Journal of Banking & Finance最新文献

We document that leased capital constitutes about 30% of the total productive physical assets used by US public firms. We develop an analytical framework to demonstrate how the neglect of leases leads to an overestimation of productivity. This overestimation can be decomposed into two distinct channels: one arises from the mismeasured factor share, and the other from the omitted-leased-capital channel. Empirically, we find that the overestimation of aggregate productivity is substantial, has been increasing over time, and exhibits strong countercyclicality. In the cross-section, the decomposition of overestimation presents asymmetric patterns for firms of different sizes and levels of financial constraint. Our findings highlight the critical importance of explicitly accounting for the “unmeasured” leased capital in studies on productivity measurements.

This paper documents that Black and Hispanic borrowers are 52% more likely to experience a debt collection judgment compared to their counterparts, controlling for socioeconomic variables including income, credit scores, delinquent debt balances, and other relevant credit characteristics. Statistical discrimination mechanisms such as spatially targeting collection efforts based on the likelihood that an attorney represents the defendant or that the defendant will contest the debt in court do not explain the racial gap in judgments. We find support that the judgment gap is partially driven by taste-based discrimination, as evidenced by minorities having less 90-day past due debt balances than non-minority borrowers when controlling for model covariates. Furthermore, the disparity primarily affects non-delinquent borrowers, indicating higher levels of creditor discretion in initiating judgment proceedings. A back-of-the-envelope calculation suggests that the racial wealth gap explains at most 48% of the racial disparity in judgments.

Motivated by the theoretical literature on firms' choice between bank loans and public debt, this paper analyzes whether more able managers choose a higher fraction of public debt. We find that firms with more able managers choose a higher level of public debt. We also find that the use of public debt by more able managers is positively associated with wealth creation for shareholders and negatively associated with bankruptcy risk. Our cross-sectional analyses suggest that this baseline relationship is conditional on a better information environment. We address endogeneity issues in multiple ways and run an extensive array of robustness checks. Overall, our findings are consistent with the prediction that managerial ability mitigates the information monopoly of banks.

We study the relationship between crowding and performance in the active mutual fund industry. Using the equity holdings overlap of 17,364 global funds, we find that funds that crowd into the same stocks underperform passive benchmark funds by 1.4% per year. The negative returns to crowding can at least in part be explained by excess demand for liquidity and the associated discount for holding liquid stocks. We show that our measure of crowding contains novel information about performance that is not reflected in other variables that describe funds’ investment environment, such as fund size and style. Our findings suggest that crowding of investment opportunities is important for understanding diminishing returns.

We examine the role of inventor CEOs—those with personal hands-on experience of innovation—in the financing of innovation. Using a sample of technology initial public offerings (IPOs) in the US, we document that inventor CEOs are associated with lower underpricing at the time of the IPOs. Inventor CEOs also appear more capable of taking their firms public during “cold” IPO market periods when financing conditions are more difficult. Analyses based on regulatory changes and a founder-only sample suggest that these relationships are causal. Further, inventor-led firms appear to invest the IPO proceeds more productively, as reflected in the firms’ superior innovation outcomes in the post-IPO period. The stock market fails to fully understand these relationships, and IPOs led by inventor CEOs produce large positive abnormal returns in the three years following the offering. Our findings are consistent with inventor CEOs facilitating innovation financing for startup firms by effectively reducing information asymmetry and valuation uncertainty of firm's innovation capital for outside investors.

Despite the importance of the personal income tax around the world, little is known about its impact on innovation. We construct a large database of inventors who patented at publicly listed companies during the 2008–2016 period and exploit the revised Personal Income Tax (PIT) Law of 2011 in China as a quasi-natural experiment to establish the causal effect of the personal income tax on corporate innovation. Using a difference-in-differences identification strategy, we show that a lower personal income tax rate has a significantly positive impact on patent quantity and quality. Further, the revised PIT Law raises the efficiency of R&D activities, induces more explorative innovation, and improves the success rate of patent applications, providing consistent evidence for the intentional effort channel. Moreover, this positive innovation effect is more pronounced in firms with an R&D team that is more sensitive to the salary incentive system, greater innovation dependence, better governance, and firms located in regions with better innovation environments. Taken together, our findings shed light on how inventors and firms respond to decreasing personal income tax rates and confirm that the net return to innovation can be vital to the innovation capacity of firms.

The CARD Act restricts consumer credit card issuers’ ability to raise interest rates. We examine whether the Act influences the degree to which an issuer adjusts offered interest rates in response to changes in interest rates offered by other lenders in credit card solicitations—the price responsiveness. Using small business card offers as a control group, we find a significant decline in the price responsiveness after the Act. The decline is concentrated among other lenders’ rate reductions rather than rate increases and is more pronounced in areas with more subprime borrowers. The results underscore an unintended consequence of regulating the consumer credit market.

Machine learning methods have achieved great success in modeling complex patterns in finance such as asset pricing and credit risk that enable them to outperform statistical models. In addition to the predictive accuracy of machine learning methods, the ability to interpret what a model has learned is crucial in the finance industry. We address this challenge by adapting interpretable machine learning to the context of corporate bond recovery rate modeling. In addition to the best performance, we show the value of interpretable machine learning by finding drivers of recovery rates and their relationship that cannot be discovered by the use of traditional machine learning methods. Our findings are financially meaningful and consistent with the findings in the existing credit risk literature.

Navigating the complex landscape of corporate climate disclosures and their real impacts is crucial for managing climate-related financial risks. However, current disclosures oftentimes suffer from imprecision, inaccuracy, and greenwashing. We introduce ClimateBert _CTI, a deep learning algorithm, to identify climate-related cheap talk in MSCI World index firms’ annual reports. We find that only targeted climate engagement is associated with less cheap talk. Voluntary climate disclosures are associated with more cheap talk. Moreover, cheap talk correlates with increased negative news coverage and higher emissions growth. Hence, cheap talk helps assess climate initiatives’ effectiveness and anticipate reputation and transition risk exposure.

We comprehensively examine the interaction effects in the cross-sectional predictability of country and industry returns. Using nearly five decades of data from 68 countries, we construct all possible double-sorted portfolios based on 44 portfolio characteristics and uncover numerous significant interactions. An out-of-sample value-weighted strategy that selects the top long-short country (industry) interactions generates a monthly World CAPM alpha of 0.33% (0.62%) with a Sharpe ratio of 0.58 (0.75). The strongest interactions stem from implementing momentum and technical analysis signals in small and illiquid countries or industries. Furthermore, the return patterns mainly emanate from frontier and weakly integrated markets—highlighting the role of market frictions and segmentation in the occurrence of abnormal returns. Consistent with these interpretations, the interactions decline over time as global markets mature and become more integrated.