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This paper provides novel evidence that increased student loan debts, caused by rising tuitions, increase borrowers' demand for additional consumer debt, while simultaneously restricting their ability to access it. The net effect of student loan debt on consumer borrowing varies by market, depending on whether the supply or demand channel dominates. In loosely underwritten credit markets, increased student loan debt causes borrowing to increase, while in tightly underwritten markets, increased student loan debt reduces credit use. These findings match predictions of a standard life cycle model of household consumption and borrowing, augmented by a realistic student loan repayment contract. (JEL G51, D15, I22, D14) [ABSTRACT FROM AUTHOR]

As housing development and housing market policies involve many long-term decisions, improving house price predictions could benefit the functioning of the housing market. Therefore, in this paper, we investigate how house price predictions can be improved. In particular, the merits of Bayesian estimation techniques in enhancing house price predictions are examined in this study. We compare the pseudo out-of-sample forecasting power of three Bayesian models—a Bayesian vector autoregression in levels (BVAR-l), a Bayesian vector autoregression in differences (BVAR-d), and a Bayesian vector error correction model (BVECM)—and their non-Bayesian counterparts. These techniques are compared using a theoretical model that predicts the borrowing capacity of credit-constrained and unconstrained households to affect house prices. The findings indicate that the Bayesian models outperform their non-Bayesian counterparts, and within the class of Bayesian models, the BVAR-d is found to be more accurate than the BVAR-l. For the two winning Bayesian models, i.e., the BVECM and the BVAR-d, the difference in forecasting power is more ambiguous; which model prevails depends on the desired forecasting horizon and the state of the economy. Hence, both Bayesian models may be considered when conducting research on house prices. [ABSTRACT FROM AUTHOR]

Machine learning for financial risk prediction has garnered substantial interest in recent decades. However, the class imbalance problem and the dilemma of accuracy gain by loss interpretability have yet to be widely studied. Symbolic classifiers have emerged as a promising solution for forecasting banking failures and estimating creditworthiness as it addresses class imbalance while maintaining both accuracy and interpretability. This paper aims to evaluate the effectiveness of REMED, a symbolic classifier, in the context of financial risk management, and focuses on its ability to handle class imbalance and provide interpretable decision rules. Through empirical analysis of a real-world imbalanced financial dataset from the Federal Deposit Insurance Corporation, we demonstrate that REMED effectively handles class imbalance, improving performance accuracy metrics while ensuring interpretability through a concise and easily understandable rule system. A comparative analysis is conducted against two well-known rule-generating approaches, J48 and JRip. The findings suggest that, with further development and validation, REMED can be implemented as a competitive approach to improve predictive accuracy on imbalanced financial datasets without compromising model interpretability. [ABSTRACT FROM AUTHOR]

Libor is an estimate of interbank borrowing costs computed daily from rates reported by a fixed panel of banks. Evidence suggests that banks have manipulated Libor in recent years by misreporting their borrowing costs. I estimate a strategic reporting model that identifies banks' borrowing costs as well as their motives for misreporting. The estimation places a lower bound on the value that Libor would have had if banks had truthfully reported their borrowing costs. The model is identified even when unobserved heterogeneity exists in the form of a common cost component that is known by all banks but unobservable to the econometrician and is allowed to follow a non-stationary process. The only data used for identification are banks' Libor quotes. Overall, I find that the estimated lower bound for the unmanipulated Libor is always above the published Libor, with an average deviation of 25 basis points at the worst of the financial crisis of 2007–2008. The estimated bound displays a pattern similar to two other measures of interbank borrowing costs that have been used previously to assess the extent of manipulation. The model is also used to determine the extent to which misreporting was motivated by signalling or banks' net exposure to Libor. The estimation results indicate that sending creditworthiness signals was the main driver of systematic misreporting from 2007 to 2010. [ABSTRACT FROM AUTHOR]

This article explores young people's consumption of credit and the role of credit and debt in the distinction between youth and adulthood. The article engages with recent shifts in the nature of credit that have turned credit into an object of consumption in itself, as well as broader arguments about the financialisation of daily life, in order to understand the temporalities and moral distinctions enacted in different forms of credit and debt among youth. While it is well recognised that financialised capitalism operates and creates value from differences including gender, racialisation and class, the formation of youth subjectivities through credit and debt technologies remains unexplored in the literature despite an emerging crisis of consumer credit among young people. With this in mind, this article draws on a qualitative study of youth, credit and debt, to show that young people experience debt within contradictory temporalities and calculative logics, including the long-term 'investments' required to become an adult, and the logic of consumption attached to consumer credit which positions credit as a failure of self-responsible adulthood because it places future creditworthiness in jeopardy. In this way, the article suggests a future research agenda on the way that biographical distinctions are enacted through credit and debt, and how notions of youth and adulthood contribute to the qualification and consumption of credit. [ABSTRACT FROM AUTHOR]

We study credit contracts under a life‐cycle setting where time‐inconsistent agents lack the internal commitment to stick to consumption plans and external commitment to repaying loans. With unrestricted credit, agents with only internal commitment problems may overborrow. If, additionally, they face external commitment problems, lenders endogenously impose borrowing limits similar to the ability‐to‐repay rules consumer financial protection agencies impose. Even with restricted credit access, except in exceptional cases, agents suffering from the twin commitment problems can achieve, at most, fully sophisticated allocations. The government can achieve the first‐best allocations if and only if it is assisted with endogenously imposed borrowing limits. [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]

In an uncertain economic environment, the ability of firms to adapt their production levels to unforeseen market demand is critical for their investment, financing and trade credit policies. This paper focuses on the impact of production flexibility on trade credit values and maturity in the presence of uncertain demand. We develop a continuous‐time real options framework where a buyer firm with capacity constraints orders input goods on credit from a supplier. The supplier optimally chooses the trade credit maturity, considering its influence on the buyer's optimal quantities and default timing. We distinguish between flexible firms, capable of suspending production during adverse conditions, and rigid firms, constrained to constant full‐scale production. Our findings reveal that production flexibility positively affects trade credit values and maturity. Flexible firms invest in larger capacity, default later and order larger quantities, resulting in higher trade credit values. Suppliers extend longer maturities to flexible firms, reflecting their higher creditworthiness and the positive effects of extended trade credit on their installed capacity. Furthermore, we explore extensions to our framework, including switching costs, entry timing, interactions between debt and trade credit and a non‐cooperative bargaining game. [ABSTRACT FROM AUTHOR]

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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]

Credit risk, stemming from the failure of a contractual party, is a significant variable in financial institutions. Assessing credit risk involves evaluating the creditworthiness of individuals, businesses, or entities to predict the likelihood of defaulting on financial obligations. While financial institutions categorize consumers based on creditworthiness, there is no universally defined set of attributes or indices. This research proposes Range control-based class imbalance and Optimized Granular Elastic Net regression (ROGENet) for feature selection in credit risk assessment. The dataset exhibits severe class imbalance, addressed using Range-Controlled Synthetic Minority Oversampling TEchnique (RCSMOTE). The balanced data undergo Granular Elastic Net regression with hybrid Gazelle sand cat Swarm Optimization (GENGSO) for feature selection. Elastic net, ensuring sparsity and grouping for correlated features, proves beneficial for assessing credit risk. ROGENet provides a detailed perspective on credit risk evaluation, surpassing conventional methods. The oversampling feature selection enhances the accuracy of minority class by 99.4, 99, 98.6 and 97.3%, respectively. [ABSTRACT FROM AUTHOR]

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]

As iii many other areas of financial consumer law, the European Court of Justice heralds a strong protective approach to the financier's duty to assess the creditworthiness of the consumer. But enhancing deterrence above all by means of contract law does not set afair and efficient balance between the quite diverse incentives of both parties in adjusting to the policy. [ABSTRACT FROM AUTHOR]

The article used machine learning algorithms to solve credit scoring problems. Methods of analysis, forecasting and determination of the solvency of individuals have been studied. In addition, the article compares and examines one of the current problems of banking systems, and also presents the results of data processing. The research results presented in the article showed the effectiveness of credit scoring in decision making and showed that their use can significantly increase the accuracy of forecasting the creditworthiness of individuals and improve the lending decision-making process. The research results presented in the article make an important contribution to the banking industry and solve the problems of decision-making on financing of individuals. Practical application of the results will help improve the lending process and reduce the risks of financial organizations. The authors of the article plan to continue research in this area. [ABSTRACT FROM AUTHOR]

This study examines the relationship between integrated report quality (IRQ) and supplier financing using 603 firm-year observations from firms listed on the Johannesburg Stock Exchange (JSE) between 2011 and 2021. We find that IRQ has a positive association with supplier financing, indicating that firms with higher IRQ are more likely to attract increased supplier finance. This positive IRQ-supplier financing relationship is further intensified by lower information asymmetry, implying that firms' information environment plays a pivotal role. Mediation analysis indicates that information asymmetry and creditworthiness serve as underlying channels through which IRQ affects supplier financing. The IRQ-supplier financing interaction is also found to increase firms' value. The study's findings are crucial for stakeholders, such as report preparers, regulators, policymakers, the IIRC, IASB, ISSB, and JSE. These findings highlight IRQ's importance in enhancing supplier finance and indicate further steps to enhance IRQ for fostering financial stability and sustainable development worldwide. Data Availability: All data are publicly available from the sources mentioned in the paper. JEL Classifications: G32; M41; M42. [ABSTRACT FROM AUTHOR]

India's economic growth is closely associated with its energy demand, and the oil and gas industry fulfills this demand. The importance of this industry is such that it influences the decisions of many other sectors of the Indian economy. The volatility of this industry makes it necessary to analyze the liquidity position by assessing the impact of prices on the availability of liquid resources. Liquidity analysis pertains to how quickly the assets of a company can be converted into cash to meet its current obligations so as to ensure financial stability and creditworthiness. Inability of a firm to meet its short-term responsibility is likely to result in bad credit rating as well as a decrease in the goodwill of the firm in the market. Therefore, the present paper analyzes the liquidity position of oil and gas industry by making a comparative study of selected public sector undertakings (PSUs) and private companies in India for the period 2016-17 to 2020-21. The results reveal that Oil India Ltd., ONGC and Vedanta Ltd. (Cairn Oil and Gas) have a better liquidity position than IOCL and Nayara Energy. [ABSTRACT FROM AUTHOR]

The limited liability company's creditworthiness is compromised due to the limited liability of its partners, resulting in reduced security for its creditors. To safeguard the interests of these creditors, it becomes necessary to enhance control measures. This study aims to explore various aspects of control within such a company, with a particular focus on the role of the company's auditor throughout its different stages. The study seeks to analyze the position of the Jordanian legislator in comparison to other legal frameworks, including those of Egypt, Syria, and Lebanon. Divided into three sections and a conclusion, the research reveals that the Jordanian legislator has granted the auditor discretionary power in determining the approval or rejection of the company's registration, without providing specific criteria for making this decision. Additionally, there are no guarantees in place to ensure debt repayment and protect the creditors. Furthermore, there is currently no regulation in place regarding the responsibility of auditors to ensure that partners deposit the outstanding portion of their capital within the designated timeframe. In light of these findings, the study proposes a number of suggestions. [ABSTRACT FROM AUTHOR]

Blockchain cross-chaining is about interconnectivity and interoperability between chains and involves both physical to virtual digital aspects and cross-chaining between digital networks. During the process, the liquidity transfer of information or assets can increase the use of items with other chains, so it is worth noting that the enhancement of cross-chain liquidity is of great practical importance to cross-chain technology. In this model, Layerzero is used as the primary secure cross-chain facility to build a full-chain identity by unifying NFT-distributed autonomous cross-chain identity IDs; applying super-contract pairs to enhance cross-chain liquidity; and initiating a dynamic transaction node creditworthiness model to increase the security of the cross-chain model and its risk management. Finally, by verifying three important property metrics timeliness is improved by at least 18%, robustness is increased by at least 50.9%, and radius of convergence is reduced by at least 25%. It is verified that the liquidity cross-chain model can eliminate the authentication transition between hierarchies while saving the cross-chain time cost, as a way to truly realize the liquid interoperability between multiple chains of blockchain. [ABSTRACT FROM AUTHOR]

In October 1688, and again in May 1689, Mary Cudmore claimed to have encountered a 'spectre' in her employers' house in Cork city. The great interest the story aroused among the townspeople is indicated in letters from prominent inhabitants and an examination of Mary by the bishop of Cork and Ross. They allow us access into the homes and heads of the people of Cork at a point when significant challenges were being mounted to the Protestant authorities' dominance of the city, as the War of the Two Kings loomed, and as memories of other conflicts stirred. As observers weighed questions of scruple and certainty, and balanced concerns about imposture and creditworthiness, we also glimpse shifts in supernatural belief and contemporary debates about the construction of proof and truth. That anything of Mary's experiences has survived is remarkable, given the loss of most of the contents of the Dublin Public Record Office in 1922. In the context of ongoing deliberate and negligent destruction of archives, this article thus also argues for consideration of and consideration to partial and phantom histories. [ABSTRACT FROM AUTHOR]

The integration of environmental, social and governance (ESG) criteria into credit risk assessment has gained significant attention recently. Therefore, it is necessary to understand, select and assess the risks of these ESG criteria and evaluate how they can impact company's creditworthiness. The main objective of this study is to give a thorough overview of the types of ESG risks, their interrelation with credit risk of the company and methodological approaches they can use, along with opportunities for future advancements. The analysis shows that climate change and transition to low-carbon economy provoke two main types of ESG risks – physical risks and transition risks. Both types of ESG risks could affect company's creditworthiness through its cash flows capital and collateral. In order to assess climate-related types of risks methodologies based on forward-looking scenarios are needed with the employment of data that has not been observed in the past. These results are a basis for further development of methodological architecture of ESG-credit risks. [ABSTRACT FROM AUTHOR]

This study examines the existing credit rating methodology proposed in the literature to explore the development of a new credit rating model based on the financial variables of the enterprise. The focus is on the period after the financial crisis of 2018. This study aims to develop a credit rating model using neural networking and tests the same for its accuracy. The goal of this study is to address the issue brought up by previous research on subjectivity in the data used to determine creditworthiness. The database for the study includes financial data up to July 2022 from December 2018. A model is created to assess an enterprise's creditworthiness using neural networking. This study first evaluated the existing credit rating models proposed in the literature. Next, based on financial data and neural networking, a model is developed. It was evident that the model developed in this study has an accuracy of 85.16% and 76.47% on train and test data respectively. There exist several models to determine the creditworthiness of borrowers but all failed to address the concern of subjectivity in the data. The model created in this study made use of cutting-edge technology such as neural networking and financial data. This paper's unique approach and model construction based on a comparison of existing models is rare in the literature and justify the originality of this paper with a practical value at the global level. [ABSTRACT FROM AUTHOR]

Classic inventory theory suggests that inventory plays a vital role in matching demand and supply. This paper provides both country- and firm-level evidence that inventory can be used as a financial instrument to take advantage of arbitrage opportunities in financial markets with limited capital mobility. Using data from China customs' metal imports and firm-level inventory from metal processing industries, we show that firms can utilize the inventory of an imported product to carry lower cost capital into a country with strict capital controls and, thus, gain higher financial returns. Specifically, at the country level, we find that the level of imported metal commodities is positively associated with the expected returns from financial arbitrage. At the firm level, we find that a higher expected return from financial arbitrage will incentivize a firm to increase its inventory level through short-term borrowing. In addition, our evidence supports the notion that firms with higher liquidation value, larger size, and higher sales growth (i.e., firms with higher borrowing capacity) may be more active in using inventory as an instrument to seek higher financial gains. We also utilize a unique regulatory policy shock as a quasi-natural experiment to enhance the causality interpretation of our empirical findings. This paper was accepted by Vishal Gaur, operations management. Funding: This work was supported in part by the National Natural Science Foundation of China [Grant 72201230] and the Research Grants Council of Hong Kong, University Grants Committee [Grant 14504621]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4873. [ABSTRACT FROM AUTHOR]

This paper investigates banks' credit strategies concerning micro, small and medium enterprises (MSMEs) by taking into consideration the intrinsic characteristics of individual enterprises and external correlations among them when assessing credit risk. We analysed transaction data between enterprises and utilised the Bean Search algorithm and Markov random field to construct enterprise network topology diagrams, quantifying inter-enterprise correlations. We also applied the combined weight method to balance their creditworthiness and strength indices and developed a comprehensive and scientifically grounded credit strategy model for MSMEs by employing genetic algorithms to solve nonlinear programming problems. Overall, our proposed model aims to maximise loan revenues while simultaneously minimising the risk of customer default. [ABSTRACT FROM AUTHOR]

This paper investigates the macroeconomic fundamentals that international investors consider crucial when assessing a country's default risk. Using panel data for 41 countries over the period 2002–2019, we find that the macroeconomic determinants of a sovereign credit default swap (CDS) are heterogeneous across developed and developing economies after controlling for potential endogeneity. While international investors consider government budget balance and inflation as crucial elements in the evaluation of the CDS of developed economies, more stress is placed on economic growth and foreign reserves in the assessment of the creditworthiness of developing economies. Furthermore, we document that better institutional quality reduces the sovereign default risk in both developed and developing economies. However, global shocks appear to have a strong impact in developing economies. The results remain robust to various specifications. [ABSTRACT FROM AUTHOR]

Using a sample of Chinese listed firms from the Shanghai and Shenzhen Stock Exchanges from 2012 to 2018, we investigate the externality of the forced resignations of politically connected independent directors on the cost of debt for non-connected peers. Exploiting an exogenous shock caused by the 18th decree, we find that when politically connected independent directors resign from a Chinese listed firm, non-connected peers within the same industry experience a decrease in the cost of debt. Moreover, the sensitivity analyses further show that the above externality is more prominent in a subsample of Chinese listed non-SOEs, and in a subsample of Chinese listed firms operating in more competitive industries as well as in less developed financial markets, indicating that the externality is conditional on ownership type, industry competition and financial market development. Finally, further analyses show that peers tend to issue new debt after the political connection is disrupted. We also find that when peers have a higher creditworthiness and resignation firms have a lower creditworthiness, peers enjoy a lower cost of debt after resignations. These findings suggest that the resignations have a positive externality on peers' cost of debt due to the elimination of preferential treatment and an improved resource allocation efficiency. [ABSTRACT FROM AUTHOR]

When making decisions about whether a product can meet required performance standards, it is often of interest to make decisions as soon as enough information has been obtained–even if that is before the conclusion of the planned test or experimental design. Evaluating data with the intent of stopping a test early is referred to as interim analysis, which can be used to address time and cost constraints. In such cases, it is important to execute tests as efficiently and effectively as possible. A Bayesian method for improving efficiency is to use informative priors to incorporate previous information—such as expert opinion or previous data—to create more precise parameter estimates and avoid allocating costly resources in a sub-optimal manner. We consider the class of power priors and variants thereof, proposing a variant that allows for a computationally efficient MCMC sampling method in an interim analysis setting to evaluate a product. A novel power prior that accounts for differences and similarities between current and previous data sets, and its role in analysis, is considered in an interim analysis construct. Simulations demonstrate previous information can be leveraged using this proposed prior to stop testing early and obtain more precise parameter estimates. [ABSTRACT FROM AUTHOR]

This study investigates the way in which corporate social responsibility (CSR) practices are perceived by the U.K. bond market. In this respect, we distinguish between the two ambiguous subdimensions – that is, CSR disclosure and CSR performance. We analysed the association between CSR disclosure and the cost of corporate bonds by means of a multi‐theoretical framework combining economics with social theories. We also examine the moderating effect of the CSR performance on this relationship. We estimate a random‐effects GLS regression and random‐effects ordered probit regression using a sample of companies belonging to the FTSE 100 index during the 2013–2017 period. We find that bondholders and credit rating agencies incorporate CSR disclosure into their assessment and evaluation of the firms' creditworthiness. Mainly, our results show that bondholders encourage and value CSR reporting primarily for companies with weak CSR performance, but stop doing so once the company reach a certain level of CSR performance. We further examined each CSR disclosure subdimension and aspects of corporate creditworthiness through a disaggregated analysis. Both environmental and community relations disclosures are significant in corporate creditworthiness, whereas only responsibility disclosure is related to credit ratings. Finally, results highlight that disclosure of CSR information is especially relevant to financially healthy firms. Our findings have several implications for academics, practitioners, and policymakers in understanding the consequences of CSR disclosure and CSR performance on bond markets and credit rating. [ABSTRACT FROM AUTHOR]

CreditScore combines the predictive power of crop growth models with future climatic scenarios, satellite images, and market data to form a comprehensive profile for each farmer-borrower, based on the future yields of their crops, with the ultimate goal of assessing long-term risks affecting yields which are related to climate change. The objective of this study is to present the tools and datasets that are employed operationally by CreditScore for future yield and profitability assessments. A modeling approach built on a fusion of satellite-derived vegetation indices, agro-meteorological indicators, and crop phenology is tested and evaluated in terms of data intensiveness for the prediction of wheat and cotton yields. AquaCrop, a process-based model, provided high to moderate accuracies by fully relying on freely available datasets as sources of input data. The findings introduce a promising framework that can support the financial institutions in evaluating potential customers' agribusinesses prior to and throughout the lending process. [ABSTRACT FROM AUTHOR]

Chit fund is a peer-to-peer saving and borrowing scheme operated among trusted groups of people. It is a reliable source of funds in emergencies with no guarantor and low-interest rate. Despite their enduring benefits, chit funds face challenges related to trust, transparency, and security. At present, unscrupulous subscribers might join chit-funds, borrow money, and make payment defaults. This leads to disruptions in contribution cycle and affects overall functioning. Additionally, non-transparent record-keeping and transaction processes, hinder participants from verifying fund activities, creating a susceptible environment for fraud and malpractice. These challenges are an obstacle to the sustainability and trustworthiness of the chit-fund system. Therefore, to overcome aforementioned challenges, this paper proposes TruChit, a blockchain-based chit fund system with creditworthiness evaluation framework. It leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) to assess the creditworthiness of subscribers, which ensures the credibility and reliability of individual participants. Further, proposes permissioned blockchain-assisted chit-fund framework with role-based access control to instill trust, security, and transparency within chit-fund operations. Moreover, the efficacy of TruChit is evaluated by analyzing credit score dataset and achieves 93.5% accuracy, which is better than other approaches. [ABSTRACT FROM AUTHOR]

Purpose: Bank creditworthiness refers to the evaluation of a bank's ability to meet its financial obligations. It is an assessment of the bank's financial health, stability and capacity to manage risks. This paper aims to investigate the credit rating patterns that are crucial for assessing creditworthiness of the Islamic banks, thereby evaluating the stability of their industry. Design/methodology/approach: Three distinct machine learning algorithms are exploited and evaluated for the desired objective. This research initially uses the decision tree machine learning algorithm as a base learner conducting an in-depth comparison with the ensemble decision tree and Random Forest. Subsequently, the Apriori algorithm is deployed to uncover the most significant attributes impacting a bank's credit rating. To appraise the previously elucidated models, a ten-fold cross-validation method is applied. This method involves segmenting the data sets into ten folds, with nine used for training and one for testing alternatively ten times changeable. This approach aims to mitigate any potential biases that could arise during the learning and training phases. Following this process, the accuracy is assessed and depicted in a confusion matrix as outlined in the methodology section. Findings: The findings of this investigation reveal that the Random Forest machine learning algorithm superperforms others, achieving an impressive 90.5% accuracy in predicting credit ratings. Notably, our research sheds light on the significance of the loan-to-deposit ratio as a primary attribute affecting credit rating predictions. Moreover, this study uncovers additional pivotal banking features that intensely impact the measurements under study. This paper's findings provide evidence that the loan-to-deposit ratio looks to be the purest bank attribute that affects credit rating prediction. In addition, deposit-to-assets ratio and profit sharing investment account ratio criteria are found to be effective in credit rating prediction and the ownership structure criterion came to be viewed as one of the essential bank attributes in credit rating prediction. Originality/value: These findings contribute significant evidence to the understanding of attributes that strongly influence credit rating predictions within the banking sector. This study uniquely contributes by uncovering patterns that have not been previously documented in the literature, broadening our understanding in this field. [ABSTRACT FROM AUTHOR]

The development of financial technology (Fintech henceforth) has brought both opportunities and challenges to commercial banks' risk management. Here, we discuss the interactive relationship between Fintech and traditional finance. We first construct a mathematical model and deduce that the synergy between Fintech and traditional finance can more effectively help banks measure the creditworthiness of customers, thereby reducing risk-taking. In order to verify the deduced result, we then conduct an empirical analysis using panel data of Chinese commercial banks from 2011 to 2020. The empirical results support the theoretical outcome that the synergy between Fintech and traditional finance has indeed effectively reduced commercial banks' risk-taking. Our research also shows that Fintech development significantly reduces the risk-taking of commercial banks, and this mitigation effect is more prominent for larger-scale banks and those with a higher-developed traditional finance basis. Moreover, this mitigation effect is more significant after 2016, a period marked by an increased level of Fintech development. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between earnings management and financial distress and considers whether this relationship varies based on the severity of financial distress and signs of discretionary accruals (a proxy for earnings management). For this purpose, multiple regression analysis has been employed on a sample of 192 financially distressed Indian firms during the period 2011–2018, counting to 1,272 firm-year observations. Discretionary accruals are estimated by the Modified Jones model and Raman and Shahrur (2008) model, while Altman's Z -score and distance-to-default model are used to detect the degree of financial distress. The findings disclose that the low distressed firms are indulged in higher earnings management than high distressed firms. Also, the low distressed firms are engaged more in income-decreasing earnings management. However, the results are not consistent across both earnings management and distress measures. The findings have significant implications for investors and creditors. They need to be aware of this fact while evaluating creditworthiness of a firm since firms with even a low degree of financial distress can indulge in earnings management to camouflage their true financial condition. [ABSTRACT FROM AUTHOR]

This article investigates how cross-selling affects relationship lending using internal data from a large bank and the Swedish credit registry. I show that within a bank–firm relationship, profit earned from non-loan products cross-subsidizes loans and increases (1) credit supply and (2) the likelihood of the bank's pausing or waiving interest payments for delinquent loans (lenience in delinquency). For identification, I exploit the Basel II-induced exogenous variation in products' profitability while holding constant the firm's creditworthiness and relationship informativeness. I find that the average affected firm experienced a decrease of 6 percent ($400,000) in credit supply and 30 percent (9.8 pp) in lenience in delinquency. The results highlight the importance of cross-subsidization as a mechanism through which cross-selling affects bank–firm relationships and inform optimal regulatory design for lenders who multi-produce. [ABSTRACT FROM AUTHOR]

The credit frictions encountered by small and medium-sized enterprises (SMEs) have been widely examined in the entrepreneurship literature. Although theory suggests that asset tangibility helps increase firms' borrowing capacity because it allows creditors to take possession of a firm's assets more easily, this paper provides new evidence about the role of intangible assets in reducing credit frictions for SMEs. Using an extensive dataset of more than 155,852 SMEs in Vietnam and a multivariate probit model, we find that identifiable intangible assets improve firm access to debt and equity finance. Interestingly, it is found that the friction-reducing effect of intangibles is stronger on debt finance than on equity finance, suggesting non-equivalent distributional effects of intangible assets on firm capital structure. Moreover, firm age and size can moderate the association between intangibles and access to the two sources of external finance. Plain English Summary: The transition from a manufacturing-based economy towards one driven by technology and innovation is making firms' intellectual property and intangible assets important to their business models. We challenge the conventional presumption that asset intangibility is nonbankable, especially in less-developed financial markets. This study provides new evidence about the role of intangible assets in reducing credit friction for SMEs in Vietnam. Specifically, we find that intangible assets, especially those that are identifiable (e.g. software, databases, in-progress R&D, copyrights and the right to land use) are essential in determining firms' ability to raise external finance. We also find that the friction-reducing effect of intangibles is stronger on debt than on equity finance. Thus, the principal implication of this study is that equity investors in less-developed financial markets do not fully appreciate the value of intangible assets, whereas banks and professional lenders take intangibles into consideration in their lending decisions. [ABSTRACT FROM AUTHOR]

A lack of information about potential borrowers is a major obstacle to access to financing from the traditional financial sector. To prevent fraud, increase access to finance, and support balanced sustainable development, countries aroun6d the world have moved over the past several decades to develop credit information reporting requirements and systems to improve the coverage and quality of credit information. Until recently, such requirements mainly covered banks. However, with the process of digital transformation in China and around the world, a range of new credit providers have emerged, in the context of financial technology (FinTech, TechFin, and BigTech). Application of advanced data and analytics technologies provides major opportunities for market participants-both traditional and otherwise-as well as for credit information agencies. By utilizing advanced technologies, these participants and credit reporting agencies can collect massive amounts of information from various online and other activities ('Big Data'), which contributes to the analysis of borrowing behavior and improves the accuracy of creditworthiness assessments. Thereby, enhancing availability of finance and supporting growth and development while also moderating prudential, behavioral and conduct related concerns are at the heart of financial regulation. Aligning with the international trend, China has developed a regulatory regime for credit information reporting and business over the past decades. However, this development has not come without its problems, even in the context of traditional banking and credit. With the rapid growth and development of FinTech, TechFin, and BigTech lenders, opportunities to leverage credit information and data, and challenges around its regulation have emerged. For example, due to fragmented sources of borrower information and the involvement of several actors, difficulties arise in clarifying the business scope of credit reporting and customer protection. Moreover, inadequate incentives for credit information and data sharing pose a challenge for regulators in promoting competition and innovation in the credit market. Drawing upon the experiences of other jurisdictions, including the United States, United Kingdom, European Union, Singapore, and Hong Kong, this paper argues that China should establish a sophisticated licensing regime and set out differentiated requirements for credit reporting agencies in line with the scope and nature of their business, thus addressing potential for regulatory arbitrage. Further, this paper argues that China should formulate specific rules governing the provision of customer information to credit reporting agencies and resolving disputes arising from accuracy and completeness of credit data. An effective information and data sharing scheme should be enacted to help lenders make appropriate credit decisions and facilitate access to financing. The lessons from China's experience hold key insights for other jurisdictions as they move from credit information to credit data regulation in their financial systems. [ABSTRACT FROM AUTHOR]

Nowadays, due to the worldwide crisis, there is a necessity to predict and make a prognosis for the future development of the companies from the view of their financial health. Mainly mining organizations in Slovakia face a problem with creditworthiness when a number of companies declared bankruptcy. The paper has the ambition to predict development in chosen Slovakian mining organization through the most used bankruptcy and bonity models, which brings results, serving for comparing with companies in time and sector. The results were evaluated according to the individual areas of financial position that influenced the development the most. Such results are profitable for the sustainable development of mining organizations together with providing the energetic independence of the countries. [ABSTRACT FROM AUTHOR]

Carbon risk, a type of climate risk, is expected to have a crucial impact, especially on high-carbon-emitting, "polluting" firms as opposed to less carbon-intensive, "clean" ones. With a rising number of actions and policies being continuously proposed to mitigate these concerns and an increasing number of investors demanding more climate adaptation initiatives, this transition risk will certainly need to be incorporated into a firm's credit risk assessment. In this paper, we explore the impact of the carbon risk factor, constructed as the daily median difference in default protection between polluting and clean European firms, on firm creditworthiness using quantile regressions on the tail distribution of credit default swap spreads for different maturities between 2020 and 2023. In particular, the recent European interest rate hikes lead to unexpected conclusions about when the carbon risk factor affects firm creditworthiness and how rapidly the net-zero economy transition must occur. Contrary to the previous literature, we find that investors are expecting the transition to occur in the medium-to-long term. [ABSTRACT FROM AUTHOR]

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