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We analyze the effect of subsidies on firm R&D, conditional on firms' access to external financing during the financial crisis when access to external financing was particularly difficult. Specifically, we determine whether subsidies can mitigate or completely offset the effect of external financing constraints of firms or banks. Financial constraints are considered through (i) firms' credit ratings, and (ii) the capital reserves of their banks. Subsidies were found to help maintain R&D investments during the crisis, especially when financial constraints were present. After the crisis, the subsidized firms reached the level of R&D spending they had prior to the crisis. [ABSTRACT FROM AUTHOR]

After the expiration of each lease, lessees may return leased equipment on schedule, renew the lease or purchase it according to the equipment status and their demands. By considering lessees' uncertain options and equipment status difference, the pricing and inventory decision-makings of leased equipment are explored. A mixed-integer nonlinear programming model by maximising the net present value of lessor's profit is presented with respect to constraints like rental revenue, manufacturing, lessees' credit check, transportation, maintenance, upgrade and inventory costs. The rental price and inventory decisions are obtained by solving the problem with a particle swarm optimisation algorithm. We also analyse the impacts of purchasing cost of old equipment from a third-party supplier on lessor's inventory, renewal price or purchasing price of leased equipment on lessees' options and lessor's profit. The results show: (1) with the extension of lease period, the rental price increases while the growth rate decreases; (2) the maintenance cost accounts for about 20% of total cost, and the preventive maintenance strategy can reduce excess maintenance cost as lease period increases; (3) the lessor shall set moderate renewal price discount coefficient and purchasing price coefficient, and analyse purchasing cost of old equipment to manage inventory timely. [ABSTRACT FROM AUTHOR]

Municipal bonds, which are also known as muni bonds or simply munis, are commonly issued by state and local governments. Interest paid on munis is generally tax exempt which benefits investors in high tax brackets. Individuals should only own munis in a taxable account so they can capture the tax-exempt benefit. They have very low default/credit risk due to either a high credit rating and/or a third-party guarantee. Capital gains associated with these bonds are taxable unless the de minimis rule applies. Tax-exempt interest payments may also trigger the alternative minimum tax (AMT). Investors can purchase munis directly, or indirectly through a fund. [ABSTRACT FROM AUTHOR]

Micro, Small, and Medium-sized Enterprises (MSMEs) are essential for the growth and development of the country's economy, as they create jobs, generate income, and foster production and innovation. In recent years, credit risk assessment (CRA) has been an essential process used by financial institutions to evaluate the creditworthiness of MSMEs and determine the likelihood of default. Traditionally, CRA has relied on credit scores and financial statements, but with the advent of machine learning (ML) algorithms, lenders have a new tool at their disposal. By and large, ML algorithms are designed to classify borrowers based on their credit history and transactional data while leveraging the entity relationship involved in credit transactions. This study introduces an innovative knowledge graph-driven credit risk assessment model (RGCN-RF) based on the Relational Graph Convolutional Network (RGCN) and Random Forest (RF) algorithm. RGCN is employed to identify topological structures and relationships, which is currently nascent in traditional credit risk assessment methods. RF categorises MSMEs based on the enterprise embedding vector generated from RGCN. Extensive experimentation is conducted to assess model performance utilising the Indian MSMEs database. The balanced accuracy of 92% obtained using the RGCN-RF model demonstrates a considerable advancement over prior techniques in identifying risk-free enterprises. [ABSTRACT FROM AUTHOR]

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This paper, starting from the effects of financial regulatory policies, considers the interaction between Chengxin_Moody and Lianhe_Fitch with the dual rating system and the multi-rating system, constructs a new ordered Logit model, and attempts to explore the impact of the Notice, the dual rating system and the multiple rating system on the probability of Chinese corporate bond defaults, rating upgrades, rating downgrades, and the magnitude of credit rating migrations. This study compares the effectiveness of different rating regulatory systems. Using nine thousand two hundred and sixty-two data of Chinese corporate bonds as the research samples. Empirical analysis and robustness test reveal the following findings: (1) The issuance of the Notice has a significant positive effect on the implementation of both the dual rating system and the multiple rating system, with a greater impact on the implementation of the multiple rating system; (2) The issuance of the Notice, along with the dual rating system and the multiple rating system, can all reduce the probability of corporate bond defaults, with the multiple rating system showing the best preventive effect against corporate bond defaults; (3) The dual rating system is more effective in promoting rating agencies to adjust rating behaviors, accurately correcting corporate bond ratings and effectively alleviating the issue of rating inflation. Competition among rating agencies intensifies rating shopping; (4) Under the dual rating system, rating agencies are more likely to expand the magnitude of credit rating upgrades and downgrades, enhancing the differentiation between high-quality corporate bonds and junk corporate bonds; (5) The selection of rating information from Chengxin_Moody and Lianhe_Fitch can appropriately adjust the degree of flexibility or tightening in response to rating regulatory systems. Chengxin_Moody demonstrates a more sensitive reaction compared to Lianhe_Fitch regarding the rating regulatory systems. This study provides valuable references for the formulation and evaluation of the effectiveness of Chinese corporate bond rating regulatory policies. [ABSTRACT FROM AUTHOR]

This paper examines the impact of credit rating downgrade risk on stock price crash risk. Using various proxies to identify the proximity to credit rating downgrades, I find firms closer to downgrade thresholds experience greater stock price crash risk. This relationship intensifies for firms on the investment grade-speculative grade boundary. [ABSTRACT FROM AUTHOR]

Political economy literature documents how financial investors are more partial to right executives than left ones. Right cabinets face lower interest rates, less volatile stock prices and exchange rates, and higher credit ratings than left cabinets, even after accounting for fiscal differences. But does this advantage persist if right governments accommodate far right parties or ideas? I hypothesize that because far right populism can introduce political instability, markets' evaluation of right executives might deteriorate if they enter coalition with far right parties or adopt their positions. Employing a panel analysis of bond spread data, and a comparative case study of the Netherlands and Sweden, I find that right executives enjoy significantly lower spreads than their left-wing counter-parts, but this advantage disappears if they rule in coalition with the far right or produce overly right-wing manifestos. These findings highlight that right parties may encounter tangible borrowing costs and market rebuke if they accommodate far right populism. [ABSTRACT FROM AUTHOR]

In this paper, we adopt an ensemble machine learning framework—a Light Gradient Boosting Machine (LightGBM) and develop an algorithmic credit rating prediction model by innovatively incorporating firms' extra supply chain information both from suppliers and customers. By utilizing data from listed firms in North America from 2006 to 2020, our results find that the accuracy of the prediction improves by incorporating supply chain information in the previous year, compared to the inclusion of supply chain information in the current year. Besides, we identify the most important factors the stakeholders should pay attention to. Interestingly, we show that the models utilizing the current year's information perform better after the strike of the COVID-19, indicating that the epidemics may have accelerated the spread of credit risk along the supply chain. Furthermore, supplier information is found to be more valuable than customer information in predicting the focal firm's credit rating. A comparison of our framework with the existing methods vindicates the robustness of our main results. [ABSTRACT FROM AUTHOR]

Relative to sales, the average operating lease commitments of hospitality firms are 4 times larger than those of other publicly traded firms. In response to the recently enacted accounting standards update No. 2016-02 (ASU 2016-02) that requires lessees to recognize operating leases on their balance sheet, hospitality firms decreased their use of operating leases, switching to shorter-term off-balance sheet leases. We find that this change did not have negative consequences on firm performance, shareholders, or employees. The only significant effect we do find is an improvement in credit ratings for firms that reduced operating leases in response to the new standard. Our findings are inconsistent with the concerns some hospitality managers and academics expressed prior to the introduction of the standard. [ABSTRACT FROM AUTHOR]

We analyse the effect of credit supply constraints on R&D conditional on the financial strength of firms and heterogeneity in the restrictions in the supply of external financing. The financial strength of firms and access to external financing are identified by an exogenously calculated rating index. Restrictions in the supply of external financing are determined by the specific time period (crisis vs. non-crisis) and the balance sheet strength of the firm's main bank in terms of bank capital. Our results support the theoretical prediction that financing constraints negatively affect R&D. We find that firms with a lower financial strength reduce R&D to a stronger extent in times of stress on financial markets and when the firm faces restrictions in external financing. Additionally, the effect does not persist over time. [ABSTRACT FROM AUTHOR]

We investigate the association between a wide range of community-level environmental, social, and governance (ESG) outcomes and the credit risk of U.S. municipal finance fixed-income securities. We develop a novel dataset of multiple ESG outcomes for U.S. counties and connect it to a 2001-2020 panel of municipal bonds issued within those counties. Overall, we find supportive evidence that collective increases in community-level ESG factors (i.e., ESG outcomes) are associated with reductions in credit risk for U.S. municipal finance instruments over time. We theorize that these associations arise from variations in investor perceptions and manifested changes in fiscal health over time as a function of changing ESG outcomes. Post hoc analyses leveraging quasi-exogenous shocks to uncertainty, as well as connecting ESG outcomes to various measures of fiscal health at the county-year level, and credit ratings at the bond-year level, help validate this theory. Our research suggests that even socially agnostic investors should investigate the environmental and social performance of a municipality as part of their credit due diligence. [ABSTRACT FROM AUTHOR]

Do credit rating agencies (CRAs) learn from the options market? We examine this question by exploring the relation between options trading activity and credit rating accuracy. We find that as options trading volume increases, credit ratings become more responsive to expected credit risk and exhibit greater ability to predict future defaults. We also find that CRAs rely more on the options market as a source of ratings-related information when firm default risk is higher, options trading is more informative, manager-provided information is of lower quality, and firm uncertainty is higher. Our results are robust to a number of sensitivity tests, including alternative measures of options trading and credit rating accuracy. We reach similar inferences using various approaches to address endogeneity issues, including difference-in-difference analyses and an instrumental variables approach. Overall, our findings are consistent with the view that CRAs incorporate unique information from the options market into their rating decisions which, in turn, improves credit rating accuracy. This paper was accepted by Brian Bushee, accounting. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4980. [ABSTRACT FROM AUTHOR]

In this study, we suggest policy implications for managing the risk of bankruptcy in housing presale projects in Korea. In conclusion, we find that the competing risk Cox proportional hazard model (PHM) is the best model for explaining the bankruptcy factors of projects and the banks that lend construction funds and policy authorities need to manage presale projects by considering bankruptcy factors (the initial presale rate, regional factor, the credit rating of the developers, the housing common factor). This study differs from previous studies in that it uses the competing risk Cox proportional hazard model and single-index model methodologies for empirical analysis, distinguishes the causes of business failure as default and the default of repurchase agreement options that are similar to actual defaults, and suggests policy implications based on the analysis. [ABSTRACT FROM AUTHOR]

This paper responds to Professor Paolo Giudici's call for papers in 'Safe Machine Learning' and explores the application of Bayesian inference within the SAFE model framework, aiming to enhance the accuracy and reliability of environmental, social, and governance (ESG) factor analysis in the financial sector. The paper begins by introducing the basic concept of the SAFE model, which integrates ESG factors into the assessment of corporate credit ratings to promote sustainable development in the financial field. Furthermore, this paper discusses forecast estimation, uncertainty quantification, Gaussian processes, iterative optimization, and model robustness within the SAFE model framework. It is important to note that the SAFE model is not limited to the aforementioned applications; it also has the capacity to understand the model's extensive utility in other financial sectors, which can reflect the model's comprehensive scope. [ABSTRACT FROM AUTHOR]

According to the uncertain geographic context problem, a lack of temporal information can hinder measures of bias in mortgage lending. This study extends previous methods to: (1) measure the persistence of racial bias in mortgage lending for Black Americans by adding temporal trends and credit scores, and (2) evaluate the continuity of bias in discriminatory areas from 1990 to 2020. These additions create an indicator of persistent structural housing discrimination. We studied the Boston-Cambridge-Newton and Dallas-Fort Worth metropolitan statistical areas to examine distinct historical trajectories and urban development. We estimated the odds of mortgage denial for census tracts. Overall, all tracts in Boston-Cambridge-Newton (N = 1003) and Dallas-Fort Worth (N = 1312) displayed significant change, with greater odds of bias over time in Dallas-Fort Worth and lower odds in Boston-Cambridge-Newton. Historically redlined areas displayed the strongest persistence of bias. Results suggest that temporal data can identify persistence and improve sensitivity in measuring neighborhood bias. Understanding the temporality of residential exposure can increase research rigor and inform policy to reduce the health effects of racial bias. [ABSTRACT FROM AUTHOR]

With the emergence of Fintech lending, small firms can benefit from new channels of financing. In this setting, the creditworthiness and the decision to extend credit are often based on standardized and advanced machine-learning techniques that employ limited information. This paper investigates the ability of machine learning to correctly predict credit risk ratings for small firms. By employing a unique proprietary dataset on invoice lending activities, this paper shows that machine learning techniques overperform traditional techniques, such as probit, when the set of information available to lenders is limited. This paper contributes to the understanding of the reliability of advanced credit scoring techniques in the lending process to small businesses, making it a special interesting case for the Fintech environment. [ABSTRACT FROM AUTHOR]

School districts in the United States often borrow on the municipal bond market to pay for capital projects. Districts serving economically disadvantaged communities tend to receive lower credit ratings and pay higher interest rates. To remedy this problem, twenty-four states have established credit enhancement programs that promise to repay district debt when a district cannot do so, thereby enhancing the district's credit rating. With a generalized difference-in-differences approach, I rely on cross- and within-district variations to estimate the effect of state enhancement on district bond interest rate, per-pupil capital spending, and student performance. State enhancement reduces district bond interest rates by 6 percent and increases per-student capital spending by 2 percent to 7 percent. It also reduces the disparity in the interest rate and capital spending across districts serving lower- and higher-income families, with no discernible effect on test scores. I find no evidence that the amount of enhanced school debt is associated with significant changes in interest rates paid by state governments. Districts in states without such programs could have achieved cost savings in the range of $383 million to $1 billion from 2009 to 2019 had the states adopted similar programs. [ABSTRACT FROM AUTHOR]

Environmental credit evaluation is essential for the establishment of an environmental financial system, but most studies have focused on the impact of green financial instruments such as green credit and green bonds, resulting in insufficient attention to this core aspect. This study examines the impact of environmental credit evaluation on firm ESG performance using a staggered difference‐in‐differences model based on China's environmental credit rating policy. By analyzing data from 2010 to 2020 on 1018 publicly listed firms in high‐pollution industries in China, this study finds that the implementation of the environmental credit rating policy significantly improves ESG performance. This positive impact is realized through enhancing environmental information transparency, strengthening external supervision, and alleviating financing constraints. [ABSTRACT FROM AUTHOR]

Small Finance Banks (SFBs) are specialised banking institutions incorporated to serve the unbanked, small and underserved customers. Their primary motive is to improve financial access by providing access to essential financial services. This article examines the performance of ten SFBs in India over four years. The CAMELS model has been applied for performance analysis, which provides performance and ranking of various SFBs using the model’s six parameters: Capital adequacy, Asset quality, Management, Earning quality, Liquidity and Sensitivity. This study will help identify SFBs in financial distress, which will be helpful in timely action for their revival. Financial analysts and credit rating agencies can also use the comparative performance of these banks to frame their opinion or ratings. The common public, including customers and investors, can frame their short-term and long-term investment decisions based on the performance and ranking of these institutions. Furthermore, a proper assessment of these banks is needed as their good performance can ensure that formal credit availability reaches the grassroots level. [ABSTRACT FROM AUTHOR]

Introduction: Enhancing the efficacy of online food safety supervision requires thoughtful selection and efficient application of regulatory measures. Methods: This study examined the current state of online food safety supervision in the important food producing city in central China of Zhengzhou. The effectiveness of supervision frequency and penalty on food safety governance of online catering was examined through model construction based on the optimal law enforcement theory. The efficacy of monitoring was evaluated using real supervision and punishment data from the online catering sector in a Chinese new first-tier city. Results and discussion: Results showed that although high-frequency and high-penalty supervision are two common methods of online food safety governance, the deterrent effect of high-frequency supervision on online food businesses is more significant for improving the supervision efficiency of the online catering market. Simultaneously, bolstering the education of caterers and food operators as well as raising their degree of training are also efficient ways to raise the efficacy of government oversight. The application of law enforcement economics is broadened in this study, which also has implication for the advancement of credit rating and classification supervision in the area of food safety for online catering. [ABSTRACT FROM AUTHOR]

In recent years, rapid technological advancements have propelled blockchain and artificial intelligence (AI) into prominent roles within the digital industry, each having unique applications. Blockchain, recognized for its secure and transparent data storage, and AI, a powerful tool for data analysis and decision making, exhibit common features that render them complementary. At the same time, machine learning has become a robust and influential technology, adopted by many companies to address non-trivial technical problems. This adoption is fueled by the vast amounts of data generated and utilized in daily operations. An intriguing intersection of blockchain and AI occurs in the realm of federated learning, a distributed approach allowing multiple parties to collaboratively train a shared model without centralizing data. This paper presents a decentralized platform FBLearn for the implementation of federated learning in blockchain, which enables us to harness the benefits of federated learning without the necessity of exchanging sensitive customer or product data, thereby fostering trustless collaboration. As the decentralized blockchain network is introduced in the distributed model training to replace the centralized server, global model aggregation approaches have to be utilized. This paper investigates several techniques for model aggregation based on the local model average and ensemble using either local or globally distributed validation data for model evaluation. The suggested aggregation approaches are experimentally evaluated based on two use cases of the FBLearn platform: credit risk scoring using a random forest classifier and credit card fraud detection using a logistic regression. The experimental results confirm that the suggested adaptive weight calculation and ensemble techniques based on the quality of local training data enhance the robustness of the global model. The performance evaluation metrics and ROC curves prove that the aggregation strategies successfully isolate the influence of the low-quality models on the final model. The proposed system's ability to outperform models created with separate datasets underscores its potential to enhance collaborative efforts and to improve the accuracy of the final global model compared to each of the local models. Integrating blockchain and federated learning presents a forward-looking approach to data collaboration while addressing privacy concerns. [ABSTRACT FROM AUTHOR]

Based on a comprehensive dataset of Chinese A-share stocks, we find significant evidence for analysts' anchoring in China. This anchoring behavior exists only when forecasts are lower than the industry median and correlates to a series of analysts' and corporate characteristics. The following market reactions to analyst anchoring are discontinuous: only forecasts without analysts' anchoring achieve higher and positive returns in the next period, while those with anchoring bias have no influence on the following returns. Further analyses based on the earnings response coefficient reveal that analysts' anchoring inhibits earnings information dissemination, and discontinuous stock performance is mainly from professional institutions. Overall, this paper provides evident results about analyst anchoring and its impact, and the findings are beneficial for investors to efficiently use analysts' forecast information. [ABSTRACT FROM AUTHOR]

Using data from 360 smallholder farmers in Southeast Nigeria, the study creates the architecture for a new farmer's hybrid credit rating system used in classifying farmers who applied for microfinance loans based on their creditworthiness. We discovered new evidence that the hybridized credit scoring algorithm demonstrated unprecedented concordance in assessing the financial viability of farmers along gender lines. The discriminant analysis, in particular, closely aligned with the credit score model, with 34.4% and 46.7% of male and female farmers grouped as creditworthy, reflecting the model's estimates of 33.3% and 45.5%, indicating gaps of 12.3% and 12.2%, respectively, to the advantage of the female farmers. Our findings further suggest that annual income, marital status, and farm size strongly influence the separation between creditworthy and non-creditworthy farmers. While age, loan term, and a history of defaults had a negative impact on discrimination, in light of the findings, we recommend a collaboration between authorities, financial institutions, and extension workers in offering tailored trainings to both male and female farmers, assisting them in meeting up-to-date credit prerequisites, adopting modified farming techniques, and improving their general preparedness to be accepted for loans in this changing credit evaluation landscape so as to bridge the disparity and promote financial inclusion for farmers irrespective of gender affiliations. [ABSTRACT FROM AUTHOR]

This study aims to examine the development of the professionalism of madrasah supervisors in order to improve the quality of madrasahs. The method used is a qualitative approach with a descriptive method, where data is collected through interviews, observations, and documentation. The data analysis technique involves data reduction, data presentation, and conclusion drawing. The results of this study reveal several key points: 1) The planning of supervisor development is carried out through recruitment based on administrative, competence, and qualification requirements, including the preparation of scientific papers and meeting credit score standards; 2) The implementation is conducted through supervision programs, which include the development of teacher competencies, monitoring graduate competency standards, content standards, process standards, and assessment standards, as well as teacher performance evaluations; 3) The evaluation of professional development has been continuously performed in accordance with the planned objectives and the established assessment standards; 4) Follow-up actions are conducted both individually and in groups; and 5) The design of this research is called the "Madrasah Supervisor Professional Development Model," which will be implemented as a program for the continuous improvement of madrasah quality. This model is expected to significantly contribute to enhancing the professionalism of madrasah supervisors and, in turn, elevate the overall standards of education within the madrasah system. [ABSTRACT FROM AUTHOR]

Green credit financing is closely related to corporate credit rating, so conducting in-depth research on the differentiation of green credit has practical significance. For the three-stage supply chain system composed of risk avoidance suppliers, retailers and banks, suppliers face a capital gap to produce green products to meet market demand. Based on Stackelberg game theory, this paper establishes a bank green credit and mixed financing model to study the best financing strategy of suppliers under differentiated green credit. Research shows that credit rating is not always positively correlated with product greenness, and it is important for enterprises to improve their own credit rating; the higher the degree of suppliers' risk avoidance, the lower their utility profit; only when the green sensitivity coefficient is low, the conclusion that retailers are more willing to choose to cooperate with suppliers with high credit rating is inevitable; banks providing loans to suppliers with middle credit rating can maximize their profits, and providing loans to suppliers with high credit rating can better stimulate green production. [ABSTRACT FROM AUTHOR]

Few studies have explored the impact of heterogeneous government investment on corporate bond ratings from the perspective of government social capital allocation. This paper examines the bond market's response to government social capital allocation, utilizing empirical data from the bond ratings of listed companies issued between 2008 and 2020. The analysis begins with a rational explanation, conceptual definition, and index construction of government social capital. The empirical research finds that government social capital allocation significantly enhances corporate credit ratings. Mechanism tests reveal that local governments provide "government support" signals through short-term and long-term borrowing, enabling firms to secure more long-term loans and reduce their default risk. These signals are captured by rating agencies, which then influence the rating outcomes. Heterogeneity tests indicate that the positive impact of government capital allocation on ratings is more pronounced during periods of corporate financial distress. Furthermore, foreign rating agencies are better able to capture signals of government social capital allocation, and their independence confirms that high ratings are not the result of rating inflation. [ABSTRACT FROM AUTHOR]

This study analyzes a compensation reform proposal designed to prevent inaccurate ratings produced by credit rating agencies (CRAs). Specifically, the CRA's incentive to exert effort to observe the portfolio's signal and adopt the rating disclosure policy is investigated under the rating-contingent and incentive-based contract. The issuer has a risky portfolio and solicits a rating from the CRA, which endogenously observes a signal and decides on a rating disclosure policy during the rating production process. The findings reveal that the CRA exerts no effort to observe a signal and inflate the rating under the rating contingent contract. Under the incentive-based contract, the CRA always exerts an optimal effort to observe a signal and adopt the full disclosure regime. Hence, the incentive-based contract better incentivizes the CRA to exert more effort to improve rating accuracy and implement the full disclosure policy than the rating contingent contract. [ABSTRACT FROM AUTHOR]

Psychometric-based credit scores measure important personality traits that are characteristic of good borrowers' behaviors. While such data can potentially improve credit models for underbanked consumers, the utility of psychometric data in consumer lending is still largely understudied. The present study contributes to the literature in this respect, as it is one of the first studies to evaluate the efficacy of psychometric-based credit scores for predicting future loan defaults among underbanked consumers. The results from two culturally diverse samples of loan applicants (Sub-Saharan Africa, n = 1113; Western Europe, n = 1033) found that psychometric scores correlated significantly with future loan defaults (Gini = 0.28–0.31) and were incrementally valid above and beyond the banks' own credit scorecards. These results highlight the theoretical basis for personality in financial behaviors, as well as the practical utility that psychometric scores can have for credit decisioning in general and the facilitation of financial inclusion for underbanked consumer groups in particular. [ABSTRACT FROM AUTHOR]

Over the past years, machine learning emerged as a powerful tool for credit scoring, producing high-quality results compared to traditional statistical methods. However, literature shows that statistical methods are still being used because they still perform and can be interpretable compared to neural network models, considered to be black boxes. This study compares the predictive power of logistic regression and multilayer perceptron algorithms on two credit-risk datasets by applying the Local Interpretable Model-Agnostic Explanations (LIME) explainability technique. Our results show that multilayer perceptron outperforms logistic regression in terms of balanced accuracy, Matthews Correlation Coefficient, and F1 score. Based on our findings from LIME, building models on imbalanced datasets results in biased predictions towards the majority class. Model developers in the field of finance could consider explanation methods such as LIME to extend the use of deep learning models to help them make well-informed decisions. [ABSTRACT FROM AUTHOR]

While the production of credit ratings has long been limited mainly to rating agencies (CRAs), recent years have seen the growing popularity of consensus credit ratings crowdsourced from banks (i.e., bank ratings). We provide the first comprehensive examination of the properties and informativeness of bank ratings relative to CRA ratings. We find that bank ratings often deviate from CRA ratings, with over 60% of firm-months having different bank and CRA ratings. These deviations contain useful information. Bank ratings improve out-of-sample prediction of defaults and CRA rating revisions and explain the cross-section of credit spreads. However, bank ratings do not improve out-of-sample prediction of credit excess returns, indicating that current prices incorporate bank rating information. Overall our findings suggest that bank ratings are a useful supplement to traditional credit ratings. [ABSTRACT FROM AUTHOR]

Swift and unexpected shifts of financial regulations can have profound implications for the general population. This is evidenced by China's abrupt transition in its stance on P2P lending in 2018. Initially embracing these platforms, the abrupt regulatory pivot to widespread shutdowns. Our empirical research, drawing upon credit application data, demonstrates how this indiscriminate approach hindered economic development opportunities for a significant portion of borrowers, particularly the underprivileged. As a remedy, we advocate for the implementation of AI-driven regulatory frameworks. Rather than a monolithic approach to all borrowers, AI helps distinguish between real financial risks and those that can be managed. This nuanced strategy safeguards individuals' economic progression, while efficiently mitigating financial hazards. For policymakers and industry stakeholders, our findings underscore the importance of contemplating the broader ramifications of regulatory decisions and harnessing innovative methodologies, such as AI, to strike an optimal balance. Financial regulators often focus on containing risks in financial services; however, they may not simultaneously pay adequate attention to regulation's adverse effects. This study examines how the economic development of borrowers was affected by China's suppressive regulation of peer-to-peer (P2P) lending in 2018, which unexpectedly switched from an "all-in" policy to an "all-shutdown" policy, leading to a massive closure of P2P lending companies and the eventual shutdown of the entire industry by 2021. Leveraging data on individuals' credit applications, we show that this one-size-fits-all regulation obstructed borrowers' economic development potential, especially for underprivileged and underserved borrowers, as reflected by their credit scores and their selection of financial channels. To alleviate the unintended adverse effects, we advocate using artificial intelligence (AI) to stipulate personalized regulation as a PolicyTech solution. We demonstrate that by restricting some borrowers' access to P2P lending according to their AI-predicted financial risk, it is possible to protect borrowers' overall economic development opportunity, while containing credit risks. This work yields significant theoretical and societal implications. History: Olivia Sheng, Senior Editor; Huaxia Rui, Associate Editor. Funding: Financial support from the National Natural Science Foundation of China [Grant 71831006], the Research Grant Council Hong Kong [Grants GRF 11501722 and 11500519], the InnoHK initiative, the Government of the Hong Kong Special Administrative Region, and the Laboratory for AI-Powered Financial Technologies is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2021.0580. [ABSTRACT FROM AUTHOR]

The text discusses the concept of continuous auditing, which involves ongoing evaluations of risk factors and controls in retail lending businesses, particularly credit card lending. It highlights the importance of excluding delinquent borrowers from credit line increases and identifies risks associated with inactive borrowers, recent limit changes, and debt restrictions. The text introduces a four-step process that uses a decision tree algorithm to identify high-risk borrowers. It recommends the deployment of continuous auditing to ensure timely updates and collaboration between internal audit and other functions. The text also emphasizes the need for auditors to develop digital trust skills to remain effective in credit line management. [Extracted from the article]

Credit scorecards are essential tools for banks to assess the creditworthiness of loan applicants. While advanced machine learning models like XGBoost and random forest often outperform traditional logistic regression in predictive accuracy, their lack of interpretability hinders their adoption in practice. This study bridges the gap between research and practice by developing a novel framework for constructing interpretable credit scorecards using Shapley values. We apply this framework to two credit datasets, discretizing numerical variables and utilizing one-hot encoding to facilitate model development. Shapley values are then employed to derive credit scores for each predictor variable group in XGBoost, random forest, LightGBM, and CatBoost models. Our results demonstrate that this approach yields credit scorecards with interpretability comparable to logistic regression while maintaining superior predictive accuracy. This framework offers a practical and effective solution for credit practitioners seeking to leverage the power of advanced models without sacrificing transparency and regulatory compliance. [ABSTRACT FROM AUTHOR]

This paper integrates the credit rating agency and domestic conflict literatures, investigating the effects of non-violent and violent domestic political unrest on sovereign bond ratings. Using up to 60 developing countries and 94 unrest cases from 1996-2018, we find that while countries under domestic unrest often receive bond downgrades, non-violent unrest appears not to be responsible. Further, we use mediation analysis and show that respect for the rule of law and economic stability seem to mediate the relationship between violent and non-violent unrest and bond ratings. Given developing countries' need to issue debt, and the critical role credit rating agencies play in rating sovereign bonds, our work suggests that countries should seek to avoid violent domestic political unrest if for no other reason than to acquire lower-cost capital. [ABSTRACT FROM AUTHOR]

The history of Credit Rating Agencies [CRAs], commonly called Rating Agencies, has a long and distinguished trajectory marked by influence, reputation and power. Due to the ability of this field to instigate significant changes in market regulations and actions of economic actors, this subject is extensively debated within the literature. In economic sociology, while some studies have focused on perceptions of performativity and market devices to understand how the calculability of its methods influences the economy, others, along relational lines of sociology, aim to understand them through a more complex constructive process. In this study, we attempt to fill a gap by exploring the complementarity between two theoretical approaches to sociology: theory of fields and performativity. Thus, we mobilize theory of fields to understand the inter-field relationship between the State and CRAs. Specifically, this paper sets out to i) analyze the co-constitution of the state and market fields with the field of CRAs; ii) describe the existing regulation and situate the CRAs and their classification devices in the broader history of the State and the market; iii) analyze how the legitimation of the CRAs by the State generates broader social contexts that assist in the performative work of the CRAs. Based on historical documentary research, we analyze some of the main milestones and regulatory events in the field of CRAs. This research reaffirms the assumption that even after credibility crises, the State is the most significant predictor of legitimacy, power, and influence that CRAs hold to date. The legitimacy of this power and influence has yet to be exhausted because the reputation of CRAs is rooted in the reputation of the State itself. This happens because the State provides essential actions (such as regulations) for the existence of the financial market. Thus, results suggest that the State, through regulatory measures, can engage several other fields to promote the legitimacy of performative work of the CRAs, whose purpose is to provide calculation tools to shape markets. [ABSTRACT FROM AUTHOR]

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Research limitations/implications: 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. Practical implications: 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. Originality/value: 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. [ABSTRACT FROM AUTHOR]

We study an economy with financial frictions in which a regulator designs a test that reveals outside information about a firm's quality to investors. The firm can also disclose verifiable inside information about its quality. We show that the regulator optimally aims for "public speech and private silence," which is achieved with tests that give insiders an incentive to stay quiet. We fully characterize optimal tests by developing tools for Bayesian persuasion with incentive constraints, and use these results to derive novel guidance for the design of bank stress tests, as well as benchmarks for socially optimal corporate credit ratings. [ABSTRACT FROM AUTHOR]

We investigate the impact of sovereign wealth funds (SWFs) equity ownership on bonds' credit ratings of their target firms. Using a sample of 2045 bonds issued by 324 SWF target firms from 16 countries over the period 1996–2020, we find evidence linking SWF investments to lower likelihood of bond rating upgrades. Consistent with value‐reducing political agenda hypothesis, our results suggest that credit rating agencies perceive SWFs as a structure that could affect the quality of corporate governance and harm bondholder interests by leaving them vulnerable to losses. Our results also show that credit rating could be improved: (i) with SWF transparency and experience; (ii) when SWFs take a more passive investment stance; and (iii) within the financial crisis period. Finally, and interestingly, using generalized structural equation modelling, we provide evidence supporting the mediating role of target firm's corporate governance quality in the relationship between SWF investments and bond ratings. Our findings are robust to controls for the endogeneity and heteroscedasticity issues and to alternative sample compositions and regression frameworks. [ABSTRACT FROM AUTHOR]