Journal of Financial Stability最新文献

This study empirically examines the impact of an exogenous decrease in banks’ shareholding on bank loans and firms’ risk-taking, utilizing a regulatory change in Japan relating to banks’ shareholding as an instrument. We find that an exogenous reduction in a bank’s shareholding decreased the bank’s share of loans in the client firm’s total loans, while it increased the volatility of a firm’s return on assets. The reduction in a bank’s shareholding did not affect firm risk as perceived by equity investors or its borrowing terms. These findings are consistent with the prediction that banks hold equity claims over client firms to gain a competitive advantage, and are weakly compatible with the prediction that banks’ shareholding mitigates shareholder–creditor conflict.

Increasing accounting information comparability (AIC) theoretically facilitates investors’ analysis of firm performance and improves stock price informativeness by incorporating more firm-specific information. However, achieving the purported purpose empirically is subject to firms’ institutional environment and corporate governance. We propose that under weak legal systems and less developed market environments, higher AIC may adversely affect price informativeness due to managers’ incentives and ability to obfuscate information and investors’ “hallo” effect. Using a large sample from China, we show that the AIC is positively related to price synchronicity, an inverse measure of price informativeness. Additionally, the positive impact is significantly greater for firms located in regions with weak legal systems and less developed market environments. The positive relation is also significantly greater when the business environment and economic policy uncertainties are high.

Relying on theories in which bank create private money by making loans that create deposits—a process we call “funding liquidity creation”—we measure how much funding liquidity the U.S. banking system creates. Private money creation by banks enables lending to not be constrained by the supply of cash deposits. During the 2001–2020 period, 92 percent of bank deposits were due to funding liquidity creation, and during 2011–2020 funding liquidity creation averaged $10.7 trillion per year, or 57 percent of GDP. Using natural disasters data, we provide causal evidence that better-capitalized banks create more funding liquidity and lend more even during times when cash deposit balances are falling or unchanged. Large banks as well as the top banks in Federal Reserve districts create more liquidity.

We conduct an international study on the effect of climate policy uncertainty on the systemic risk of banks from G20 countries. We find that climate policy uncertainty is associated with lower bank systemic risk. This relation is more pronounced in countries with high innovation capacity, climate readiness, more systemically important banks, and a more competitive banking system. Climate-related information disclosure and sustainable investments are critical economic channels through which the effect of climate policy uncertainty works. Our findings alleviate the concern that climate transition risk may contribute to financial instability and provide practical implications for regulators to design climate transition policies.

Small business lending (SBL) plays an important role in funding productive investment and fostering local economic growth. Recently, nonbank lenders have gained market share in the SBL market in the United States, especially relative to community banks. Among nonbanks, fintech lenders have become particularly active, leveraging alternative data and complex modeling for their own internal credit scoring. We use proprietary loan-level data from two fintech SBL platforms (Funding Circle and LendingClub) to explore the characteristics of loans originated pre-pandemic (20162019). Our results show that these fintech SBL platforms lent relatively more in zip codes with higher unemployment rates and higher business bankruptcy filings. Moreover, fintech platforms’ internal credit scores were able to predict future loan performance more accurately than traditional credit scores, particularly in areas with high unemployment. Using Y-14 M loan-level bank data, we compare fintech SBL with traditional bank business cards in terms of credit access and interest rates. Overall, while not all fintech firms follow the same approach, we find that fintech lenders could help close the credit gap, allowing small businesses that were less likely to receive credit through traditional lenders to access credit and potentially at lower cost.

This paper compares the predictive power of credit scoring models based on machine learning techniques with that of traditional loss and default models. Using proprietary transaction-level data from a leading fintech company in China, we test the performance of different models to predict losses and defaults both in normal times and when the economy is subject to a shock. In particular, we analyse the case of an (exogenous) change in regulation policy on shadow banking in China that caused credit conditions to deteriorate. We find that the model based on machine learning and non-traditional data is better able to predict losses and defaults than traditional models in the presence of a negative shock to the aggregate credit supply. This result reflects a higher capacity of non-traditional data to capture relevant borrower characteristics and of machine learning techniques to better mine the non-linear relationship between variables in a period of stress.

Blockchain is a ground-breaking technology with potential applications in fundraising. In this study, we analyze the blockchain-based fundraising data from 2019 to 2021 to investigate the differences between various fundraising models (i.e., ICO, IEO, IDO, and MIX). More specifically, in Study 1, we conduct ANCOVA and ANOVA to examine differences in fundraising success and token performance after listing between different fundraising models. In Study 2, we first explore the factors that affect fundraising success and token performance, and then verify whether the impact of these factors varies between fundraising models. The findings of our research have implications for both firms and investors, assisting firms in selecting the most effective fundraising models and aiding investors in identifying tokens with the greatest potential.

This article examines how competition between banks and a digital PSP impacts the lending rate and the consumers’ use of payment instruments. The digital PSP offers a digital wallet and payment services, but does not offer credit. In contrast, banks invest their deposits in lending activities, which implies that they may incur some costs of adjusting their liquidity needs when consumers make payments. I show that the adoption of the digital wallet for payments may sometimes increase the volume of payments by bank deposit transfers and the lending rate. This results from banks’ trade-off between lowering their costs of liquidity when consumers pay from their digital wallet and reducing the revenues they receive from bank transfer fees.

We develop a general equilibrium model that highlights the trade-offs between physical and digital forms of retail central bank money. The key differences between cash and central bank digital currency (CBDC) include transaction efficiency, possibilities for tax evasion, and, potentially, nominal rates of return. We establish conditions under which cash and CBDC can co-exist and show how government policies can influence relative holdings of cash, CBDC, and other assets. We illustrate how a CBDC can facilitate negative nominal interest rates and helicopter drops, and also how a CBDC can be structured to prevent capital flight from other assets.