Knowledge that Transforms

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.

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.

Few studies investigate whether ESG mitigates the harmful effects of changes in firms’ external environments. We evidence that ESG mitigated the impact of COVID-19 work-from-home and workplace prescriptions amongst several other pandemic-related government regulatory interventions, even when controlling for firm size. In a novel approach, we apply scrutiny of firms to restrict our cross-national sample to only firms with no cross-border trade, that is, explicitly domestically focused operational processes irrespective of the endpoint of corporate sales, enhancing methodological robustness. Consequently, we isolate an ESG effect. Results indicate the existence of a premium during the onset of each analysed national pandemic experience, particularly pronounced for those corporations that had achieved more substantiative ESG-based preparation and development before the onset of the COVID-19 pandemic.

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.

The seeds of financial imbalances are sown in times of buoyant economic growth. We study the link between macroeconomic performance and financial imbalances, focusing on the experience of the United States since the 1960s. We first follow a narrative approach to review historical episodes of significant financial imbalances and find that the onset of financial disturbances typically occurs when the economy is running hot. We then look for evidence of a statistical link between measures of macroeconomic conditions and financial imbalances. In our in-sample analysis, we find that strong economic growth is followed by a buildup of financial imbalances across all dimensions of the National Financial Conditions Index. In our out-of-sample analysis, we find that the link between strong economic performance and increases in nonfinancial leverage is particularly strong and robust. Using a structural VAR identified with narrative sign restrictions, we also demonstrate that business cycle shocks are important drivers of nonfinancial leverage.

This paper explores the dynamic effects of counterparty risk on stock liquidity using data on unsecured creditors after a debtor has declared bankruptcy. Through matched pair fixed effect panel regressions, we find that liquidity for unsecured creditors reduces after such declarations but only in the short term. This is evidenced by increases in various spread measures and Kyle's (1985) lambda and decreases in the bid depth differentials between the stocks of the unsecured creditors and the matched firms. Additionally, we find the greater the credit exposure, the greater the decline in liquidity. In the long term, debtor bankruptcies appear to have no effect on spread measures. Rather, the market depth for unsecured creditor stocks improves.

We examine the impact of fire sales on the UK financial system through commonly held assets across different financial sectors. In particular, we model indirect contagion via fire sales across UK banks and non-banks subject to different types of constraints. We find that performing a stress simulation that does not account for common asset holdings across multiple sectors can severely underestimate the fire sale losses in the financial system. In addition, pro-rata liquidation strategy would result in a higher level of fire sale losses in the system as whole, but a waterfall strategy may produce a higher spillover effect for a passive institution (or a passive sector) that chooses not to promptly liquidate any of its assets during distress while other institutions decide to do so.