We examine the pricing ofsystematic information asymmetry, induced by Chinese gov-ernment intervention, in the cross-section of stock returns. Using market-wide order im-balance as a proxy for systematic information, we observe a strong correlation betweenthe standard deviation of market-wide order imbalance and economic policy uncertainty.Furthermore, we find a significant positive relationship between the sensitivity of stocks tosystematic information asymmetry (OIBeta) and their future returns. The average monthlyreturn spread between high- and low-OIBeta portfolios ranges from 1.30% to 1.77%, andthis result remains robust after controlling for traditional risk factors. Our results providesubstantial evidence that the pricing of OIBeta is driven by systematic information asym-metry rather than alternative explanatory channels.

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.

This paper examines how the integration of artificial intelligence (AI) and big data affects banking operations, emphasizing the crucial role of big data in unlocking the full potential of AI. Leveraging a comprehensive dataset of over 4.5 million loans issued by a leading commercial bank in China and exploiting a policy mandate as an exogenous shock, we document significant improvements in credit rating accuracy and loan performance, particularly for SMEs. Specifically, the adoption of AI and big data reduces the rate of unclassified credit ratings by 40.1% and decreases loan default rates by 29.6%. Analyzing the bank's phased implementation, we find that integrating big data analytics substantially enhances the effectiveness of AI models. We further identify significant heterogeneity: improvements are especially pronounced for unsecured and short-term loans, borrowers with incomplete financial records, first-time borrowers, long-distance borrowers, and firms located in economically underdeveloped or linguistically diverse regions. Our findings underscore the powerful synergy between big data and AI, demonstrating their joint capability to alleviate information frictions and enhance credit allocation efficiency.

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.

We investigate the impact of financial geographic density on corporate financial asset holdings in emerging market. We proxy for financial geographic density by calculating the number of financial institutions around a firm within a certain radius based on the geographic distance between the firm and financial institutions. Using data on publicly listed A-share firms in China from 2011 to 2021, we find that financial geographic density has a positive impact on nonfinancial firms’ financial asset investments, especially for the firms located in regions with a larger number of banking depository financial institutions or facing greater market competition. An increase in the number of financial institutions surrounding firms increases corporate financial asset holdings by alleviating information asymmetry. Moreover, we document that Fintech has little impact on the relationship between financial geographic density and corporate financial asset holdings. As the rise of financial geographic density, firms hold more financial assets for precautionary motives, which contribute to corporate innovation.

收益率非对称性定价是金融研究中长期存在争议的重要问题。本文创新性地提出了基于概率分布、反映收益率整体非对称性的新测度（Return Asymmetry, RA），首次为该争议提供了跨市场的系统性证据。研究发现：首先，RA测度在中、美等主要市场均能负向预测股票横截面收益率，其解释力较传统测度显著提升；其次，RA的定价优势源于其对收益率复杂分布信息的更全面捕捉，特别是能有效识别系统与特质非对称之间的交互效应；最后，通过博彩偏好、投资者情绪、关注度和套利限制等多维度渠道分析，证实行为因素是驱动收益率非对称性定价的核心机制。本研究不仅有助于弥合学术分歧，更建立了具有全球适用性的非对称定价分析范式。

This paper investigates whether increased female representation on corporate boards affects firms’ bond financing costs. Exploiting the 2017 Big Three’s campaigns as a plausibly exogenous shock, we document that firms experiencing larger increases in female board representation, induced by the campaigns, experience significant reductions in bond yield spreads and improvements in credit ratings. We identify reduced leverage and enhanced workplace environment as key mechanisms, and show that the effects are stronger among firms with greater tail risk and information asymmetry. An alternative identification strategy based on California’s SB 826 regulatory mandate yields consistent results. Our findings suggest that board gender diversity enhances governance in ways valued by credit markets.

This study empirically investigates the impact of supply chain network centrality on corporate greenwashing behavior. By constructing supply chain networks of Chinese A-share listed companies, we find a strong positive correlation between supply chain network centrality and corporate greenwashing behavior, with an increase of approximately 6.20%. The paper identifies the underlying mechanism as the contagion of the green bandwagon effect within the supply chain, which is observed specifically in the downstream network, particularly among corporate-customers. Additionally, we observe that the positive effects are more pronounced in companies with lower information asymmetry, as well as in labor- and capital-intensive industries and regions with disadvantaged economic conditions. These findings offer important insights for improving corporate environmental responsibility and curbing greenwashing practices.

Individual institutions marginal contributions to the systemic risk contain predictive power for its potential future exposure and provide early warning signals to regulators and the public. We use higher-order co-skewness and co-kurtosis to construct systemic risk contribution measures, which allow us to identify and characterize the co-movement driving the asymmetry and tail behavior of the joint distribution of asset returns. We illustrate the usefulness of higher-order moment augmented approach by using 4868 stocks living in the Chinese market from June 2002 to March 2022. The empirical results show that these higher-order moment measures convey useful information for systemic risk contribution measurement and portfolio selection, complementary to the information extracted from a standard principal components analysis.

Based on institutions' general role and the specialty of motivated investors' relatively larger stake, we examine whether ownership by motivated investors is associated with the focal firm's ESG rating disagreement in China. Our results suggest that ownership by motivated investors can decrease the focal firm's ESG rating disagreement. That relationship is strengthened by a better internal or external information environment. What's more, ownership by motivated investors can increase the quality of ESG disclosure and the level of consensus ESG rating. ESG rating disagreement increases stock return volatility and price synchronicity, while motivated investors can mitigate those negative effects. Our results confirm that motivated investors have greater incentive and capability to discipline managers and influence corporate policies and actions even in an emerging market with weak investor protection and the popularity of exploration by ultimate controllers. That would shed valuable insights into the high-quality development of other emerging markets, especially those in south-east Asian.