Search results

A major source of external funding for US airports comes from issuing municipal bonds. Credit rating agencies evaluate the bonds using multiple factors, but the judgments behind the ratings are not well understood. This paper examines if airport rate-setting methods affect the bond ratings of US airports.

Using a set of unbalanced panel data for 58 hub airports from 2010 to 2019, we examine the effect of the rate-setting methods and other airport characteristics on Fitch’s airport bond rating.

We find that compensatory airports consistently receive a very high bond rating from Fitch. The probability of getting a very high Fitch rating increases by ∼28 percentage points for a compensatory airport. Additionally, the probability of getting a very high rating is about 33 percentage points higher for a legacy hub.

The study uses Fitch bond ratings. Future studies could examine if S&P’s and Moody’s ratings are also influenced by airport rate-setting methods and legacy hub status.

The results uncover the linkage between bond ratings and their determinants for US airports. This information is important for investors when assessing airport creditworthiness and for airport operators as they manage capital project financing.

This is the first study to evaluate the effects of rate-setting methods on airport bond rating and also the first to document a statistically significant relationship between airports’ legacy hub status and bond ratings.

This paper aims to investigate how a US firm’s political landscape affects the integration of environmental, social and governance (hereafter ESG) measures in CEO compensation contracts, thereby affecting the firm’s ESG performance and credit rating.

Based on the results of state senatorial and presidential elections and the location of a US firm’s headquarters, the authors categorize whether a firm has a political environment that is predominantly Democratic (blue) or Republican (red). The empirical analyses are based on a sample of US firms in the period 2014–2021.

The authors find that firms in blue states are more likely to link CEO compensation to ESG performance measures. Further, the results show that firms in blue states with ESG-linked compensation contracts have better ESG performance. Lastly, the authors find evidence that a firm’s ESG performance has a positive impact on its credit rating, but the impact is weakened if firms in red states link ESG performance to executive compensation.

To the best of the authors’ knowledge, this is the first research that explores how a firm’s political environment affects the use of ESG performance measures in CEO compensation contracts. Furthermore, the authors contribute to the literature by showing evidence that the political environment interacts with the impact of ESG-linked compensation incentives on the firm’s ESG performance and, thus, its credit rating.

This study aims to establish that the relationship between the risky indebtedness behavior (RIB) of consumers and their attitude toward adopting buy-now-pay-later (BNPL) is not immediate but is mediated through impulse buying. Moreover, it explores how perceived risk moderates the association between the attitude to adopt BNPL and its adoption intention.

This study used the existing theoretical and empirical evidence to propose a model and validated it using the data collected from 339 young shoppers in India. Analysis of data is conducted using partial least squares structural equation modeling.

The study results show that consumers’ RIB is not directly related to their attitude toward BNPL. However, impulse buying fully mediates this relationship, influencing the attitude toward BNPL. Impulse buying and attitude serially mediate the relationship between RIB and BNPL adoption intention. Further, in the context of BNPL, perceived risk strengthens the attitude-intention gap.

This study advises policymakers and BNPL providers to carefully assess users’ creditworthiness to prevent those already in debt from entering into a detrimental loop.

This study provides novel perspectives on consumer’s RIB and BNPL within the Indian context. The study additionally identifies the mediating influence of impulse buying and the moderating effect of perceived risk on BNPL adoption intention.

Prior studies generally focus on income smoothing through discretionary accruals and document that managers have incentives to smooth earnings due to various reasons. This paper aims to focus on income smoothing through research and development (R&D) management and examine whether and how income smoothing through R&D management affects credit rating agencies’ perception of firm risk.

The authors use financial statement data from the CRSP/Compustat Merged data set universe for the period from 1992 to 2019 after excluding financial and utility industries. The authors follow the model for credit ratings used in previous literature to test the hypothesis. Specifically, the authors use an ordered probit model to express credit ratings as a function of income smoothing attributes.

The authors find that R&D-based income smoothing improves a firm’s credit rating. However, the positive effect of R&D-based income smoothing on credit ratings is less than that of accruals-based income smoothing. This study also shows that the positive effect of R&D-based income smoothing is more pronounced for firms less subject to opportunistic incentives, further strengthening the notion that managers smooth earnings through R&D management to provide more informative earnings.

This study contributes to the income smoothing literature in several ways. First, the authors contribute to the research by showing that managers’ income smoothing activity through R&D management positively affects firms’ credit rating. Second, the authors also document the relative benefits of the two different income smoothing techniques in terms of improving credit agencies’ perception of firms’ creditworthiness.

This study aims to analyze the factors that influenced the stability of Islamic banks in Asia.

The panel data consisted of 16 Asian countries operating Islamic banks from 2010 to 2019. The data were analyzed through dynamic panel regression using Arellano–Bond generalized method of moments (GMM).

This study provides novel insights into the factors influencing the stability of Islamic banks in Asia. The findings suggest that past financial stability, liquidity risk, loan risk, inflation, gross domestic product, government effectiveness, rule of law and control of corruption are all significant contributors to Islamic bank stability. Notably, political stability, voice and accountability and regulatory quality did not show a significant association.

The current study’s focus was solely on Islamic bank stability in Asian countries, which leaves room for further exploration. Future research could benefit from expanding the scope to encompass all nations with active Islamic banking institutions. In addition, incorporating a broader range of macroeconomic variables, such as exchange rates, interest rates, profit-sharing equivalents and investment rates, could provide deeper insights into the factors influencing Islamic bank stability across diverse contexts.

This study has significant practical implications for policymakers, bank managers and regulatory authorities seeking to enhance the stability of Islamic banks in Asia. By implementing robust risk management frameworks, adopting prudent regulatory policies, and actively fostering economic growth, policymakers can create an environment conducive to the sustained development and prosperity of Islamic banking institutions. Notably, promoting good governance practices and instituting effective crisis prevention measures can further bolster the resilience of the Islamic banking sector, enabling it to play a more dynamic role in contributing to the overall development and welfare of Asian societies.

The findings of this study carry significant social implications, highlighting the need for governments in Asian countries to prioritize public policies that promote good governance and ethical practices within the banking industry. Such policies, coupled with efforts to attract foreign investments and foster a stable and transparent banking sector, have the potential to generate far-reaching positive effects on society. Through economic growth stimulated by a robust Islamic banking sector, Asian countries can create new employment opportunities, improve living standards and ultimately enhance the overall well-being of their citizens.

This study contributes to the ongoing discourse on Islamic banking stability by offering novel insights and expanding the empirical knowledge base in this field. The dual application of robust regression methodologies – namely, GMM dynamic panel data models – presents a unique analytical framework for investigating the complex interplay between diverse variables and Islamic bank stability. This methodological choice fosters deeper understanding of the dynamic relationships at play, advancing our understanding of how specific factors influence the sector's resilience and performance. In addition, the study uses rigorous empirical techniques and engages with the extant literature to provide fresh perspectives and nuanced interpretations of the findings, further solidifying its contribution to the field's originality and richness.

The research documented in this paper aims to examine multiple factors related to borrowers' default in peer-to-peer (P2P) lending in the USA. This study is motivated by the hypothesis that both P2P loan characteristics and macroeconomic variables have influence on loan performance. The authors define a set of loan characteristics, borrower characteristics and macroeconomic variables that are significant in determining the probability of default and should be taken into consideration when assessing credit risk.

The research question in this study is to find the significant explanatory variables that are essential in determining the probability of default for LendingClub loans. The empirical study is based on a total number of 1,863,491 loan records issued through LendingClub from 2007 to 2020Q3 and a logistic regression model is developed to predict loan defaults.

The results, in line with prior research, show that a number of borrower and contractual loan characteristics predict loan defaults. The innovation of this study is the introduction of specific macroeconomic indicators. The study indicates that macroeconomic variables assessed alongside loan data can significantly improve the forecasting performance of default model. The general finding demonstrates that higher percentage change in House Price Index, Consumer Sentiment Index and S&P500 Index is associated with a lower probability of delinquency. The empirical results also exhibit significant positive effect of unemployment rate and GDP growth rate on P2P loan default rates.

The results have important implications for investors for whom it is of great importance to know the determinants of borrowers' creditworthiness and loan performance when estimating the investment in a certain P2P loan. In addition, the forecasting performance of the model could be applied by authorities in order to deal with the credit risk in P2P lending and to prevent the effects of increasing defaults on the economy.

This paper fulfills an identified need to shed light on the association between specific macroeconomic indicators and the default risk from P2P lending within an economy, while the majority of the existing literature investigate loan and borrower information to evaluate credit risk of P2P loans and predict the likelihood of default.

The objective of this study is to investigate the relationship between trade credit supply and financial distress outcomes, considering the role that trade credit plays as a substantial source of liquidity for distressed companies. Specifically, it examines whether there is an association between trade credit supply and the outcomes experienced by companies that undergo the voluntary administration (VA) insolvency procedure under Australian corporate law.

The study examines a sample of companies that were listed on the Australian Securities Exchange and entered VA between 2002 and 2019. Ordered logistic regression is used to determine the relation between trade credit and VA outcomes. The VA outcomes considered are as follows: (1) company liquidation, (2) orderly dissolution through an agreement with creditors, or (3) an agreement with creditors for reorganization of all or part of the company's business.

The findings show that trade creditors' willingness to supply credit is influenced by their rational expectations about the future prospects of financially distressed customers. Higher levels of trade credit and an increase in trade credit supply prior to VA are associated with a greater probability of achieving a reorganization versus a liquidation or dissolution outcome.

There is no apparent prior study investigating the connection between trade credit supply and outcomes for distressed companies entering insolvency administration. Therefore, this study provides novel evidence on the role of trade credit in the context of financial distress. Understanding the relationship between trade credit supply and outcomes is particularly significant considering that many jurisdictions offer distressed companies the opportunity to pursue reorganization under their insolvency laws. Examining financial distress and trade credit in the Australian creditor-friendly context expands on existing research. Prior research has predominantly relied on data from the United States, which has debtor-friendly bankruptcy law. Consequently, these studies may lack generalizability to jurisdictions with creditor-friendly law such as Australia.

The purpose of the study is to examine the use of alternative information in bank lending to small and medium enterprises (SMEs). Understanding alternative information and its use in bank lending to SMEs is important because it has become a growing part of the future of SME finance. The results and findings of my study not only enrich the finance literature but, more importantly, also address the use of Fintech in the risk management of SME lending, a new and complex problem that is specific to both the information technology and finance field.

To answer the research question, the author used a case study approach that relies upon qualitative data and analysis. By iterating between the existing literature, theoretical pieces and empirical findings, the author explain and interpret in detail how the use of alternative information impacts loan outcomes and develop insights to guide future research.

The case is outlined in two time periods including the prepartnership period and the postpartnership period. It highlights the establishment of a partnership between LoanBank and FintechInc (pseudonym), aimed at SME-focused Fintech lending. The findings underscore how the partnership has enabled a mutually beneficial situation where LoanBank and FintechInc leverage each other’s strengths to provide efficient and effective lending services. The adoption of alternative information in the risk management Fintech (RMF) platform of FintechInc has transformed LoanBank’s lending processes, showcasing how technological innovations can enhance SME lending practices.

The study’s originality mainly lies in the three detailed insights regarding alternative information’s impact on SME lending: information, platform properties and financial inclusion. The information part demonstrates that RMF platforms expand the information used for lending decisions, shifting from traditional hard and soft data to incorporating various alternative information sources. The platform properties part suggests that location, openness and technology also play a pivotal role in shaping lending outcomes. Finally, the financial inclusion part proposes that the use of alternative information has the potential to improve financial inclusion and offer better credit terms to previously underserved borrowers.

This paper explores how financial technology (FinTech) organisations address poverty-related challenges when providing digital financial services. Employing the conceptual foundation of the liability of poorness (i.e. literacy gaps, a scarcity mindset, intense non-business pressures and a lack of financial slack), this paper explores the innovative strategies that FinTechs use to address these liabilities and promote entrepreneurship.

The paper uses detailed case data collected from three FinTech organisations operating in one South Asian country.

FinTech organisations' innovative strategies reflect a combination of “high touch” (human) vs “low touch” (digital) solutions. All the organisations simplified internal systems or procedures to accommodate customers. The degree to which the three organisations adopted each of the identified strategies shows an emerging typology of FinTechs; that is, innovators with high digital interactions, a mix of digital-human interactions and high human interactions.

The paper develops a typology which categorises FinTech innovative strategies. The typology highlights strategies pro-poor FinTechs use and explains the types of entrepreneurial support innovative organisations provide for their customers. Both the typology and the innovative strategies contribute to enhanced financial inclusion and entrepreneurial promotion amongst the poor.

The originality of the paper comes from its focus on FinTechs' innovative pro-poor strategies. Existing studies typically address the technology-side of innovations. In contrast, this paper combines innovative strategies with the liability of poorness to identify issues associated with financial inclusion.

This study aims to examine the impact of intellectual capital (IC) on default risk in Indian companies listed on the National Stock Exchange.

This study applies panel data regression analysis to derive a relationship between IC and default risk for the sample period 2013–2022. The value-added intellectual coefficient (VAIC) of Pulic (2000) has been applied to measure IC performance, and default risk is estimated using the revised Z-score model of Altman (2000).

The results revealed a positive association between Z-score and VAIC. It implies that a higher value of VAIC improves financial stability and leads to a lower likelihood of default. The findings further suggest that new default forecasting models can be experimented with IC indicators for better default prediction.

The findings can have implications for investors and banks. This paper provides evidence of IC performance in improving the financial solvency of firms. Investors and financial institutions should invest their resources in a healthy firm that effectively manages and invests in their IC. It will eventually award investors and creditors high returns through efficient value-creation processes.

This study provides evidence of IC performance in improving the financial solvency of Indian high-defaulting firms, which lacks sufficient evidence in this domain of research. Numerous studies exist examining the relationship between firm performance and IC value, but this area is inadequately focused and underresearched. This study, therefore, fills the research gap from an Indian perspective.