Using a sample of 501 Chinese non-financial firms listed on the Hong Kong Stock Exchange from 2008 to 2020, we find that both foreign exchange (FX) derivatives and foreign currency (FC) debt significantly reduce firms’ probability of default. We further observe that larger, non-state-owned enterprises (SOEs), Hong Kong-headquartered firms, firms operating after China’s 2015 exchange rate reform and firms under high trade policy uncertainty (TPU) are more likely to use both FX derivatives and FC debt concurrently, thereby diversifying their strategies for managing default risk. Our analysis indicates that these tools reduce firms’ default risk primarily by improving firms’ profitability, raising their likelihood of obtaining credit ratings, and increasing their use of interest rate derivatives. Importantly, we reveal that FX derivatives and FC debt act as substitutes in mitigating firms’ default risk. Notably, this substitution effect is more pronounced for larger, non-SOEs, Hong Kong-headquartered firms, firms operating after exchange rate reform and firms facing high TPU. Finally, we find that using FX derivatives significantly dampens firms’ investment, which may explain why Chinese firms tend to prefer FC debt to manage their default risk.

This paper investigates the causal impact of pre-trade transparency on the market liquidity of an over-the-counter-style market by leveraging a natural experiment in China’s interbank corporate bond market. We find that turnover, market liquidity, and aggregate bond returns significantly declined when the regulators unexpectedly suspended real-time quote dissemination in March 2023. Consistent with our expectation, these effects were mainly focused on interbank bonds, not exchange bonds, and bonds with lower credit ratings and longer maturities. This study contributes novel evidence to the transparency literature and provides insights for policymakers in emerging markets weighing the trade-offs between data governance and market efficiency.

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.

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.

We investigate the impact of government environmental credit ratings on bond credit spreads based on a sample of Chinese A-share listed companies from 2014 to 2022. Empirical results demonstrate that a favourable environmental credit rating significantly reduces bond credit spreads, highlighting the incentivising effect of environmental credit ratings. Mechanism testing reveals that a good environmental credit rating diminishes information asymmetry and enhances an enterprise’s resource acquisition capabilities, reducing bond credit spreads. Furthermore, subgroup analyses suggest the reduction effect is more pronounced in enterprises with low debt and tax credit ratings.

We investigate the impact of government environmental credit ratings on bond credit spreads based on a sample of Chinese A-share listed companies from 2014 to 2022. Empirical results demonstrate that a favourable environmental credit rating significantly reduces bond credit spreads, highlighting the incentivising effect of environmental credit ratings. Mechanism testing reveals that a good environmental credit rating diminishes information asymmetry and enhances an enterprise’s resource acquisition capabilities, reducing bond credit spreads. Furthermore, subgroup analyses suggest the reduction effect is more pronounced in enterprises with low debt and tax credit ratings.

There is increasing attention on information transfers along supply chain partners for firm (extreme) events. This growing literature finds spillover effects following certain types of firm events. Using data from credit rating actions of Chineselisted firms over the period between March 2007 and May 2020, we examine the spillover effects of supply chains by focusing on the market reactions of event firms to the action announcements. We find strong evidence of spillover effects driven by the market reactions of event firms, which are enhanced through information diffusion channels as supply chain partners receive more investor attention. Moreover, the effects are stronger when event firms’ market reactions are negative, event firms are nonstated-owned, the industry concentration of event firms is higher, or the suppliercustomer business relationship is closer. Overall, these findings highlight the role of investor attention in addition to network characteristics in supply chain spillovers.

This paper creates default-probability-(PD)-implied credit ratings for Chinese firms following the S&P global rating standard. The domestic credit rating agency (DCRA) ratings are higher than the PD-implied ratings by ten notches on average for the identical level of default risk, implying that the domestic ratings are significantly inflated. The PD-implied ratings outperform the DCRA ratings in detecting defaults and complement the latter in predicting yield spreads. The PD-implied ratings draw information from operating efficiency-related variables; in contrast, the DCRA ratings pay attention to scale-based firm characteristics in credit risk assessment.

Using a difference-in-differences (DID) approach, this research assesses the effect of a firm’s credit rating issued by domestic rating agencies on stock price crash risk (SPCR). The results show that SPCR for treated firms decreases by 11% after firm ratings, suggesting that they can aggravate information content at the firm level. The effect is consistently more evident when stock price synchronization is higher and is stronger in firms with low media coverage, in firms with low audit quality, in state-controlled firms, and in firms with low investor protection. In addition, during a bear market year, the quality of firm ratings is higher. Overall, our findings support that investors could gain more information via firm ratings issued by credit rating agencies. Through our research, policymakers and investors can pay more attention to firm ratings that help play the information intermediary role of credit rating agencies.

The goal of this paper is to discuss how we establish the “Hammer (CAFÉ) Credit System” by applying Gibbs sampling algorithm under the framework of bigdata approach to extract features in depicting bad or illegal behaviors by following the “five step principle” applying international credit rating standards. In particular, we will show that our Hammer (CAFÉ) Credit System is able to resolve three problems of the current credit rating market in China which rate: “1) the rating is falsely high; 2) the differentiation of credit rating grades is insufficient; and 3) the poor performance of predicting early warning and related issues”. In addition the Hammer (CAFÉ) credit is supported by clearly defining the "BBB" as the basic investment level with annualized rate of default probability in accordance with international standards in the practice of financial industries, and the credit transition matrix for “AAA-A” to “CCC-C” credit grades.