Knowledge that Transforms

This empirical study investigates whether a strong bank culture may help strengthen, weaken, or have no effect on the relationship between regulatory capital and liquidity creation. Using a machine learning approach and banks’ 10-K reports, we first measure the corporate culture of selected bank holding companies (BHCs) in the United State (U.S.) over the period between 1995 and 2019. We find that bank culture does affect the link between regulatory capital and liquidity creation. In particular, while we find that regulatory capital has a negative impact on bank liquidity creation, a strong culture in a bank weakens this negative association. We also find that an increase in asset-side liquidity creation is the main channel through which bank culture exerts its moderating role. Finally, our results are largely driven by smaller banks, banks with a more traditional funding structure and more profitable banks. The results of this study suggest that regulators should consider bank culture as being a crucial element in the monitoring approach when designing bank regulation and supervision.

This paper shows that higher information technology (IT) adoption by banks was associated to a larger increase in corporate lending in the months following the COVID-19 outbreak in Italy. Examining banks with heterogeneous degrees of IT adoption, we investigate the dynamics of credit and its allocation across firms using a new database with detailed information on banks’ IT expenditures and use of innovative technologies matched with bank-firm level data on credit growth before and during the pandemic. Using a diff-in-diff approach, we find that banks with a higher share of IT spending increased their credit more than others during the pandemic. The increase was concentrated in term loans extended to smaller and financially sounder companies; the effect was stronger in the initial phase of tighter restrictions to firm activity and individual mobility, and more significant for undertakings active in the sectors most affected by the shock. We provide evidence that these results are driven by bank’s ability to offer credit entirely online and bank’s use of artificial intelligence for credit risk assessment. Physical proximity between borrowers and lenders was important for credit provision during the pandemic, but only when combined with high level of IT adoption.

I uncover heterogeneous effects of macroprudential policy on GDP growth distribution by bringing together the literature on the impact of macroprudential policy and recent developments on the use of quantile regressions. I identify important benefits of macroprudential policy on the left-tail of the GDP growth distribution which contrast with the negative effects found in previous studies using conditional mean models. These benefits may offset the deterioration on growth-at-risk produced by the build-up of cyclical vulnerabilities and the materialization of financial crises. I also find that the impact of macroprudential policy is dependent on the position in the financial cycle, the type of instrument, and the time elapsed since its implementation. In particular, tightening capital measures during expansions may take up to two years to show evidence of benefits on growth-at-risk, while the positive impact of borrower-based measures is rapidly observed. Conversely, in downturns the benefits of loosening capital measures are more immediate, while those of borrower-based measures are limited. This suggests the importance of timing in macroprudential policy. Overall, this study provides a useful framework to assess the impact of macroprudential policy in terms of GDP growth and to identify the term-structure of specific instruments.

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.

Digitalization is a crucial strategy for firms to gain a competitive advantage. This study utilizes data from Chinese listed firms from 2011 to 2020 to examine how digitalization affects firms' stock price informativeness. Empirical results demonstrate that digitalization reduces stock price synchronicity and promotes firms' stock price informativeness. Moreover, digitalization improves stock price informativeness by enhancing investment efficiency and firm value, reducing information asymmetry, and alleviating agency costs. These findings suggest that effective digital strategies and capabilities constitute an important yet underappreciated resource that enables firms to generate value-relevant information and improve the information environment in emerging capital markets. Our study contributes new evidence on the financial market implications of digital transformation from a resource-based view perspective.

This study establishes a significant positive relation between high quality auditors and long-run post-IPO equity performance. IPOs associated with high-ranked auditors benefit from superior information quality irrespective of underwriter rank, manifesting in significantly better post-IPO equity performance. The auditor certification effect is robust and persists longer than the underwriter certification effect. IPOs, regardless of the underwriter rank, benefit significantly from the auditor reputation effect. Further, the auditor certification effect is more pronounced: (a) when underwriter certification is weak (‘substitution effect’), and (b) in the presence of greater information asymmetry. VC backed IPOs perform significantly better; however, VC reputation has no effect, after controlling for auditor rank and underwriter certification. Our conclusions are reinforced by a battery of robustness checks, including the use of alternative methodologies to address endogeneity, audit quality proxies, performance metrics, model specifications, and validity tests.

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.

We show how external credit market development can affect corporate social responsibility. Using a sample of US public firms over the period 1991–2010, we find that bank deregulation negatively affects CSR performance. We argue that deregulation-induced banking competition enhances credit accessibility, thereby reducing firms’ incentives to pursue CSR as a means of securing stakeholder rewards. Empirical evidence shows that firms increase their use of debt financing in response to the intensified banking competition, and these firms experience a more pronounced decline in CSR performance. We alleviate the potential concern that the observed decline in CSR could be attributed to changes in bank monitoring following deregulation. Further analyses find that firms reduce CSR regardless of their material nature, suggesting that the primary driver of CSR could be the trade-off between costs and returns. Overall, our findings shed light on the strategic motives of CSR, which exhibits adaptability in response to business dynamism.

We examine the social desirability of macroprudential measures, particularly those aimed at riskier home buyers. We examine the effectiveness of these measures against social costs, such as reduced access to the housing ladder for poorer households. Our analysis shows that the measures implemented so far have not limited access to credit or the housing markets. They have been effective in limiting the riskiest loans, minimizing negative equity episodes, reducing systemic risks by debilitating the house price-leverage spiral, and limiting the depths of contractions of a range of macro-financial variables. The welfare of households has also improved. Costs from these measures have been limited and have materialized through a rise in the age-income profile of first-time buyers, and somewhat more attenuated booms. Our results point to the conclusion that macroprudence is desirable when insulated from short-term interference and quick gains. The economy becomes more robust and even households in the lowest decile of the wealth distribution benefit from the general equilibrium effects of more stable financial provision.

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.