Search

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]

Existing studies indicated that firm debt holders can use the credit default swap (CDS) market to hedge their credit risk, and thus they would reduce their monitoring of the firms, leading to largely distressed firms shirking and increasing positive abnormal earnings accruals. Besides providing insurance, however, the CDS spreads also perform price discovery of credit risk information sought by trade creditors and potential lenders who are not protected. High absolute abnormal discretionary accruals or bad earnings quality, especially negative abnormal accruals, would lead adverse CDS price signals that are very costly to the firm. This compels the firm under nondistressed conditions to be able to improve cash holdings, cash flows, working capital, and earnings reporting quality. Our new results indicate that the channels of improvement in earnings quality are through a firm's large accounts payable and low cash holdings related to trade credit exposures. In the longer run, this leads to higher profitability and improved firm value. Thus, the generation of public information via the CDS market reduces information asymmetry and can enforce greater discipline in discretionary accounts reporting. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The importance of pursuing financial inclusion to accelerate economic growth and enhance financial sustainability has been well noted. However, studies have provided few actionable insights into how financial institutions can balance the potential socioeconomic trade-off between profitability and equality. One major challenge arises from a lack of understanding of the impacts of various types of market information available on financial equality beyond economic profitability. Another challenge lies in how the socioeconomic trade-off under a large set of counterfactual policies in a real-world setting can be evaluated. Our motivation for the present study was the emerging sources of digitized user-behavior data (i.e., "alternative data") stemming from the high penetration of mobile devices and internet access. Accordingly, we investigated how alternative data from smartphones and social media can help mitigate potential financial inequality while preserving business profitability in the context of financial credit risk assessment. We partnered with a leading microloan website to design a novel "meta" experiment that allowed us to simulate various real-world field experiments under an exhaustive set of counterfactual policies. Interestingly, we found that profiling user financial risk using smartphone activities is 1.3 times more effective in improving financial inclusion than using online social media information (23.05% better vs. 18.11%), and nearly 1.3 times more effective in improving business profitability (42% better vs. 33%). Surprisingly, we found that using consumers' online shopping activities for credit risk profiling can hurt financial inclusion. Furthermore, we investigated potential explanations for financial inclusion improvements. Our findings suggest that alternative data, especially users' smartphone activities, not only demonstrate higher ubiquity but also appear to be more orthogonal to conventional sensitive demographic attributes. This, in turn, can help mitigate statistical bias driven by the unobserved factors or underrepresentative training samples in machine-based risk assessment processes. [ABSTRACT FROM AUTHOR]

With the rapid development of fintech, the need for dynamic credit risk evaluation is becoming increasingly important. While previous studies on credit scoring have mostly focused on single-period loan default prediction, we call for a new avenue--multiperiod default prediction (MPDP)--to depict risk profiles over time. To address the challenges raised by MPDP, such as monotonic default probability prediction and complex relationship accommodation, we propose a novel approach, hybrid and collective scoring (HACS). We design a hybrid modeling strategy to predict whether and when a borrower will default separately through a default discrimination model and a default time estimation model, respectively, and synthesize them through a probabilistic framework. To accommodate various possible patterns of default time and measure the distribution of default probability over successive time intervals, we propose a joint default modeling method to train the default time estimation model. Empirical evaluations at the model (time-to-default prediction performance and discrimination performance) and mechanism (identifiability and discriminability) levels, as well as impact analyses at the application (granting performance and profitability performance) level, show that HACS outperforms the benchmarked survival analysis and multilabel learning methods on all fronts. It can more accurately predict time-to-default and provide financial institutions and investors better decisionsupport in granting loans and selecting loan portfolios. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

Models developed by banks to forecast losses in their credit card portfolios have generally performed poorly during the COVID‐19 pandemic, particularly in 2020, when large forecast errors were observed at many banks. In this study, we attempt to understand the source of this error and explore ways to improve model fit. We use account‐level monthly performance data from the largest credit card banks in the U.S. between 2008 and 2018 to build models that mimic the typical model design employed by large banks to forecast credit card losses. We then fit these on data from 2019 to 2021. We find that COVID‐period model errors can be reduced significantly through two simple modifications: (1) including measures of the macroeconomic environment beyond indicators of the labor market, which served as the primary macro drivers used in many pre‐pandemic models and (2) adjusting macro drivers to capture persistent/sustained changes, as opposed to temporary volatility in these variables. These model improvements, we find, can be achieved without a significant reduction in model performance for the pre‐COVID period, including the Great Recession. Moreover, in broadening the set of macro influences and capturing sustained changes, we believe models can be made more robust to future downturns, which may bear little resemblance to past recessions. [ABSTRACT FROM AUTHOR]

This paper uses a generalised stacking method to introduce a novel hybrid model that combines a one-dimensional convolutional neural network 1DCNN with extreme gradient boosting XGBoost. We compared the predictive accuracies of the proposed hybrid architecture with three conventional algorithms-1DCNN, XGBoost and logistic regression (LR) using a dataset of over twenty thousand peer-to-peer (P2P) consumer credit observations. By leveraging the SHAP algorithm, the research provides a detailed analysis of feature importance, contributing to the model's predictions and offering insights into the overall and individual significance of different features. The findings demonstrate that the hybrid model outperforms the LR, XGBoost and 1DCNN models in terms of classification accuracy. Furthermore, the research addresses concern regarding fairness and bias by showing that removing potentially discriminatory features, such as age and gender, does not significantly impact the hybrid model's classification capabilities. This suggests that fair and unbiased credit scoring models can achieve high effectiveness levels without compromising accuracy. This paper makes significant contributions to academic research and practical applications in credit risk management by presenting a hybrid model that offers superior classification accuracy and promotes interpretability using the model agnostic SHAP framework. [ABSTRACT FROM AUTHOR]

This study proposes a Privacy-Preserving Deep Neural Network for Credit Risk Prediction (PPDNN-CRP) framework that leverages homomorphic encryption (HE) to ensure data privacy throughout the credit risk prediction process. The PPDNN-CRP framework employs the Paillier homomorphic encryption scheme to secure sensitive loan application data during both the training and inference phases. Implemented using TensorFlow for deep neural network operations and TenSEAL for HE, the framework uses the Kaggle loan dataset to evaluate its performance. The results show that PPDNN-CRP achieved an accuracy of 80.48%, demonstrating competitive performance compared to Privacy-Preserving Logistic Regression (PPLR) at 77.23% and a slight decrease from the non-private DNN-CRP model at 86.18%. The model exhibited strong metrics with a precision of 0.84, recall of 0.91, F1-score of 0.87, and an AUC of 0.74. Security analysis confirms that PPDNN-CRP effectively defends against various privacy attacks, including poisoning, evasion, membership inference, model inversion, and model extraction, through robust encryption techniques and privacy measures. This framework offers a promising approach for achieving high-quality credit risk prediction while maintaining data privacy and complying with legal and ethical standards. [ABSTRACT FROM AUTHOR]

With the development of financial technology, the traditional experience-based and single-network credit default prediction model can no longer meet the current needs. This manuscript proposes a credit default prediction model based on TabNeT-Stacking. First, use the PyTorch deep learning framework to construct an improved TabNet structure. The multi-population genetic algorithm is used to optimize the Attention Transformer automatic feature selection module. The particle swarm algorithm is used to optimize the hyperparameter selection and achieve automatic parameter search. Finally, Stacking ensemble learning is used, and the improved TabNet is used to extract features. XGBoost (eXtreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), CatBoost (Category Boosting), KNN (K-NearestNeighbor), and SVM (Support Vector Machine) are selected as the first-layer base learners, and XGBoost is used as the second-layer meta-learner. The experimental results show that compared with original models, the credit default prediction model proposed in this manuscript outperforms the comparison models in terms of accuracy, precision, recall, F1 score, and AUC (Area Under the Curve) of credit default prediction results. [ABSTRACT FROM AUTHOR]

We explore the potential of machine learning (ML) models applied in two financial risk management areas, i.e., credit risk management and financial risk hedging, through two practical use cases. This comparative study starts with the issue of explainability in complex ML models used in peer-to-peer lending for credit risk management. The first use case examines the limitations of using Kernel-SHAP with dependent features and evaluates different methods for estimating these dependencies using the Lending Club dataset. Our results suggest that accounting for feature dependence improves the understanding and robustness of prediction explanations. The second use case investigates a dynamic method for hedging foreign exchange risk in international equity portfolios, emphasizing the importance of accurate forecasts of currency returns. The analysis demonstrates that predictions yielded by ML models can significantly enhance the hedging of portfolios against currency risk. These findings highlight the transformative potential of advanced ML models in financial risk management, showcasing their capability to improve financial risk measurement and management. Further, our study outlines future research directions to advance this field. [ABSTRACT FROM AUTHOR]

This study examines received and transmitted volatility spillovers of Credit Default Swap (CDS) and Asset-Swap Spread (ASW) for Brazil and Turkey. The empirical analysis is implemented using two country-based (stock markets and exchange rates) and two global (volatility index and global economic activity index) variables to account for the impact of integration into global markets. Empirical results suggest that both countries display distinctive features in their spillover networks. While exchange rates and the stock market figure prominently in Brazil as a source of spillovers, for Turkey, the primary element in spillovers appears to be credit risk indicators. Time-varying analysis results show that the European Debt Crisis of 2010-2011 and the global liquidity crunch of 2018-2019 are two critical periods in volatility spillovers that occurred toward credit risk indicators. Brazil displays more sensitivity to the developments of the pandemic than Turkey, likely due to its dependence on global economic activity and energy prices. Finally, for both countries, the leading variable in spillovers to credit risk indicators during financial turbulence episodes appears to be foreign exchange markets. This result highlights both economies’ fragility and vulnerability to foreign exchange market-based shocks. Thus, we suggest effective and solid measures in this regard. Otherwise, those shocks could potentially induce a higher cost of financing in both economies due to the negative impacts on CDS and ASW spreads. [ABSTRACT FROM AUTHOR]

This study explores the influence of risk disclosure levels and types on the readability of annual reports of non-financial firms in the UK during the COVID-19 outbreak. It further investigates how the disclosure of COVID-19-related information moderates the relationship between risk disclosure and readability. The study uses a content analysis approach and CFIE software to measure the level of risk disclosure and readability in the annual reports of non-financial firms listed on the FTSE all-share from 2019 to 2021. The results show a positive and significant effect of risk disclosure level on readability, which is stronger for firms that disclosed COVID-19 information. Different types of risk disclosure have varying effects on readability, with COVID-19 risk, credit risk, and strategic risk positively affecting readability, while operational risk negatively affects it. The study contributes to the literature on information asymmetry and institutional theory by demonstrating how risk disclosure and readability are influenced by external factors like the COVID-19 outbreak and internal factors such as firm characteristics and types of risks. It introduces a new risk definition and category specific to the COVID-19 pandemic and develops new measurements for risk disclosure, including credit, liquidity, market, operational, business, strategic, and COVID-19 risks. The study provides valuable insights for managers, investors, regulators, and standard setters on the relationship between risk disclosure and readability in annual reports. It highlights the importance of disclosing COVID-19-related information to enhance the readability and understandability of financial communication. The paper contributes to the literature and practice on risk disclosure, readability, and financial communication during crises. [ABSTRACT FROM AUTHOR]

This paper proposes an explicable early warning machine learning model for predicting financial distress, which generalizes across listed Nordic corporations. We develop a novel dataset, covering the period from Q1 2001 to Q2 2022, in which we combine idiosyncratic quarterly financial statement data, information from financial markets, and indicators of macroeconomic trends. The preferred LightGBM model, whose features are selected by applying explainable artificial intelligence, outperforms the benchmark models by a notable margin across evaluation metrics. We find that features related to liquidity, solvency, and size are highly important indicators of financial health and thus crucial variables for forecasting financial distress. Furthermore, we show that explicitly accounting for seasonality, in combination with entity, market, and macro information, improves model performance. [ABSTRACT FROM AUTHOR]

The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are more accessible than ever, but it has been challenging to create and implement systems that support real-world financial applications, primarily due to their lack of transparency and explainability—both of which are essential for building trustworthy technology. The novelty of this study lies in the development of an explainable AI (XAI) model that not only addresses these transparency concerns but also serves as a tool for policy development in credit risk management. By offering a clear understanding of the underlying factors influencing AI predictions, the proposed model can assist regulators and financial institutions in shaping data-driven policies, ensuring fairness, and enhancing trust. This study proposes an explainable AI model for credit risk management, specifically aimed at quantifying the risks associated with credit borrowing through peer-to-peer lending platforms. The model leverages Shapley values to generate AI predictions based on key explanatory variables. The decision tree and random forest models achieved the highest accuracy levels of 0.89 and 0.93, respectively. The model's performance was further tested using a larger dataset, where it maintained stable accuracy levels, with the decision tree and random forest models reaching accuracies of 0.90 and 0.93, respectively. To ensure reliable explainable AI (XAI) modeling, these models were chosen due to the binary classification nature of the problem. LIME and SHAP were employed to present the XAI models as both local and global surrogates. [ABSTRACT FROM AUTHOR]

Using a unique data set of Italian banks covering the 2011 to 2019 period, this study investigates the influence of human resource training on bank stability and examines whether this relationship is affected by bank efficiency and credit risk. In line with the resource‐based view (RBV) of the firm, our findings suggest that employee training exerts a positive impact on bank stability. Credit risk and bank efficiency are also identified as potential drivers in this relationship. Importantly, our results remain robust when addressing endogeneity issues and considering alternative model specifications. This study offers novel insights into the impact of employee‐related variables on bank performance. The practical implications of our findings hold relevance for both banks and regulatory authorities, as human resource training can profoundly influence the effectiveness of risk management strategies and, ultimately, the sound and prudent management of banks. [ABSTRACT FROM AUTHOR]

We implement a two‐stage methodology based on the structural vector autoregressive and time‐varying parameter vector autoregressive models to examine the time‐varying effect of distinct types of oil‐price shocks on sovereign credit risks measured by credit default swap (CDS) spreads in Gulf Cooperation Council countries. Using monthly data for the period from May 2011 to February 2022, our results show time‐varying responses to structural oil shocks in the short‐ and medium‐run periods, with more fluctuations in responses detected over the full sample period in the former. Overall, we detect a break in the contagious impacts of oil shocks during and in the aftermath of, the 2014–2015 oil crisis and COVID‐19 crisis. Specifically, the Bahraini market is found to exhibit a positive (negative) reaction to the oil supply shocks (OS) and oil market‐specific demand shocks (OSD) throughout the pandemic period. Furthermore, we uncover a transient response from the Saudi and Qatari CDS spreads to the aggregate demand shocks (ADS) and the OSD over the full sample period, indicating the need for portfolio rebalancing. In the UAE, we detect a positive impact over the three sampled years of OSD since 2011. Moreover, a notable decoupling pattern continues to appear between short‐ and medium‐term innovations in the ADS. Our results suggest adopting more conservative trading in the CDS markets while understanding the oil price and the economic state. The complexity of the trading strategy should also depend on the target Gulf market itself and that seems essential when it comes to investing in Qatar and Saudi Arabia. [ABSTRACT FROM AUTHOR]

The income smoothing (IS) literature connected with changes in the external environment provides unclear results whether this phenomenon occurs in expansion or in crisis. Given that a substantial amount of credit risk is generated in retail banking, this study exploits a large number of micro‐loan portfolio level data of a systemic Greek bank and examines, for the first time, the IS behaviour at the retail banking level, contrasting the expansion period (2006–2008) with the financial crisis period (2009–2011). This specific case study provides more granular data compared with traditional archival studies, thus allowing the immediate and more frequent identification of exogenous (such as crisis effects) and endogenous (such as the geographic origination of loan portfolios) features that may affect IS practices. Based on the association between pre‐provision loan income and loans loss provisions as a proxy for IS, we employ the robust Mean Group estimator (Pesaran and Smith, Journal of Econometrics, 1995;68(1):79–113) and find that this association is higher in expansionary periods and declines in recession. Moreover, we conclude that the IS behaviour is affected by geographical features of loan portfolios. [ABSTRACT FROM AUTHOR]

This paper focuses on utilizing non‐performing loans (NPLs) as the primary indicator of credit risk to analyze how various bank and country‐level characteristics influence changes in credit risk within and between banks across China, Europe, and the U.S. during the COVID‐19 period. Over 4,959 bank‐quarter observations (from Q4 2019 to Q4 2021), it becomes evident that COVID‐19 significantly contributes to the variation in NPLs, underscoring its adverse impact on credit risk. This pattern is consistent across all countries; however, Chinese banks exhibit more robust capabilities in managing credit risk exposure compared to their European and U.S. counterparts. These findings offer significant implications for policymakers, investors, and regulators who are concerned about the repercussions of global pandemics on financial institutions. [ABSTRACT FROM AUTHOR]

Purpose: The study investigates the impact of the interaction effect of product market competition and rollover risk on the default risk of the firms. Design/methodology/approach: The study used the sample of unbalanced panel data from Indian corporates without any survivability bias over the period from 2009 to 2020 consisting of 30,396 firm-year observations of 6,718 firms spread across 143 industry groups. The panel data regression tests the interaction effect in the context of the asset substitution problem, predation threat theory, competitive shock, and competitive risk. Findings: The empirical results highlighted the dominance of the predatory effect of competition over the disciplinary advantage of short-term debts. The competitive shock to the industry results in a higher credit spread for refinancing short-term debt and significantly increases rollover risk for firms. Smaller firms have higher default risk from rollover losses than larger firms in the face of competition due to asset-substitution problems and strong rivalry. For firms with weaker fundamentals, the interaction effect of rollover risk and competition exacerbates the flight-to-quality problem, resulting in a systemic event. Practical implications: The investors can benefit by factoring ex-ante the interdependence of competition, debt market illiquidity, and default premia while calculating the credit risk. The shareholders of competitive firms can reduce the moral hazard of refinancing the rollover losses and defaulting at a higher fundamental default threshold, by reducing sub-optimal utilization of funds by managers and agency costs. Originality/value: As per the best of author knowledge, the present study is the first to study the moderating effect of product market competition in exacerbating default risk through the rollover channel. [ABSTRACT FROM AUTHOR]

Using a unique proprietary dataset from a large European commercial bank containing granular loan-level information on credit lines to mid-corporate firms, we investigate the bank's decisions to allow firms to retain existing credit at a time of acute financial instability. Our results highlight the importance of bank-firm relationships during crisis times. Existing borrowers who actively used their credit lines were not rationed, unless they posed an increased credit risk. We do not find evidence of evergreening practices. [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]

The essence of credit guarantee financing (CGF) is the risk sharing between the guarantor and financial institution. Inspired by the case of the CGF of Evergrowing Bank, this study aims to explore the risk compensation strategies and guarantee decisions for guarantors under CGF and provide decision support for the efficient operation of the supply chain CGF. Considering three kinds of risk compensation contracts, namely cost-sharing, revenue-sharing, and quantity-flexibility, the Stackelberg game method is used to construct the supplier CGF model under the retailer's financial constraints. The results show that additional contract parameters, interest rate, debtor's initial capital, and implicit bankruptcy cost are the main factors affecting the credit guarantee coefficient decision. Besides, we find optimal additional contract parameters that can optimize the profits of supply chain members and complete the contract parameter design. The quantity-flexibility contract is the most effective strategy for the supplier to compensate for guarantee risk. However, the retailer's choice of additional contract depends on the initial capital or implicit bankruptcy cost. The main innovation of this study is that it endogenizes the credit guarantee decision and explores the details of the risk compensation contract. Further, the impact of implicit bankruptcy cost on CGF decision is also mentioned for the first time in supply chain finance. Our findings lead to recommendations for the optimal credit guarantee scheme and contract design of compensation for guarantee risk in the supply chain. [ABSTRACT FROM AUTHOR]

This article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each method, identifying their impact on the accuracy and reliability of borrower creditworthiness assessments. Current trends in machine learning and credit scoring are also covered, warning of challenges and discussing prospects. The analysis highlights the significant contributions of methods such as LGBM classifier, LR, LDA, DT classifier, gradient boosting classifier and XGB classifier to the development of modern credit scoring practices, highlighting their potential for improving the accuracy and reliability of borrower creditworthiness forecasts in the financial services industry. Additionally, the article discusses the importance of careful selection of machine learning models and the need to continually update methodology in light of the rapidly changing nature of the financial market. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak caused a massive setback to the stability of financial system due to emergence of several other risks with COVID, which significantly influenced the continuity of profitable banking operations. Therefore, this study aims to see that how differently the liquidity risk and credit risk influenced the banking profitability during Covid-19 (Q12020 to Q42021) than before COVID (Q12018 to Q42019). The study employs pooled OLS, and OLS fixed & random effects models, to analyze the panel data on a sample of 37 banks currently operating in Pakistan. The results depict that liquidity risk has a positive and significant relationship with return on assets and return on equity, but insignificant relationship with net interest margin. Credit risk has a negative and significant relationship with return on assets, return on equity, and net interest margin. The study also applies quantile regression to address the normality issue in data. The quantile regression results are consistent with pooled OLS, and OLS fixed and random effects results. The study makes valuable suggestions for regulators, policymakers, and others users of financial institutional data. The current study will help to set policies for efficient management of LR and CR. [ABSTRACT FROM AUTHOR]

Rating migration matrix is a crux to assess credit risks. Modeling and predicting these matrices are then an issue of great importance for risk managers in any financial institution. As a challenger to usual parametric modeling approaches, we propose a new structured dictionary learning model with auto-regressive regularization that is able to meet key expectations and constraints: small amount of data, fast evolution in time of these matrices, economic interpretability of the calibrated model. To show the model applicability, we present a numerical test with both synthetic and real data and a comparison study with the widely used parametric Gaussian Copula model: it turns out that our new approach based on dictionary learning significantly outperforms the Gaussian Copula model. [ABSTRACT FROM AUTHOR]

We propose a pricing formula for a defaultable zero-coupon bond with imperfect information under a regime switching model using a structural form of credit risk modelling. This paper provides explicit representations of risky debt under regime switching with a constant interest rate and risky debt under regime switching with a regime switching interest rate. While the value of the firm's equity is observed continuously, we assume that the total value of the firm is only observed at discrete times, such as the dates of the release of the firm's annual reports, or quarterly reports. This uncertainty about the true value of the firm results in credit spreads that do not approach zero as the debt approaches maturity, which is a problem with many structural models. The firm's value is typically decomposed into its equity and debt; however, we consider the asset–to–equity ratio, an accounting ratio used to examine a firm's financial well-being. The parameters in our model are regime switching, where the regime can be thought of as the state of the economy. A Markov chain with a constant transition rate matrix produces the regime switching. [ABSTRACT FROM AUTHOR]

Related party transactions (RPTs) can serve as channels for the spread of credit risk events among blockchain firms. However, current credit risk-assessment models typically only consider a firm's individual characteristics, overlooking the impact of related parties in the blockchain. We suggest incorporating RPT network analysis to improve credit risk evaluation. Our approach begins by representing an RPT network using a weighted adjacency matrix. We then apply DANE, a deep network embedding algorithm, to generate condensed vector representations of the firms within the network. These representations are subsequently used as inputs for credit risk-evaluation models to predict the default distance. Following this, we employ SHAP (Shapley Additive Explanations) to analyze how the network information contributes to the prediction. Lastly, this study demonstrates the enhancing effect of using DANE-based integrated features in credit risk assessment. [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]

• Provides a new framework for analyzing UIB credit risk by emphasizing the role of local government implicit debt governance. • Explores the moderating effect of financial technology development on the effectiveness of debt governance. • Examines the specific mechanisms (asset extension and risk warranty) through which debt governance influences UIB credit risk. • Compares the effectiveness of the 2018 and 2014 local government debt governance policies. Growing concerns surrounding urban investment bond (UIB) defaults in China necessitate a thorough examination of risk mitigation strategies. This study investigates the impact of local government implicit debt governance on UIB credit risk using a propensity score matching difference-in-differences (PSM-DID) approach. Our findings reveal that effective debt governance significantly reduces UIB credit risk, particularly for bonds issued by local government financing vehicles (LGFVs) with "high bargaining power". However, financial technology development may obscure credit risks and dampen the positive effects of governance. Further analysis, grounded in the "financial potential energy" framework, highlights the mediating roles of asset extension and risk warranty. Comparing the 2014 "Document No. 43" with the 2018 debt governance policy, we find the latter to be more effective in mitigating UIB credit risk. This study offers valuable insights into the micro-level effects of local government debt governance and provides guidance for policymakers in managing credit risks. [ABSTRACT FROM AUTHOR]

This study explores the impact of Environmental, Social, and Governance (ESG) scores on the credit risk of Chinese A-share listed companies. Based on the empirical study, we analyse both the individual and combined effects of E, S, and G scores. Our findings indicate that improvements in ESG scores significantly reduce credit risk, particularly among green companies and those with high transparency. This conclusion remains robust after accounting for potential endogeneity bias through the application of the instrumental variable method. Furthermore, our study delves into how ESG scores impact credit risk. An increase in ESG scores reduces a company's debt default risk by decreasing the negative ESG-related news sentiment, enhancing stock liquidity, lowering financing costs, and improving the quality of internal controls. These insights contribute to a deeper understanding of ESG factors in risk management and offer practical guidance for companies seeking to enhance their ESG performance in relation to credit risk. [ABSTRACT FROM AUTHOR]

This research aims to test and analyze the influence of interest rate risk and credit risk on profitability (empirical study of banking companies listed on the Indonesian Stock Exchange). The population in this study was 43 companies obtained using a purposive sampling method on Banking companies during the 2020-2022 period and based on predetermined criteria, a sample of 34 manufacturing companies listed on the Indonesia Stock Exchange was obtained. The total sample data for the 2020-2022 period is 102 samples. The analysis method used is multiple linear analysis with the help of SPSS software. The research results show that interest rate risk have a positive and significant effect on the profitability of banking companies listed on the Indonesian Stock Exchange in 2020-2022. And credit risk has a negative and significant impact on profitability in banking companies listed on the Indonesian Stock Exchange in 2020-2022. [ABSTRACT FROM AUTHOR]

The most intense periods of financial difficulties have highlighted the need for new evidence regarding the understanding of the transmission of sovereign credit risk between states. Using the Diebold and Yilmaz (2014) and an event study methodology based on Swanson et al. (2011), we study how UMP announcements impact market expectations and interconnectedness, focusing on both Western European and CEE states. The results show that the transmission of spillover effects is either constant or diminished for CEE states when asset purchase programs are implemented: markets in Latvia, Hungary, and Croatia respond promptly to UMP announcements, detaching from high credit risk networks or maintaining positions in low-risk networks. Additionally, UMP significantly reduced CDS spreads across the European Union, with pronounced effects in GIIPS states and positive effects in CEE states like Lithuania, Bulgaria, Romania, Croatia, and Poland. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of firm-level political risk on the firm's supply of trade credit. Using a novel measure of firm-level political risk developed by Hassan et al. (2019), we find that firm-level political risk has a positive impact on the firm's trade credit extension. We also demonstrate that our results are not driven by macro financial and political events, firm-level political sentiment and non-political risks, and macro-level economic and political factors. Our result continues to hold after addressing the potential endogeneity concern. Additionally, we find that the positive impact of political risk on trade credit extension is more pronounced for firms selling differentiated goods and services. Furthermore, we demonstrate that extending trade credit enhances firm value when a firm faces higher political risk. This paper highlights the importance of considering firm-level heterogeneity in political risk. It also contributes to the literature on the determinants of trade credit extension and deepens our understanding of the relationship between political risk, trade credit, and corporate performance. [ABSTRACT FROM AUTHOR]

In recent years, investors' increasing focus on sustainable investments and the sustainability orientation of companies has led to parallel growth in the market for environmental, social and governance (ESG) performance and ESG rating agencies. However, even though ESG rating agencies have become very influential institutions, the literature has found that ESG performance ratings provided by different agencies often differ from each other. This causes consequences that should be considered, such as complex evaluation of companies' ESG performance and uncertainty in ESG investment decisions. Therefore, it is necessary to identify which determinants influence ESG performance. This study aims to identify the internal determinants of an ESG score using bank-specific balance sheet indicators such as capital and risk ratios. The analysis focuses on the European banking sector from 2018 to 2022. Banks mainly foster the transition to a more inclusive and sustainable economy. Moreover, after the recent financial crises, banks have increased their social responsibility practices, strengthening their credibility, trust and reputation. Generalised estimating equations with standard error robust to heteroscedasticity were used. The results reveal that the factors that most influence the ESG score provided by ESG rating agencies are bank size and liquidity risk exposure. The larger the size of the bank and the lower the exposure to liquidity risk, the higher the ESG score assigned. [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]

Purpose. The purpose of this research is to propose a tool for designing a microcredit risk pricing strategy for borrowers of microfinance institutions (MFIs). Design/methodology/approach. Considering the specific characteristics of microcredit borrowers, we first estimate and measure microcredit risk through the default probability, applying a parametric technique such as logistic regression and a non-parametric technique based on an artificial neural network, looking for the model with the highest predictive power. Secondly, based on the Basel III internal ratings-based (IRB) approach, we use the credit risk measurement for each borrower to design a pricing model that sets microcredit interest rates according to default risk. Findings. The paper demonstrates that the probability of default for each borrower is more accurately adjusted using the artificial neural network. Furthermore, our results suggest that, given a profitability target for the MFI, the microcredit interest rate for clients with a lower level of credit risk should be lower than a standard, fixed rate to achieve the profitability target. Practical implications. This tool allows us, on the one hand, to measure and assess credit risk and minimize default losses in MFIs and, secondly, to promote their competitiveness by reducing interest rates, capital requirements, and credit losses, favoring the financial self-sustainability of these institutions. Social implications. Our findings have the potential to make microfinance institutions fairer and more equitable in their lending practices by providing microcredit with risk-adjusted pricing. Furthermore, our findings can contribute to the design of government policies aimed at promoting the financial and social inclusion of vulnerable people. Originality. The personal characteristics of microcredit clients, mainly reputation and moral solvency, are crucial to the default behavior of microfinance borrowers. These factors should have an impact on the pricing of microcredit. [ABSTRACT FROM AUTHOR]

For a bank-based economy like Vietnam, the commercial banking sector's conduct greatly influences Vietnamese economic and social prosperity. In Vietnam, net income from credit activities hold the largest portion of the total revenue of Vietnamese commercial banks. Therefore, in the context of Vietnam, credit risk obviously also plays a pivotal important role in the banking sector. Hence, the risk of credit failure can lead to a bank's collapse and have a profound effect on a country's societal structure. As seen in the previous literature, there are many macroeconomic and bank-level factors that have commonly affected the level of credit risk; however, these factors may change in the recent development era of the banking industry, especially the new impacts of digital transformation and the transition to full Basel III adoption. The overall aim of this study is to analyze the impacts of digital transformation and Basel III implementation on the credit risk level of Vietnamese commercial banks during the period from 2017 to 2023, with a sample of 21 Vietnamese listed commercial banks. This study employs the pooled OLS, fixed effect model (FEM), and random effect model (REM) methods to reach the finding that investing in technology for the readiness of digital transformation and implementing Basel III could adversely affect credit risk. Based on this finding, the authors give some recommendations for commercial banks to enhance the sustainability, safety, and better management of credit risk. [ABSTRACT FROM AUTHOR]

Lending to the agricultural sector by commercial banks in Ghana is characterized by high credit risk. Empirical evidence suggests that commercial banks in Ghana have credit risk management (CRM) challenges. This study explores the credit risk mitigation strategies adopted by commercial banks to minimize credit risk in agricultural finance in Ghana. The study adopted a mixed-method approach using a survey questionnaire and interview instruments. The findings indicate that some of the strategies used by commercial banks to mitigate credit risk in agricultural finance do not meet commercial banks' CRM needs. In addition, Ghanaian commercial banks have not fully adopted some of the recommended strategies that are used to mitigate credit risk associated with agricultural lending. The study unveils some appropriate strategies used to mitigate credit risk exposure in agricultural finance among commercial banks. These strategies include agricultural value-chain financing, collaboration with off-takers, incentive-based and risk-sharing schemes, adoption of a holistic agricultural value chain financing, policy interventions, use of agricultural insurance pool, and the proper structuring of agricultural loans. [ABSTRACT FROM AUTHOR]

This article is a book review of "Optimizing Project Work Management, and Delivery" by Gary L. Richardson and Brad M. Jackson. The book aims to provide an integrated decision architecture for project management that can be applied to all project types. It discusses various delivery methods, project profiles, project success drivers, and project externals. The book also explores different delivery strategies, such as the classic predictive model, the iterative development model, and the critical chain model. It concludes with a series of success recipes for project managers to follow. The reviewer recommends this book to project managers and program managers in various industries. [Extracted from the article]

Purpose: This study aims to examine the simultaneous effect of variations in the Capital Adequacy Ratio and Credit Risk of Islamic banks of the Gulf Cooperation Council under the influence of the Basel III regulations using an innovative approach. Design/methodology/approach: This approach highlights the critical importance of the Basel III reform in preserving the stability of the regional and international financial sector in the Gulf Cooperation Council and globally by examining the complex dynamics between Capital Adequacy Ratio and Credit Risk and their interaction under regulatory constraints. The annual reports and financial performance of 26 Islamic banks were analyzed over the period 2013–2021. Findings: The findings highlight the critical importance of the Basel III reform in preserving the stability of the regional and international financial sector in the Gulf Cooperation Council and globally by examining the complex dynamics between Capital Adequacy Ratio and Credit Risk and their interaction under regulatory constraints. The annual reports and financial performance of 26 Islamic banks were analyzed over the period 2013–2021. Originality/value: The insights from findings help define effective strategies to manage and mitigate Credit Risk while strengthening solvency under Basel III prudential supervision. Policymakers, regulatory authorities and banking institutions can optimize the management of Credit Risk and create a robust and stable financial environment for Islamic banks. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the effect of the diversity of the board of directors (BOD) and the shariah supervisory board (SSB) on credit risk, insolvency, operations, reputation, rate of deposit return risk (RDRR) and equity-based financing risk (EBFR) of Islamic banks (IB). Design/methodology/approach: The study uses 68 IBs from 19 countries covering 2009 to 2019. BOD and SSB diversity attributes data were hand-collected from the annual reports. Financial data were collected from the bankscope database. The robustness test and two-step system generalized method of moment estimation technique were used to address potential endogeneity issues. Findings: This study provides evidence that diversity in the experience and cross-membership of board members decreases the risk. Gender diversity increases the risk, but the BOD's education level diversity has no relationship with risk. More interestingly, influences in the experience and cross-membership of the SSB's members positively influence risk. However, members' education levels and gender diversity have not been proven to affect risk. Practical implications: The paper recommends that Islamic banking authorities play a stronger role and make a greater effort in driving corporate governance reform. Also, determining individual characteristics of the board is a requirement to become a member of a BOD or an SSB. Originality/value: This paper expands the commitment literature through the diversity of the BOD's and the SSB's members in terms of their education levels, experience, cross-membership and gender. This study expands the list of potential risks for IBs, by including the RDRR and EBFR. [ABSTRACT FROM AUTHOR]

Investment in and allocating investment resources to business activities is done through the financial market and the banking credit market is part of this market. The most role of banks in the financial market is to give credit to customers. In this matter the capital adequacy assessment and credit risk are important and their control is considered as one of the key effective factors in bank operation and plays a major role in banks and financial institutes’ stability. So, the aim is to determine the effect of macroeconomic variables (GDP, liquidity, inflation rate and exchange rate) on Capital adequacy, liquidity risk and credit risk. The results showed that macroeconomic variables have a significant effect on banking risks. [ABSTRACT FROM AUTHOR]