The discussion on the innovative effects of intellectual property pledge financing is a mainstream trend. In this context, this study has improved the existing research from several aspects, such as broadening the dimensions of innovation, adding dynamic analysis, refining multidimensional mediation mechanisms, and employing unique samples. Ultimately, we come to the following conclusions: (1) Intellectual property pledge financing suppresses enterprise innovation, especially innovation quality, but this pattern will be broken by raising the threshold of innovation conditions. The reason is that strict innovation conditions can lead to a poor innovation foundation for enterprises, which are rarely affected by the fluctuation of funds obtained from intellectual property pledge financing. (2) Intellectual property pledge financing has a non-linear effect on firm innovation, characterized by an increase followed by a decrease, suggesting that intellectual property pledge financing in current China can only provide a temporary stimulus for firm innovation. (3) The relationship between intellectual property pledge financing and enterprise innovation is strongly moderated by the ownership, type, and size of the enterprise, with the inhibitory effect of intellectual property pledge financing on enterprise innovation occurring mainly in state-owned enterprises, high-tech enterprises, and small enterprises, while its positive effects are more pronounced in private enterprises, non-high-tech enterprises, and medium-sized enterprises. (4) Financing constraints, internal incentives, external supervision, and signaling mechanisms are indeed key pathways through which intellectual property pledge financing affects firm innovation, especially when we analyse these mechanisms using dynamic models.

The healthy operation of the stock market relies on sound liquidity. We utilize the semantic information from disclosure texts of listed companies on the China Science and Technology Innovation Board (STAR Market) to construct a daily corporate network. Through empirical tests and performance analyses of machine learning models, we elucidate the relationship between the similarity of company disclosure text contents and the temporal and spatial correlations of stock liquidity. Our liquidity indicators encompass trading costs, market depth, trading speed, and price impact, recognized across four dimensions. Furthermore, we reveal that the information loss caused by employing Minimum Spanning Tree (MST) topology significantly affects the explanatory power of network topology indicators for stock liquidity, with a more pronounced impact observed at the document level. Subsequently, by establishing a neural network model to predict next-day liquidity indicators, we demonstrate the temporal relationship of stock liquidity. We model a liquidity predicting task and train a daily liquidity prediction model incorporating Graph Convolutional Network (GCN) modules to solve it. Compared to models with the same parameter structure containing only fully connected layers, the GCN prediction model, which leverages company network structure information, exhibits stronger performance and faster convergence. We provide new insights for research on company disclosure and capital market liquidity.

We examine the impact of state-owned shareholders on fraud within private firms. Utilizing a sample of A-share private listed firms in China observed from 2008 to 2021. We discover a significant negative association between state-owned shareholders and the likelihood of fraud in private firms. State-owned shareholders primarily act as inhibitors of fraud, and their effect on the probability of fraud being detected is not statistically significant. This finding remains robust even after conducting a series of sensitivity tests to mitigate potential selectivity bias and reverse causality endogeneity issues. In the analysis of heterogeneity, we found that state-owned shareholders play a more active role under conditions of imperfect external institutional development, and they also exert a more significant inhibitory effect on enterprises with lower governance levels and higher business risks. Our mechanism test demonstrates that the inhibitory effect of state-owned shareholders on corporate fraud is achieved by improving corporate governance and alleviating financial distress. This study also examines the impact of state-owned shareholders' local characteristics, external supervision mechanisms, and internal governance mechanisms in unique Chinese enterprises on fraudulent behaviour by private enterprises. Overall, our study provides empirical evidence that state-owned shareholder ownership is associated with reducing fraudulent behaviour within private firms.

The economic institutionalist literature often suggests that sub-optimal institutional arrangements impart unique distortions in China, and excessive corporate debt is a symptom of this condition. However, lax monetary policies after the global financial crisis, and specifically, quantitative easing have led to concerns about debt bubbles under a wide range of institutional regimes. This study draws on data from Chinese listed firms, supplemented by numerous macroeconomic control variables, to isolate the effect of international capital flows from other drivers of firm leverage. We conclude that the rise in, and distribution of, Chinese corporate debt can partly be as-cribed to the effects of monetary policy outside of China and that Chinese institutional features amplify these effects. Whilst Chinese firms are affected by developments in the global financial ecosystem, domestic institutional realities and distortions may unevenly add their own particular effects, providing further support for and extending the variegated capitalism literature.

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 the influence of gambling culture on household investment decisions concerning risky financial assets. To estimate these effects, the study utilizes data from the 2019 China Household Finance Survey. The empirical findings reveal that gambling culture significantly enhances household preferences for risky financial assets and raises the proportion of household allocations to these assets. Furthermore, both subjective financial literacy and objective financial literacy amplify these positive effects. The heterogeneity analysis revealed that the effects of gambling culture on household preference for and allocation of risky financial assets varied across regions, income levels, and household types.

The aging population is expanding globally, and addressing the challenges of elderly care is urgent. Using the 2019 China Household Finance Survey data, this study finds that commercial pension insurance significantly promotes households’ allocation of risky financial assets. We test the mechanisms using household risk perception and investment risk preference as mediating variables. Heterogeneity analysis reveals that the positive effect of commercial pension insurance on risky financial asset allocation is more significant in rural households with household registration, those with two sets of housing, and households in the northeast. The research findings of this article aim to promote the continuous improvement of China’s elderly care system and provide important empirical evidence for the formulation of relevant policies.

The process of internationalization of China’s banking industry began in 1917. After a hundred years of development, China’s banking internationalization has made great achievements. However, there is still a big gap between China’s banking industry and the financial institutions in some developed countries in the field of internationalization. In the process of internationalization, China's banking industry are now still facing the dilemma of backward development concept, lack of effective risk control system and international talents. This thesis mainly introduces the history, present situation and difficulties of the internationalization of China’s banking industry. The first part gives a description to the history of the internationalization of China’s banking industry, which starts in the year of 1917. An analysis of the current situation of China’s banking industry’ internationalization is given in the second part of this article. And the third part summarizes the difficulties that are faced by China’s banking industry.

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.

Under the guidance of the dual-carbon target, the development of the carbon financial system is of great significance to compensate for the gap between green and low-carbon investment. Considering the current state of the development of carbon financial system, China has initially formed a carbon financial system, including participants, carbon financial products and macro and micro operation structures, but the system is still in the initial development stage. Given the current restrictions on the development of carbon finance, it can be seen that there are still problems such as unreasonable economic structure, insufficient market construction, single product category, low utilization rate and urgent construction of relevant judicial guarantee system. Therefore, the system should be improved at the economic level and the legal level. The economic level includes adjusting the layout of economic development structure, strengthening the construction of market infrastructure, encouraging the diversification of carbon financial products and strengthening publicity and education promotion strategies. The legal level includes improving the top-level design, formulating judicial interpretation to promote carbon financial trading, promoting commercial law amendment, and promoting the linkage mechanism between specialized environmental justice and carbon finance and other safeguard measures. Finally, improving the carbon finance system is required to promote and protect the orderly development of carbon finance. To promote the reform of the pattern of economic development, the concept of ecological and environmental protection in the financial sector needs to be implemented to form an overall pattern of pollution reduction, carbon reduction and synergistic efficiency improvement.