers’ active social engagement and (2) their index fund-heavy portfolios. Evidence further reveals influencers’self-enhancing reporting through selective performance disclosure. Crucially, the dynamics ultimately increase risk exposure and impair returns for follower investors.

We construct a novel risk-based similarity peer network by applying machine learning techniques to extract a comprehensive set of disclosed risk factors from firms' annual reports. We find that a firm's future returns can be significantly predicted by the past returns of its risk-similar peers, even after excluding firms within the same industry. A long-short portfolio, formed based on the returns of these risk-similar peers, generates an alpha of 84 basis points per month. This return predictability is particularly pronounced for negative-return stocks and those with limited investor attention, suggesting that the effect is driven by slow information diffusion across firms with similar risk exposures. Our findings highlight that the risk factors disclosed in 10-K filings contain valuable information that is often overlooked by investors.

How much value has been lost in the Chinese bankruptcy system due to excessive liquidation of companies whose going concern value is greater than the liquidation value? I compile new judiciary bankruptcy auction data covering all bankruptcy asset sales from 2017 to 2022 in China. I estimate the valuation of the asset for both the final buyer and creditor through the revealed preference method using an auction model. On average, excessive liquidation results in a 13.5% welfare loss. However, solely considering the liquidation process, an 8% welfare gain is derived from selling the asset without transferring it to the creditors. Firms that are (1) larger in total asset size, (2) have less information disclosure, (3) have less access to the financial market, and (4) possess a higher fraction of intangible assets are more vulnerable to such welfare loss. Overall, this paper suggests that policies promoting bankruptcy reorganization by introducing distressed investors who target larger bankruptcy firms suffering more from information asymmetry will significantly enhance welfare in the Chinese bankruptcy market.

This study explores how Chinese mutual fund managers’ degrees of disagreement (DOD) on stock market returns affect investor capital allocation decisions using a novel text-based measure of expectations in fund disclosures. In the time series, the DOD neg-atively predicts market returns. Cross-sectional results show that investors correctly perceive the DOD as an overpricing signal and discount fund performance accordingly. Flow-performance sensitivity (FPS) is diminished during high dispersion periods. The ef-fect is stronger for outperforming funds and funds with substantial investments in bubble and high-beta stocks, but weaker for skilled funds. We also discuss ffnancial sophisti-cation of investors and provide evidence that our results are not contingent upon such sophistication.