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.

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 investigates whether regional negative public sentiment associated with extreme non-financial social shocks (e.g., violence or crime) will affect the resident firms’ M&A announcement return. Using a sample of 3,200 M&A deals in China, our empirical results consistently show that M&A announcement return is significantly lower after the firm’s headquarter city has experienced negative social shocks. We further find that better CSR performance helps to mitigate the impact of these negative shocks. Overall, we show that firm operations will be largely affected by the resident environment and location, and better CSR performance acts as an effective risk management strategy.

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.

Despite nearly twenty years of privatization, mixed-ownership reform has been the mainstay of SOE reform in China in recent years. This raises the question of whether the financial performance of mixed-ownership firms (Mixed firms) is better than private-owned enterprises (POEs). Although Mixed firms suffer more from government intervention, unclear property rights, and interest conflicts between state shareholders and private shareholders, they can also benefit from the external resources controlled by the state. Therefore, the performance of Mixed firms is still unclear. Collecting data from the Chinese A-share listed market, we divide the firms into POEs, Mixed firms controlled by the state (MixedSOEs), and Mixed firms controlled by the private sectors (MixedPOEs). Measuring profitability using ROA and ROE, we find that on average, POEs perform better than Mixed firms, and MixedPOEs have a higher profitability than MixedSOEs. Within Mixed firms, more state shares are related to lower profitability, and more private shares are related to higher profitability. Using the NBS survey data, we further find that on average, SOEs exhibit the lowest profitability, with MixedSOEs and MixedPOEs in the middle, and POEs have the highest profitability. We try to address the endogeneity challenge in several ways and get similar results. Overall, our analysis provides new evidence on the financial performance of mixed-ownership firms.

A second-tier patent system with relatively low protectability standards has been adopted by many countries, but empirical evidence on how it is used by firms israre. Using Chinese patent data, we exploit “parallel filings” – where a second-tierpatent is filed simultaneously with an invention patent – to shed light on its usein practice. The data indicate that while parallel filings appear to be inventionswith a narrower scope, they are cited more frequently in the early years and morelikely to be licensed or transferred compared to inventions protected by standardpatents. We provide evidence that parallel filing is likely a strategic choice forshort-life-cycle technologies that achieve high value early in their lifetime but decayfast. The rapid issuance of the second-tier patent facilitates knowledge diffusionand technology transfer, thereby helping the patentees capitalize on the value of fast-moving technologies. This study provides some much-needed empirical evidenceon how the quick procedure of the second-tier patent system serves short life-cycletechnologies.

This study investigates the effect of government digital attention on firm digital innovation. Using data from Chinese listed firms over 2012–2020, we find government digital attention can significantly propel the improvement of firms' digital innovation levels, primarily driving an increase in the quantity of digital innovations rather than a qualitative enhancement. Further analysis indicates that government attention achieves this impact by elevating the regional digital infrastructure, increasing firms' digital subsidies, alleviating firms' financing constraints, encouraging firms to intensify R&D investment, fostering a positive attitude towards digital transformation, and consequently, boosting the overall level of firms' digital innovation.

This study investigates the role of financial development in shaping the relationship between FDI and firm innovation, based on Chinese firm-level dataset during 2008-2014. Our findings reveal that bank deregulation significantly enhances the positive effect of FDI on firm innovation. We also find that firms with greater financial constraints and those located in cities with lower levels of bank competition exhibit a more pronounced response. These results underscore the importance of considering financial market conditions and highlight the role of financial constraints and bank competition as crucial channels through which bank deregulation influences the effect of FDI on firm innovation.