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.

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.

We investigate the impact of financial geographic density on corporate financial asset holdings in emerging market. We proxy for financial geographic density by calculating the number of financial institutions around a firm within a certain radius based on the geographic distance between the firm and financial institutions. Using data on publicly listed A-share firms in China from 2011 to 2021, we find that financial geographic density has a positive impact on nonfinancial firms’ financial asset investments, especially for the firms located in regions with a larger number of banking depository financial institutions or facing greater market competition. An increase in the number of financial institutions surrounding firms increases corporate financial asset holdings by alleviating information asymmetry. Moreover, we document that Fintech has little impact on the relationship between financial geographic density and corporate financial asset holdings. As the rise of financial geographic density, firms hold more financial assets for precautionary motives, which contribute to corporate innovation.

Software and accounting advances have led to a rapid expansion in and proliferation of loyalty tokens, typically bundled as part of product price. Some tokens, such as in the airline industry, already account for tens of billions of dollars and are a major contributor to revenues. An open question is whether, as technology evolves, firms will have a strong incentive to make loyalty tokens tradable, raising regulation issues, including with monetary and banking authorities. This paper argues that for the vast majority of tokens, issuing firms have a strong incentive to make them non-tradable. The core incentive for token issuance here is that an issuer can earn a higher rate of return on the ``float'' (tokens issued but not yet used) than its retail customers can, much like a bank. Our main finding is that an issuer earns higher revenue by making tokens non-tradable even though the consumer would be willing to pay a higher price for tradable tokens. We further show that an issuer with stronger market power tends to allow more frequent token redemption, and its revenue is more token-dependent. We test the model's predictions with data on airline mileage and hotel reward programs and document consistent empirical results that align with our theory.

Since the global financial crisis of 2007–2009, preventing financial crises has become one of the most important objectives of regulators and banks. Although previous studies have identified the phenomenon of risk contagion in the banking system, the underlying mechanisms of risk contagion are still unclear. This study delves into the multi-stage contagion mechanism of liquidity risk based on interbank lending linkages and clearing rules and introduces a new index to quantify bank liquidity risk. We find that the contagion of liquidity risk is primarily determined by the network structure of risk exposures between banks in default and is not significantly influenced by the lending relationships of banks that remain solvent. The empirical results suggest that banks with high risk should be prioritized for cash injections to improve system liquidity. These findings offer new insights into financial risk contagion and practical recommendations for regulatory authorities formulating intervention strategies and for banks conducting risk management.

Compared to previous literature on external FinTech, this paper is more interested in the role played by bank FinTech. Based on panel data from Chinese commercial banks spanning 2010 to 2021, this paper investigates the impact of digital transformation on bank soundness and its potential mechanisms. The empirical findings demonstrate a positive association between digital transformation and bank soundness, driven primarily by strategic and management digitization. Mechanistic analysis indicates that digital transformation improves bank soundness by mitigating risk-taking behavior and promoting diversification. The positive effect of digital transformation is more pronounced in state-owned and joint-stock banks, banks with higher liquidity mismatch as well as in sub-samples with greater levels in external FinTech development and economic policies uncertainty. Additional analysis suggests that digital transformation can still enhance bank soundness even in the presence of relatively easy monetary and macroprudential policies, highlighting the harmonization and complementarity between internal innovation from digital transformation and external regulatory policies in maintaining banking stability. Overall, this paper contributes to the literature on bank FinTech, factors influencing bank stability. And it also provides a novel explanation for the relationship between financial innovation and financial stability.

We study how bank liberalization affects analyst forecast accuracy using two interest rate deregulations in China—the removal of the cap on bank lending rates in 2004 and the removal of the floor in 2013—as quasi-natural experiments. Our results show that the analyst forecast accuracy for high-risk firms decreases significantly after the removal of the lending rate cap, whereas analyst forecast accuracy for low-risk firms increases significantly after the removal of the lending rate floor. Moreover, interest rate liberalization affects forecast accuracy through operational risk and information asymmetry channels. Furthermore, the impact was concentrated on firms whose actual performance fell short of performance expectations and those that received more bank loans. Our findings imply that interest rate liberalization policies may have unintended consequences for analyst forecasts.

While it is generally believed that pressure from peers induces employees to improve their efficiency and performance, little is known about whether employees' improved performance is detrimental to the interests of others. Based on a granular dataset at the individual-month level of investment advisors' and customers’ accounts from a large retail bank in China, we find that peer pressure, as measured by the performance of advisors relative to their colleagues in the previous month, can induce the advisors to sell more financial products, but can also exacerbate misselling, resulting in a significant increase in sales of poor-quality financial products ("high-risk-low-return" products). The causal link is identified with an exogenous change of peer size. The peer pressure effects are pronounced among poor performance advisors, and client complaints play a monitoring role in curbing misselling. By exploring the correspondence between advisors and clients, we find that misselling occurs mainly between female advisors and male clients, and between advisors who lack work experience and clients who lack investment experience.