We construct the most comprehensive bankruptcy database of Chinese firms to date and document significant real effects arising from the establishment of specialized bankruptcy courts. Specifically, the recovery rate for unsecured creditors increases by 38.6 percentage points after the reform. This improvement is not driven by shorter case durations or lower direct bankruptcy costs, as intuition might suggest. Instead, it results primarily from greater efficiency in the discovery and disposal of assets during bankruptcy proceedings. The reform also increases the likelihood of reorganization and promotes capital infusion in such cases. Higher recovery rates generate broader spillovers: reductions in non-performing loans, expansion of unsecured lending by local banks, relaxation of firms’ financial constraints, shifts in capital structure and investment, and greater public willingness to file for bankruptcy when distressed.

This paper studies the economic impact of introducing a personal bankruptcy regime, using China’s recent pilot reforms as a natural experiment. We exploit the staggered rollout of personal bankruptcy frameworks across Chinese cities and construct a novel dataset of bankruptcy case filings, combined with survey-based measures of credit access and official firm registration records. Our difference-in-differences estimates show that the reforms significantly improve small business credit access - business loan take-up increases by 1.3 % (21% relative to pre-reform mean), with no offsetting rise in interest rates. Effects are concentrated among non-corporate firms, firms with less employees and female entrepreneurs. Moreover, the reform is also associated with a 9.7% increase in new firm registrations. To interpret these findings, we develop a simple but novel theoretical model in which personal bankruptcy reduces the downside risk of entrepreneurial failure while preserving creditor recoveries. These findings underscore how debtor protection policies, when designed to reduce enforcement costs without expanding exemption rights, can enhance credit supply and entrepreneurial activity.

We examine the effects of issuing green bond on green premium and green signal transmission by matching green bonds with ordinary bonds. We find that the credit spread of green bonds is significantly lower than that of ordinary bonds, especially for those green bonds with lower information disclosure complexity. Besides, issuing green bonds cannot receive a positive response from the stock market, but can significantly reduce issuer’s loan costs and provide more financial subsidies for high polluting issuers. Furthermore, by obtaining discounted loans and financial subsidies, issuing green bonds can increase issuer’s R&D intensity and reduce their carbon emissions. These findings indicate that issuing green bonds can reduce financing costs and convey green signals to market stakeholders with less investment experience.

This study employs an imbalanced panel dataset of 524 Chinese commercial banks from 2009 to 2020 to investigate the influence of Basel III on banks' loan loss provisions. Our findings reveal no significant change in the relationship between loan loss provisions and capital adequacy, although it indicates a heightened impetus for Tier 1 capital management. Furthermore, the study finds that earnings management motivations, particularly related to pre-provision profits, influence banks' loan loss provisions. Basel III's enactment reduces the ability of high-earning banks to manipulate earnings using loan loss provisions. This research provides empirical evidence from China for the global assessment of Basel III's impact on commercial banks.

We use a sample of China’s listed firms and employ a naïve Bayesian machine learning algorithm to reveal that environmental information intangibility superficially promotes green innovation. We demonstrate that this effect is channelled through the acquisition of institutional resources, including bank loans and government subsidies. The impact of environmental information intangibility on green innovation is most pronounced within state-owned enterprises, large firms, and politically connected firms. Furthermore, we confirm that environmental information intangibility does not lead to improvements in innovation efficiency or quality. This implies that green innovation may serve as a symbolic environmental activity. Our findings contribute to the understanding of the consequences of environmental information intangibility, greenwashing behaviour, and their relationship to green innovation.

We investigate the effect of third-party certification for green bonds by analyzing its impact on issuer's future green innovation performances. We find that third-party certification for green bonds can significantly promote issuer's future green innovation performances. Furthermore, the promotion effect is more prominent in non-state-owned issuers, large issuers and heavy polluting issuers, and can be more significantly exerted by professional and reputable third-party certification agencies. Besides, third-party certification for green bonds can play the effect by reducing the issuer's tax expenditure, increasing the issuer's loan financing, and receiving a positive response in stock returns. But unexpectedly, it cannot play the effect by further reducing the credit spread of green bonds. Our findings indicate that independent external supervision can play a positive role in green bond issuance, but there is still a long way to go.

Banks have a significant funding-cost advantage if their liabilities are protected by bailout guarantees. We construct a corporate finance-style model showing that banks can exploit this funding-cost advantage by just intermediating funds between investors and ultimate borrowers, thereby earning the spread between their reduced funding rate and the competitive market rate. This mechanism leads to a crowding-out of direct market finance and real effects for bank borrowers at the intensive margin: banks protected by bailout guarantees induce their borrowers to leverage excessively, to overinvest, and to conduct inferior high-risk projects. We confirm our model predictions using U.S. panel data, exploiting exogenous changes in banks' political connections, which cause variation in bailout expectations. At the bank level, we find that higher bailout probabilities are associated with more wholesale debt funding and lending. Controlling for loan demand, we confirm this effect on bank lending at the bank-firm level and find evidence on loan pricing consistent with a shift towards riskier borrower real investments. Finally, at the firm level, we find that firms linked to banks that experience an expansion in their bailout guarantees show an increase in their leverage, higher investment levels with indications of overinvestment, and lower productivity.

A unique natural experiment in China – the city-level staggered introduction of admin-istrative approval centers (AAC) – reduces bank loan processing times by substantially speeding up the process of registering collateral without affecting credit decisions. Fol-lowing the establishment of an AAC, firms significantly reduce their cash holdings. State-owned enterprises are less affected. Cash flow sensitivity of cash holdings de-creases, as does the cash flow sensitivity of investment. The share of short-term debt increases, while inventory holdings and reliance on trade credit decrease. Defaults also decrease. These results suggest that timely access to credit has important implications on firms’ financial management.

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.

Enterprise innovation is a hot topic in current academic research. Taking the demonstration city of social credit system construction implemented in China as a quasi-natural experiment, this paper investigates whether the construction of social credit system can improve enterprise innovation. The study finds that the construction of social credit system effectively enhances enterprise innovation. Mechanism test shows that the construction of social credit system escalates the scale and duration of enterprise loans, thereby fostering enterprise innovation. These findings present insights that the pivotal role of informal institutions, such as the social credit system, in facilitating the upgrading of industrial structures and augmenting the quality of economic development.