The study indicates that under conditions of financial repression, a enterprise’s ESG performance significantly impacts the extent of its short-term debt used for long-term purposes. The mechanism test reveals that ESG performance mitigates the degree of short-term debt for long-term use through three pathways: enhancing information transparency, alleviating financing constraints, and curbing excessive investment. Further research suggests that the influence of ESG performance on the use of short-term debt for long-term purposes is more pronounced among private enterprises, high-pollution and high-energy-consuming enterprises, and enterprises in underdeveloped regions. This paper enriches the research on the relationship between ESG performance and corporate financing decisions.

Purpose – This paper takes the Shanghai-Shenzhen-Hong Kong Stock Connect as a quasi-natural experimentand investigates the impact of capital market liberalization on the corporate debt maturity structure. It also aimsto provide some policy implications for corporate debt financing and further liberalization of the capital marketin China. Design/methodology/approach – Employing the exogenous event of Shanghai-Shenzhen-Hong Kong StockConnect and using the data of Chinese A-share firms from 2010 to 2020, this study constructs a difference-in-differences model to examine the relationship between capital market liberalization and corporate debt maturitystructure. To validate the results, this study performed several robustness tests, including the parallel test, theplacebo test, the Heckman two-stage regression and the propensity score matching. Findings – This paper finds that capital market liberalization has significantly increased the proportion of long-term debt of target firms. Further analyses suggest that the impact of capital market liberalization on thedebt maturity structure is more pronounced for firms with lower management ownership and non-Big 4 audit.Channel tests show that capital market liberalization improves firms’ information environment and curbsself-interested management behavior. Originality/value – This research provides empirical evidence for the consequences of capital marketliberalization and enriches the literature on the determinants of corporate debt maturity structure. Further thisstudy makes a reference for regulators and financial institutions to improve corporate financing through thegovernance role of capital market liberalization.

We examine how the sudden drop in public information search capability caused by Google’s withdrawal from China affects Chinese analysts’ earnings forecasts. We observe, after Google’s withdrawal, a decline in analysts’ forecast accuracy for firms with foreign trade relative to those without it. This decline suggests that the withdrawal hinders analysts’ acquisition of foreign information about firms, which decreases the quality of their earnings forecasts. We also find that the effect of the withdrawal on forecast accuracy is stronger for firms with higher business complexity and more opaque financial reporting and for analysts with weaker information processing capacity and more attention constraints. Additionally, we find that corporate site visits serve as an alternative information source that can compensate for the information loss caused by the Google withdrawal. Last, we document that the withdrawal reduces analysts’ forecast timeliness and increases their forecast dispersion.

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.

Using comprehensive account-level data and 192 asset pricing anomaly signals, we investigate whether retail investors and institutions trade on anomalies in China. We find that retail investors tend to trade contrary to anomaly prescriptions, suggesting that they have a strong tendency to buy (sell) overvalued (undervalued) stocks. In contrast, institutions trade consistent with anomalies, indicating that they buy (sell) undervalued (overvalued) stocks. Regarding the information content of anomalies, we find that small retail investors trade contrary to trading-based anomalies, whereas institutions trade consistent with both trading- and accounting-based anomalies. Additionally, lottery stock preference and return extrapolation help explain investors’ trading behavior on anomalies.

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.

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.

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.