企业信用风险研究；债券融资信用风险研究；建筑企业信用风险研究；国企/央企信用风险研究

… enterprise credit business, the credit risk of an enterprise is regarded as one of the primary risk … , and operation status of the enterprise, leading to the generation of credit risk (Prater et al.…

The assessment of Enterprise Credit Risk (ECR) is a critical technique for investment decisions and financial regulation. Previous methods usually construct enterprise representations by credit-related indicators, such as liquidity and staff quality. However, indicators of many enterprises are not accessible, especially for the small- and medium-sized enterprises. To alleviate the indicator deficiency, graph learning based methods are proposed to enhance enterprise representation learning by the neighbor structure of enterprise graphs. However, existing methods usually only focus on pairwise relationships, and overlook the ubiquitous high-order relationships among enterprises, e.g., supply chain connecting multiple enterprises. To resolve this issue, we propose a Multi-Structure Cascaded Graph Neural Network framework (MS-CGNN) for ECR assessment. It enhances enterprise representation learning based on enterprise graph structures of different granularity, including knowledge graphs of pairwise relationships, homogeneous and heterogeneous hypergraphs of high-order relationships. To distinguish influences of different types of hyperedges, MS-CGNN redefine new type-dependent hyperedge weight matrices for heterogeneous hypergraph convolutions. Experimental results show that MS-CGNN achieves state-of-the-art performance on real-world ECR datasets.

… credit risk rating of enterprises is therefore introduced to improve the efficiency of supervision. By analyzing the purposes of enterprise credit risk … resources in each credit risk class. Then…

The purpose of this study is to reduce the rate of multicriteria decision-making (MCDA) errors in credit risk management and to weaken the influence of different attitudes of enterprise managers on the final decision when facing credit risk. First, several solutions that are suitable for present enterprise credit risk management are proposed according to the research of enterprise risk management in the world. Moreover, the criteria and matrix are established according to the general practice of the expert method. A decision-making method of enterprise credit risk management with trapezoidal fuzzy number as the criteria of credit risk management is proposed based on the prospect theory; then, the weight is calculated based on G1 weight calculation, G2 weight calculation method, and the method of maximizing deviation; finally, the prospect values of the alternatives calculated by each method are adopted to sort and compare the proposed solutions. Considering the difference of risk degree of managers in the face of credit risk management, the ranking results of enterprise credit risk management solutions based on three weight calculation methods are compared. The results show that as long as the quantitative value of the risk attitude of the enterprise credit risk manager meets a certain range, the final choice of credit risk management scheme ranking is consistent. This exploration provides a new research direction for enterprise credit risk management, which has reference significance.

The spread of enterprise credit risk in the supply chain may lead to large‐scale bankruptcy and credit crises, which are related to national economic and social stability and financial system security. Therefore, enterprise credit risk in the supply chain context is not only a concern for banking financial institutions, credit rating agencies and enterprise managers but also the focus of governments. This article develops a DTE‐DSA (decision tree [DT] ensemble model using the differential sampling rate, Synthetic Minority Oversampling Technique [SMOTE] and AdaBoost) prediction framework integrating supply chain information to predict enterprise credit risk. The empirical test shows that using supply chain information can significantly improve the prediction score. The DTE‐DSA model has the best prediction effect in dealing with class imbalance problems. Compared with single classifier models—such as logistic regression, k‐nearest neighbours, support vector machine, DT and DT using the SMOTE—as well as ensemble models—such as extremely randomized trees, random forest, rotation forest, extreme gradient boosting, gradient boosting DT and DT ensemble model using AdaBoost—the DTE‐DSA model not only has the best prediction score but also has a more stable performance. The comprehensive use of supply chain information and the DTE‐DSA model can result in the highest prediction score, with an area under the curve of 0.9016 and a Kolmogorov–Smirnov statistic of 0.7369. Further analysis of the variables of importance enhances the interpretability of the model and obtains relevant management insights.

Abstract To explore the enterprise credit risk evaluation, the application effect of several common neural network models in Chinese small and medium-sized enterprise data sets was compared and the optimal parameters for each model were determined. In addition, the classification accuracy and the applicability of the model were compared, and finally the common problem of optimization neural network algorithm based on population was solved: need to determine the dimensions in advance. The experimental results showed that the probabilistic neural network (PNN) had the minimum error rate and second types of errors, while the PNN model had the highest AUC value and was robust. To sum up, the algorithm makes some contributions to solve the financing problem of small and medium-sized enterprises in China.

Supply chain finance has broken through traditional credit modes and advanced rapidly as a creative financial business discipline. Core enterprises have played a critical role in the credit enhancement of supply chain finance. Through the analysis of core enterprise credit risks in supply chain finance, by means of a ‘fuzzy analytical hierarchy process’ (FAHP), the paper constructs a supply chain financial credit risk evaluation system, making quantitative measurements and evaluation of core enterprise credit risk. This enables enterprises to take measures to control credit risk, thereby promoting the healthy development of supply chain finance. The examination of core enterprise supply chains suggests that a unified information file should be collected based on the core enterprise, including the operating conditions, asset status, industry status, credit record, effective information to the database, collecting related data upstream and downstream of the archives around the core enterprise, developing a data information system, electronic data information, and updating the database accurately using the latest information that might be available. Moreover, supply chain finance and modern information technology should be integrated to establish the sharing of information resources and realize the exchange of information flows, capital flows, and logistics between banks. This should reduce a variety of risks and improve the efficiency and effectiveness of supply chain finance.

… Enterprise credit risk assessment has long been regarded as … The enterprise credit risk dataset, which includes 239 … an alternative method for enterprise credit risk assessment. …

… This study aims to examine the impact of data assets on enterprise credit risk and investigate the moderating role of artificial intelligence (AI) in this relationship. Furthermore, it takes into …

ABSTRACT In order to improve the effect of enterprise credit risk assessment, this paper combines the improved fuzzy neural network to construct the enterprise credit risk assessment model. The various jamming patterns that can threaten the credit data transmission system include various blocking jamming and tracking jamming. Moreover, this paper analyzes the bit error performance of the credit data transmission system against various interferences, and obtains the bit error curves against various interferences through program simulation. In addition, this paper builds a variable-speed credit data transmission system based on Simulink, analyzes the key technologies in variable-speed credit data transmission, and simulates the anti-interference performance of variable-speed credit data transmission technology. The simulation study verifies that the enterprise credit risk assessment model proposed in this paper has good risk assessment effect and risk response strategy effect.

… chain finance, especially on credit risk. Therefore, this … credit risk assessment. Firstly, the article applies the literature induction method to review the supply chain financial credit risk …

Small and micro enterprises are pivotal in national economic and social development. To foster their growth, managing their credit risks scientifically is crucial. This study starts by examining the credit information of these enterprises. We use imbalanced sample processing algorithms to ensure a balanced representation of minority-class samples. Then, a machine learning classifier is employed to identify key factors contributing to these enterprises' low credibility. Based on these factors, an XGBoost scoring card model is developed. The study reveals: firstly, the integration of the SMOTE algorithm with the XGBoost model exhibits certain performance advantages in handling imbalanced datasets; secondly, trustworthy financial information remains at the heart of crucial risk determinants; thirdly, the XGBoost scoring card model based on significant features effectively enhances the accuracy of credit risk assessment. These insights provide both theoretical references and practical tools for enhancing the robustness of small and micro enterprises, facilitating early warnings on credit risks, and refining financing efficiency.

ABSTRACT China’s reform and opening-up policies have prioritized technological advancement, with technological SMEs driving employment and economic growth. Despite their significance, these SMEs face substantial financing and operational risks due to inadequate credit measurement tools. This study reviews the historical financing challenges of technological SMEs since the 1980s, summarizes their current credit risk status, and compares four modern credit risk models: Credit Metrics, Credit Risk+, Credit Portfolio View, and KMV. We propose a pioneering KMV Strategy for real-time risk analysis, contributing to accurate credit metrics for these SMEs. Finally, we suggest policies for managing their credit risks through prevention, control, and governance.

Abstract Taking 187 high-tech listed companies in China as samples, using the Cox model, an empirical test on the influence factors of high-tech enterprise credit risk is carried out. The empirical results show that, the financial situation has significant effect on credit risk of high-tech enterprise, especially the current ratio, accounts receivable turnover, total assets turnover ratio, return on equity, etc; and also the independent innovation capacity has significant effect on credit risk of high-tech enterprise, the stronger the independent innovation capacity is, the lower high-tech enterprise credit risk becomes. However, the influence of regional factor on credit risk of high-tech enterprise is relatively limited, the influence of growing factor is not obvious, as well as enterprise scale, and the influence of industry factor should be further examined. Particularly, this paper provides evidence that there is a significant negative correlation between independent innovation capacity and credit risk of high-tech enterprise; this will contribute to financial institutions to increase the credit support for independent innovation.

In this research, trade credit is analysed form a seller (supplier) perspective. Trade credit allows the supplier to increase sales and profits but creates the risk that the customer will not pay, and at the same time increases the risk of the supplier’s insolvency. If the supplier is a small or micro-enterprise (SMiE), it is usually an issue of human and technical resources. Therefore, when dealing with these issues, the supplier needs a high accuracy but simple and highly interpretable trade credit risk assessment model that allows for assessing the risk of insolvency of buyers (who are usually SMiE). The aim of the research is to create a statistical enterprise trade credit risk assessment (ETCRA) model for Lithuanian small and micro-enterprises (SMiE). In the empirical analysis, the financial and non-financial data of 734 small and micro-sized enterprises in the period of 2010–2012 were chosen as the samples. Based on the logistic regression, the ETCRA model was developed using financial and non-financial variables. In the ETCRA model, the enterprise’s financial performance is assessed from different perspectives: profitability, liquidity, solvency, and activity. Varied model variants have been created using (i) only financial ratios and (ii) financial ratios and non-financial variables. Moreover, the inclusion of non-financial variables in the model does not substantially improve the characteristics of the model. This means that the models that use only financial ratios can be used in practice, and the models that include non-financial variables can also be used. The designed models can be used by suppliers when making decisions of granting a trade credit for small or micro-enterprises.

The credit risk of Shouguang vegetable enterprises in China is the biggest obstacle to corporate loans. Building a credit risk assessment model for Shouguang vegetable enterprises and accurately rating the risk of loan enterprises is the key to successful loans. This article aims to construct an AHP evaluation model for the credit risk of Shouguang vegetable enterprises. The data is sourced from managers, bank credit personnel, university researchers, and enterprise related customers who are familiar with the enterprise, while considering four risk levels: impact degree(I), occurrence probability(P), risk manageability(M), and government support(S). This article uses AHP and risk index scores to evaluate the credit risk of Shouguang vegetable enterprises. This model calculates the risk index score based on survey data from 41 corporate credit risk professionals, constructs a pairwise comparison judgment matrix, and conducts consistency testing. It calculates the risk level membership vectors of impact degree, occurrence probability, risk manageability, and government support level at four risk levels, and then calculates the comprehensive evaluation membership vector of credit risk for Shouguang vegetable enterprise. The calculation results show that the comprehensive credit risk assessment level of Shouguang vegetable enterprise belongs to the general risk level, with a membership value of 0.5836. The results still show that the credit rating of Shouguang vegetable enterprises in the four risk levels of impact degree, occurrence probability, risk manageability, and government support are all average risk levels, but there are differences in membership values. The maximum membership value under the impact degree level is 0.6163, and the minimum membership value under the risk manageability level is 0.5572. This study provides a feasible and practical model for enterprise credit risk assessment and conducts a detailed evaluation of the credit risk of Shouguang vegetable enterprise, providing valuable reference for enterprise managers, bank credit personnel, and related researchers.

… green credit policies affect enterprise risk. The allocation of credit funds via green credit … of the channels through which green credit policies affect enterprise risk is conducted. Moreover, …

… To provide overall perspective on corporate default rates, we begin with a simple descriptive analysis of the data. The US experienced many severe clustered default events during the …

Corporate default predictions play an essential role in each sector of the economy, as highlighted by the global financial crisis and the increase in credit risk. This study reviews the corporate default prediction literature from the perspectives of financial engineering and machine learning. We define three generations of statistical models: discriminant analyses, binary response models, and hazard models. In addition, we introduce three representative machine learning methodologies: support vector machines, decision trees, and artificial neural network algorithms. For both the statistical models and machine learning methodologies, we identify the key studies used in corporate default prediction. By comparing these methods with findings from the interdisciplinary literature, our review suggests some new tasks in the field of machine learning for predicting corporate defaults. First, a corporate default prediction model should be a multi-period model in which future outcomes are affected by past decisions. Second, the stock price and the corporate value determined by the stock market are important factors to use in default predictions. Finally, a corporate default prediction model should be able to suggest the cause of default.

… on corporate default risk. This study endeavours to fill this gap by investigating the influence of uncertainty on corporate default risk … firm default risk measured by the Distance to Default. …

… ), and none have focused on corporate debt default risk. In addition, although existing literature has examined how digital transformation affects corporate default risk, this paper argues …

… This paper constructs a corporate default risk prediction … and supplement the current corporate default prediction indicator … of ESG indicators in corporate risk management in light of the …

… corporate credit default swaps (CDSs), this paper offers an economic understanding of implied loss given default (LGD) and jumps in default risk. … Sudden increases in the default risk of …

Abstract Default risk prediction can not only provide forward-looking and timely risk measures for regulators and investors, but also improve the stability of the financial system. However, the determinants of corporate default risk in China have not been well-identified. An empirical analysis was conducted using a unique dataset of default events in the Chinese market to fill this gap. First, we demonstrated that the default probability estimated by a structural model, which is widely used in the literature, do not fully reveal the default risk of firms in China. Second, we classified default events into minor and major defaults for empirical analysis. We found that firms that survive minor defaults behave differently from other bankrupt firms. Our results suggest that the determinants of corporate default risk in China and the United States differ. We also found that a firm’s continued increase in cash holdings is one of the most important signs of default. Overall, our study significantly improves the accuracy of forecasting corporate default risk in China.

… on corporate debt default risk. Empirical research shows that the development of fintech can significantly reduce the risk of corporate debt default. … category of corporate debt default risk, …

Abstract Corporate default risk can affect financial stability and the macroeconomy. However, the determinants of corporate default risk in China are not well defined in the literature. We address this issue by using a rich credit event dataset of 981 Chinese listed firms over the period 1998–2013 and study the factors that affect default risk. We demonstrate that leverage, liquidity, firm size are the key firm-specific factors in determining default risk in China, along with macroeconomic factors like interest rate and stock return. Moreover, ‘Too big to fail’ only applies to non-SOEs, as default risk of SOEs is not affected by the firm size. We further find that high liquidity fails to reduce firms default risk, because small-sized firms which are financially constrained have limited cash to prevent financial distress, whereas large firms with greater cash holdings are able to mitigate their default risk as they are unconstrained.

… This paper examines the effect of CPU on corporate default risk based on China’s CPU data and firm-level dataset. Our study empirically finds that corporate default risk is positively …

… standing and debt default risk. This paper examines default risk formation mechanisms for … on default risk. The findings indicate that intensified climate change significantly elevates …

… background and corporate default risk, and analyzes the mechanism of corporate governance as … that: (1) executive educational background negatively affects corporate default risk. (2) …

… between corporate social responsibility (CSR) and default risk, with a … of default and the effect is stronger in the long term than in the short run. Further, the impact of CSR on firm default …

… default incidents, this study investigates the influence of Confucian culture on corporate default risk … corporate default risk. Channel analysis reveals that Confucian culture mitigates the …

ABSTRACT Confucianism is the cornerstone of traditional Chinese culture, and it has a significant impact on corporate behaviour. This paper investigates the relationship between Confucian culture and default risk. Using a sample of Chinese listed companies that covers the period between 2010 and 2020, we find robust evidence that Confucian culture is negatively associated with the probability of default. The effect operates through improvements in reputation and resource acquisition. It is more pronounced at firms that face severe financing constraints, at firms that are located in regions with high marketization, and at firms that are subject to weaker external supervision. Additional analyses show that Confucian culture improves corporate social responsibility and the quality of internal control, reduces earnings management and corporate risk-taking, and, ultimately, decreases the overall value of the firm. On the whole, our findings provide evidence of the role of cultural factors in filling institutional voids.

… on default risk. To this end, this paper examines the effect of CSR on default risk reduction. It also examines whether the strength of the relationship between CSR and default risk varies …

Abstract This paper analyses the impact of corporate board structure on default risk of European banking firms. We focus on four core aspects of boards that have been addressed in Directive 2013/36/EU to strengthen the corporate governance of banks: the size of boards, their independence, the participation of female directors and CEO duality. We employ panel data analysis to study the 109 European listed banks between 2002 and 2019. Default risk is estimated by Merton’s (1974) distance to default. We take into account the presence of unobservable heterogeneity, simultaneity and dynamic endogeneity and estimate the model using the dynamic difference and dynamic system GMM methodologies. The results show that the size of the board influences banks’ default risk. Furthermore, bank size, firm profitability and GDP also exert a considerable influence.

… , in that we consider explicitly the probability of default for a fairly large sample of US banks.… default probabilities of banks, we also consider the impact on default risk of several corporate …

… This article introduces a relatively straightforward, risk-neutral model that uses multiperiod corporate bond default rates. This simple approach has the advantage of being highly intuitive …

… This paper develops a structural bond pricing model with liquidity and credit risk. The purpose is to enhance our understanding of both the interaction between these two sources of …

We present a model with agency costs where heterogeneous firms raise finance through either bank loans or corporate bonds and where banks are more efficient than the market in resolving informational problems. We document some major long‐run differences in corporate finance between the United States and the euro area, and show that our model can explain those differences based on information availability. The model fits the data best when the euro area is characterized by lower availability of public information about corporate credit risk relative to the United States, and when European firms value more than United States firms banks’ flexibility and information acquisition role.

… of credit risk publications over the last five and half decades. Given the mounting evidence and literature on credit risk, this … When a company seeks debt financing, say through a bond, a …

… Our first dependent variable is the firm’s cost of debt financing, which we define as the … We include the modified bond rating to control for additional credit risk determinants that are …

Accurate assessment of credit risk can improve the performance of bond portfolio managers. Using credit ratings and market-based credit risk models from S&P and Bloomberg, we investigate the performance of four credit risk models in the Rule 144A corporate bond markets in the United States over the 1990–2015 period. The authors divide their sample into straight bonds and convertible bonds and find that (1) when it comes to straight bonds, discrete models such as S&P’s credit ratings and Bloomberg ratings determine yields more accurately than the continuous market-based models of S&P and Bloomberg; (2) with regard to convertible bonds, a convertible option has a stronger effect than credit ratings in determining yields, and only Bloomberg default risk ratings, not S&P credit ratings, determine the yields; (3) for convertible bonds, the continuous market-based models of S&P and Bloomberg affect yields more significantly than discrete models; and (4) when it comes to predicting actual defaults, Bloomberg models are superior to S&P’s models, and the Bloomberg discrete model has more power than its continuous counterpart.

… bond class, the senior bond receives the entire par value… Credit risk is redistributed in such a way that the senior bond class has a lower credit risk, while the subordinate bond class has …

… of credit risk. The model measures the effective spread curve implied in the price of each bond, isolating the impact of credit risk … of value in corporate bonds. The spreads on corporate …

… for the four categories of credit risk variables, list the specific series in each category, and explain our expected relationship of each credit risk variable with the HY bond CRS series. …

… We also consider the problem of integrating credit risk in a government bond portfolio in an indexed-fund framework. In the numerical studies below we backtest the performance of the …

In 2022, various Chinese regulatory authorities, including the China Securities Regulatory Commission (CSRC) and the State‐owned Assets Supervision and Administration Commission (SASAC), jointly issued documents and convened seminars at the Shanghai and Shenzhen Stock Exchanges to highlight the importance of credit protection tools, particularly credit risk mitigation warrants (CRMWs), to bolster corporate bond market financing. However, empirical research on the factors influencing CRMW prices in the Chinese market remains scarce. This study examines the factors affecting CRMW creation prices using a sample of CRMWs issued between 2018 and 2023 and analyzes the macroeconomic factors, characteristics of the underlying issuers, and related financial instruments. This study employs Shapley decomposition to evaluate the contribution of each variable. The findings reveal that, on the macroeconomic front, the year‐on‐year GDP growth rate from the previous quarter and the loan balance of financial institutions significantly and negatively impact CRMW prices. Regarding the underlying issuers, the issuer's credit rating and return on net assets significantly negatively affect CRMW prices, whereas the asset–liability ratio and issuer age have a significant positive impact. Additionally, related financial instruments such as bond interest rates and the risk mitigation certificate term positively contribute to the coverage of the underlying bond term and have a significant positive impact on CRMW prices.

… from the same issuer, so this result does not reflect differences in credit risk (or other factors) across issuers within the same rating category. Again, this finding is consistent with previous …

… to the municipal government bonds, though these two are … bonds are exposed to not only the credit risk, but also the macroeconomic risk, industry risk, policy risk, and even a unique risk …

Following the introduction of the euro, the markets for large debt financing experienced a historical expansion. We investigate the financial factors behind the issuance of syndicated loans for an extensive sample of euro area non-financial corporations. For the first time, we compare these factors to those of its major competitor: the corporate bond market. We find that large firms, with greater financial leverage, more (verifiable) profits and higher liquidation values tend to choose syndicated loans. In contrast, firms with more short-term debt and those perceived by markets as having more growth opportunities favour financing through corporate bonds. Syndicated loans are the preferred instrument at the extreme where firms are very large, profitable but have less growth opportunities.

… As an illustration, we present a decade of one-year default rates on all corporate bonds that are rated by Moody’s Investment Service in Table I. We also provide information on the rate …

A scientifically systematic credit evaluation system serves as a crucial safeguard mechanism for maintaining a healthy business environment in the construction market, effectively regulating industry entities’ behaviors and promoting ecosystem optimization. Current credit risk assessment relies excessively on financial data, neglecting the importance of corporate operational conditions. This study focuses on constructing a credit risk assessment model for construction general contractors. Innovatively incorporating both short-term financial status and long-term operational development factors, the research integrates grey relational analysis with a two-dimensional cloud model to establish a comprehensive credit risk assessment system featuring visualization of evaluation results. The methodology involves three key steps: (1) establishing a dual-dimensional credit risk indicator system covering financial and operational aspects; (2) determining risk factor weights through grey relational analysis and generating three-dimensional cloud diagrams using reverse cloud generators; (3) visualizing corporate credit risk levels through cloud mapping. Empirical analysis of representative Contractor A, utilizing Wind Financial Database data and field research, demonstrates the model’s significant advantages in critical risk factor identification and comprehensive credit risk assessment.

… Finally, the idiosyncratic short-term debt structures of construction firms desire close attention, and this study recommends Leland and Toft’s (1996) model for further investigations. The …

… This gives rise to the need for developing the BOT credit risk model in this paper, which is … The calculated default probability by the BOT credit risk model as developed in this paper for …

Under the influence of COVID‐19, although upstream small‐ and medium‐sized enterprises (SMEs) in construction industry chain suffer from high operating costs and tight cash flow problems, their financing demands are even stronger. As an electronic and platform‐based comprehensive service, online supply chain finance can ease the financing problems of SMEs in the construction industry. However, it has become an important issue for financial institutions to effectively assess the credit risk in the process of online supply chain financing. In this paper, an online supply chain risk assessment method, which is based on a hybrid model chain, including eXtreme Gradient Boosting (XGBoost), Synthetic Minority Oversampling TEchnique for Nominal and Continuous (SMOTENC), and Random Forest (RF), is proposed to identify and control the credit risk of financial institutions. Specifically, we establish the financial credit risk assessment system with respect to the characteristics of financing enterprises in the construction industry supply chain, including the status of financing enterprises, the status of core enterprises, the operating status of the supply chain, and the status of assets under financing. On the basis of the system, the best index number of the assessment system and the minority samples are obtained by the XGBoost algorithm and the SMOTENC algorithm, respectively. The classification method based on RF is applied to judge the credit risk of financing enterprises in the supply chain of construction industry. In the simulation stage, we take upstream SMEs in the supply chain of construction industry in China as an example for empirical analysis to validate the effectiveness of our proposed method. The credit risk assessment method proposed in this paper has better performance than the commonly used ones in the academic field with an average improvement on assessment accuracy for 6.39% and an average increase of Area Under Curve for 6.95%. Our study provides meaningful exploration on the fund monitoring system of the financing service platform to improve financing efficiency and risk management level.

… models to measure the construction contractor default risk. The empirical results of … construction contractor default prediction, and they provide an alternative to measuring construction …

Small and Medium-sized Enterprises (SMEs) are vital to the modern economy, yet their credit risk analysis often struggles with scarce data, especially for online lenders lacking direct credit records. This paper introduces a Graph Neural Network (GNN)-based framework, leveraging SME interactions from transaction and social data to map spatial dependencies and predict loan default risks. Tests on real-world datasets from Discover and Ant Credit (23.4M nodes for supply chain analysis, 8.6M for default prediction) show the GNN surpasses traditional and other GNN baselines, with AUCs of 0.995 and 0.701 for supply chain mining and default prediction, respectively. It also helps regulators model supply chain disruption impacts on banks, accurately forecasting loan defaults from material shortages, and offers Federal Reserve stress testers key data for CCAR risk buffers. This approach provides a scalable, effective tool for assessing SME credit risk.

… credit risk and financial stability, addressing three objectives: (1) identifying and characterizing zombie firms … highest in Mining and Construction, while the share of zombie firms is most …

… (2018) further emphasize that credit scoring system makes it possible for … a firm's credit risk, minimizing uncertainty in investment decisions. Second, the construction of social credit …

We study the world largest credit risk losses from the year of 1972. We expect that such events drove the credit risk regulation development by the Basel Committee on Banking Supervision, including that of the Internal Ratings-Based (IRB) one of the Basel II Accord. By choosing a round threshold of current USD 100m equivalent of loss amount and the entity total assets in excess of current USD 500m as ofthe loss announcement date, we collected the dataset of 56 cases with the total credit loss of the current USD 700bn (or ca. 900 constant 2018 USD bn) which occurred during the last half of a century. We provide granular description of the stylized facts that characterize five typical credit risk evolution scenarios. The two most unexpected findings are as follows. First, we verified the announced loss amounts by analysis of stock quotes dynamics around the loss announcement dates. Thus we were able to trace three cases where announced by mass media losses may seem to have been exaggerated. Second, there is a series of events when there was a disclosure combination of credit risk loss and operational one. It is likely that the latter might have been used to partially cover the former.

The objective of the research is to analyze the ability of the artificial neural network model developed to forecast the credit risk of a panel of Italian manufacturing companies. In a theoretical point of view, this paper introduces a litera-ture review on the application of artificial intelligence systems for credit risk management. In an empirical point of view, this research compares the architecture of the artificial neural network model developed in this research to an-other one, built for a research conducted in 2004 with a similar panel of companies, showing the differences between the two neural network models.

We study whether climate transition risk is reflected in the credit default swap (CDS) spreads of European firms. Using information on the vulnerability of a firm’s value to the transition to a low-carbon economy, we construct a climate transition risk (CTR) factor, and report how this factor shifts the term structure of the CDS spreads of more but not of less vulnerable firms. Considering the CTR factor, we find that different climate transition policies have asymmetric and significant economic impacts on the credit risk of more vulnerable firms, and negligible effects on less vulnerable firms.

Abstract This study examines the impact of relationship banking and collateral on the probability of firm loan default in Kosovo. Using a sample of 2,320 loan-level data from an individual bank credit register, findings indicate that stronger firm-bank relationships reduce the probability of default, and tighter credit policies regarding higher collateral requirements and interest rates have the opposite effect. Re-specifying the model to control for the banking sector concentration Hirschman-Herfindahl Index (HHI) and the Net Interest Margin (NIM), the firm-bank relationship is no longer statistically significant. Results show that the crisis negatively impacts credit risk, while HHI positively affects the probability of loan default. This evidence suggests that banking relationship matters only in competitive markets. To test the potential interaction effect between relationship banking and collateral, Fairlie’s (1999) decomposition technique is deployed. Our results imply that high concentration levels in the banking sector render firm-bank relationships relatively less important. This is of utmost importance for SMEs, banks, and policymakers.

… classes, this paper proposes the credit risk prediction model for financial data with imbalanced classes, including the data preprocessing, the construction of base classifier and the …

… requirements concerning firms’ credit risk under the risk-… study is the risk weight function, since it provides the risk-weighted … observed in exposures to firms in the construction sector, that …

… This paper aims to construct a credit risk measurement indicator system for SMFs in the context of internet financing. The proposed system consists of a goal layer, a criterion layer, an …

… Our findings are also related to several studies that investigate the determinants of corporate bond prices. The idea that both default probabilities and risk premia affect bond prices and …

… It also makes use of an extensive longitudinal data set to test these effects on both the corporate cost of debt and the credit ratings assigned to the corporate bonds and control for a wide …

… defaulting bond that has an investment-grade credit rating one year before its default date. A Type II error is a false warning, which I define as a bond that has a speculative-grade rating, …

Undoubtedly, it is important to have an empirically effective credit risk rating method for decision-making in the financial industry, business, and even government. In our approach, for each corporate bond (CB) and its issuer, we first propose a credit risk rating (Crisk-rating) system with rating intervals for the standardized credit risk price spread (S-CRiPS) measure presented by Kariya et al. (2015), where credit information is based on the CRiPS measure, which is the difference between the CB price and its government bond (GB)-equivalent CB price. Second, for each Crisk-homogeneous class obtained through the Crisk-rating system, a term structure of default probability (TSDP) is derived via the CB-pricing model proposed in Kariya (2013), which transforms the Crisk level of each class into a default probability, showing the default likelihood over a future time horizon, in which 1545 Japanese CB prices, as of August 2010, are analyzed. To carry it out, the cross-sectional model of pricing government bonds with high empirical performance is required to get high-precision CRiPS and S-CRiPS measures. The effectiveness of our GB model and the S-CRiPS measure have been demonstrated with Japanese and United States GB prices in our papers and with an evaluation of the credit risk of the GBs of five countries in the EU and CBs issued by US energy firms in Kariya et al. (2016a, b). Our Crisk-rating system with rating intervals is tested with the distribution of the ratings of the 1545 CBs, a specific agency’s credit rating, and the ratings of groups obtained via a three-stage cluster analysis.

… After examining the 10-year rating-change experience of these corporate bonds, we looked … credit ratings based on a loan's bond-rating equivalent. Given evidence of bond-rating drift, …

In this paper, a new model for corporate bond pricing and credit rating is proposed. In this model, credit rating migrations are assumed to depend on the ratio of debt and asset value of the underl...

… indicating rating inflation problems in structured bond markets,5 … rating agencies in the corporate bond market does not lead to rating inflation suggests that regulation reform in the credit …

… inflated ratings due to the issuer-pay business model. This paper analyzes the corporate bond rating … the rating agencies. Through a simple theoretical model, I demonstrate that due to …

This article examines the relationship, commonly assumed by the literature, between credit rating and liquidity. Analyzing sixteen proxies of (il)liquidity encompassed in five dimensions of liquidity from transaction data, the authors observe that credit ratings can be grouped into four blocks based on their liquidity when working with bond-level data. The blocks are AAA/AA and A/BBB for investment-grade bonds, and BB/B and “CCC or lower” for speculative-grade bonds. However, when working with rating-level data, significant differences in liquidity appear between all rating categories. These liquidity differences are not homogeneous when a distinction is made between dimensions of liquidity.

ABSTRACT This article investigates the nexus among the liquidity measures, credit ratings, and the yield spreads of green corporate bonds in China using panel data analysis and the generalized method of moments (GMM). Lower market liquidity, a lower credit rating level, and a shorter issued age are more significant for enlarging the yield spreads of ordinary corporate bonds than those of green corporate bonds. Compared with the AAA credit rating level, the illiquidity ratio, nontrade frequency ratio, zero-trade volume, yield volatility, interest rate margin and issued age have more significant influences on the yield spreads of ordinary corporate bonds than those of green corporate bonds. The liquidity and credit rating have greater differences in affecting the yield spreads of green corporate bonds with different issuance terms.

… to predict bond ratings. First he regressed the ratings of a sample of corporate bond issues on … equations to predict the ratings on two other samples of bond issues. The sample Horrigan …

The default of corporate bonds can result in large financial losses as well as irreparable harm to investors’ trust and the economy as a whole, which implies that the identification of corporate bond default must be done promptly and properly. Current studies mainly rely on accounting and/or macroeconomic data and use the credit rank (CR) to disclose the credit status of corporate bonds in the default prediction task. However, the textual data of credit rating reports (CRRs) contain richer and more comprehensive information and are neglected in related work. In this paper, we propose a novel framework that draws on the unstructured data in CRR to predict the default of corporate bonds. We extract the rating opinion sentences (categorized as positive and negative) from the collected CRR files and use latent Dirichlet allocation (LDA) models to mine topic information. The bilateral topic information of positive and negative opinions can reflect the anti-risk ability and potential risk of corporate bonds, respectively, based on which the constructed topic features are used for default prediction. Results on real-world Chinese corporate bonds dataset show that the bilateral topic information of CRR can significantly improve the predicting power of models (LR, SVM, KNN and MLP) under three performance metrics (AUC, KS and H-measure). By analyzing the ranking of topic features using SHAP value, the proposed framework can explain the factors that affect bond defaults, which can provide a basis for the decision-making of investment behavior.

… of bond credit ratings by independent rating agencies began in the United States early in the twentieth century, bond … when corporate bond markets and the business of ratings agencies …

… the credit ratings assigned to Japanese non-financial corporations by Japanese and foreign rating agencies. More of the variance in Japanese than foreign agency ratings can be …

ABSTRACT In recent years, green financing has attracted global attention. Many countries and international organizations have proposed frameworks for developing green financing, and an increasing number of companies issue green bonds as financial instruments for funding green projects. Unlike conventional bonds, green bonds have unique features, and their issuance follows a special process. We use data on Chinese green bonds in a linear regression model to empirically explore the impact of credit ratings, corporate social responsibility (CSR), and green certification on yield spreads. The results show that these factors all have a significant impact on interest costs. Issuing green bonds is a signal of CSR, and green bonds with green certificates have lower interest costs than those without them. Finally, we outline some policy implications regarding the governance of green bonds based on our findings.

… corporate governance affects bond issuer credit ratings in China. After controlling for firms’ financial attributes, we find that issuer ratings … In this paper, we focus on the corporate bonds …

We investigate the nascent but fast-growing Chinese bond market and credit rating industry. We find Chinese bond ratings are informative and significantly correlated with bond offering yields. In addition, the Chinese bond investors distinguish ratings from different credit rating agencies (CRAs), demanding lower yields on bonds rated by global-partnered CRAs. However, the empirical results suggest that the rating scales used by Chinese CRAs are not comparable to those of international CRAs. Furthermore, Chinese CRAs have very broad rating scales and pool bonds with significantly different default risks into a single rating category, resulting in over 90% of bonds in only three rating categories.

A firm's instantaneous probability of default is modeled as a square-root diffusion process. The parameters of these processes are estimated for 188 firms, using both the time series and cross-sectional (term structure) properties of the individual firms' bond prices. Although the estimated models are moderately successful at bond pricing, there is strong evidence of misspecification. The results indicate that single factor models of instantaneous default risk face a significant challenge in matching certain key features of actual corporate bond yield spreads. In particular, such models have difficulty generating both relatively flat yield spreads when firms have low credit risk and steeper yield spreads when firms have higher credit risk.

We price corporate debt from a structural model of firm default. We assume that the capital market brings about efficient firm default when the continuation value of the firm falls below the value it would have after bankruptcy restructuring. This characterization of default makes the model more tractable and parsimonious than the existing structural models. The model can be applied in conjunction with a broad range of default-free interest rate models to price corporate bonds. Closed-form corporate bond prices are derived for various parametric examples. The term structures of yield spreads and durations predicted by our model are consistent with the empirical literature. We illustrate the empirical performance of the model by pricing selected corporate bonds with varied credit ratings.

… of future states with both high default risks and high SDFs—namely… future states with high default risks where SDFs can be “… which are associated with high default risks. The market may …

… This paper extends the default model of yield spreads for bonds by showing that, in general, they are a complex function of maturity and, in particular, are not always monotonically …

… determinants of country default risk in emerging markets, reflected by sovereign yield spreads. The … We argue that yield spreads are advantageous in capturing default risk to using …

Cet article étudie la valeur de la dette en présence de risque de défaut dans une approche en temps continu. En considérant une dette avec des remboursements réguliers de nominal (à l’instar d’un fonds d’amortissement), nous sommes en mesure d’examiner des obligations à maturité quelconque tout en conservant un cadre d’analyse homogène par rapport au temps. Cela nous permet de prolonger les résultats analytiques de Leland (1994) à une classe plus large de structures de dette. Nous étudions la structure par terme des écarts de rendement et nous obtenons qu’une augmentation des taux d’intérêt diminue la prime des émissions de dette actuelles et peut aussi renverser la structure par terme. La duration est également affectée par le risque de défaut. La mesure traditionnelle de Macaulay surestime la duration effective qui peut même s’avérer négative pour les obligations à haut risque. Tandis que la dette à court terme n’exploite pas pleinement l’avantage fiscal comme le fait la dette à long terme, elle a davantage tendance à aligner les intérêts des créanciers et des actionnaires. Les coûts d’agence liés à la substitution d’actifs sont minimisés lorsque l’entreprise se finance avec de la dette à plus court terme. La structure optimale du capital dépend de la maturité de la dette. Le niveau d’endettement optimal est ainsi plus faible, et la valeur de l’entreprise est moindre, lorsque la dette à court terme est utilisée. L’écart de rendement au levier optimal augmente avec la maturité de la dette.

… default and nondefault components in corporate spreads. We find that the majority of the corporate spread is due to default risk… to the definition of the riskless curve. We also find that the …

… Recognizing that the default risk problem is multi-dimensional, we … default risk. In section III, we study the appropriateness of using yield spreads for cross-sectional inter-firm bond risk …

… This paper develops a model of bond prices and yield spreads that incorporates the effect of both taxes and differences in default probabilities. The tax loss consequences of default are …

This paper attempts to explain the yield spreads charged to new corporate debt issues by comparing the initial yields of a set of 3,287 securities issued over eleven years in the US. We use the measure of constant maturity Treasury rates on the day of issue against the Moody’s Aaa Corporate Bond index for the week prior to the issue, and the yield on a daily index of long-term Treasury securities on the issue date. The influences of credit ratings and disagreement between rating agencies as reflected in split ratings and the interactions between these characteristics are measured. The contributions of sinking fund provisions, call or refunding status, overseas issue and contractual security arrangements are evaluated separately. The results support the view that the higher yields are observed when ratings of agencies differ and that factors associated with the issues also are significant drivers of the yield difference.

… The model is applied to a data set of 382 individual high-yield nonconvertible corporate bonds issued between 1977 and 1989. Our results show that the default risk of high-yield bonds …

ABSTRACT We find that liquidity is priced in corporate yield spreads. Using a battery of liquidity measures covering over 4,000 corporate bonds and spanning both investment grade and speculative categories, we find that more illiquid bonds earn higher yield spreads, and an improvement in liquidity causes a significant reduction in yield spreads. These results hold after controlling for common bond‐specific, firm‐specific, and macroeconomic variables, and are robust to issuers' fixed effect and potential endogeneity bias. Our findings justify the concern in the default risk literature that neither the level nor the dynamic of yield spreads can be fully explained by default risk determinants.

… -paying bonds. Then we construct the implied yield spreads using the calculated bond prices… of CDS premiums to test whether the CDS market and bond market price credit risk equally. …

… decomposition of the default, liquidity, and tax factors that determine expected corporate bond returns. In particular, the risk premium associated with a default event is estimated. The …

We present a novel empirical benchmark for analyzing credit risk using “pseudo firms” that purchase traded assets financed with equity and zero-coupon bonds. By no-arbitrage, pseudo bonds are equivalent to Treasuries minus put options on pseudo firm assets. Empirically, like corporate spreads, pseudo bond spreads are large, countercyclical, and predict lower economic growth. Using this framework, we find that bond market illiquidity, investors' overestimation of default risks, and corporate frictions do not seem to explain excessive observed credit spreads but, instead, a risk premium for tail and idiosyncratic asset risks is the primary determinant of corporate spreads. (JEL E23, E32, E44, G13, G24, G32)

… financial indicators or require high-quality external textual disclosures that are often unavailable for many construction … performance for construction firm financial distress prediction. …

PurposeIn developing countries, delays in highway infrastructure projects caused by financial distress-related factors threaten the construction industry’s capacity to contribute optimally to economic development. Against this backdrop, this paper aims to determine factors contributing to financial distress and develops a conceptual framework to illustrate the relationship between financial distress and project delay.Design/methodology/approachA questionnaire survey collected data on factors that contributed to financial distress and delays in highway infrastructure delivery. In total, 78 responses were obtained, and factor analysis revealed that factors associated with payment, project financing, cash flow, economic issues, project planning and cost control influenced project delays.FindingsThe research identifies the importance of efficient public and private policies to engender financial sustainability among construction firms in developing countries.Originality/valueThis work presents the first research of its kind and strives to engender wider academic debate and renewed economic development in some of the world’s most impoverished nations.

This study uses machine learning techniques to construct financial distress prediction (FDP) models for Chinese A-listed construction companies and compares their classification …

… the financial distress level of the companies. This study aims to measure the financial health of listed construction … and significant financial ratios that are utilized to analyze the financial …

Purpose — This study aims to analyze the influence of profitability, leverage, and intellectual capital on financial distress in companies within the building construction sub-sector.Method — The research method involves quantitative and regression analysis. The sample consists of companies within the building construction sub-sector that consistently published financial reports during the period 2016-2021. The research population comprises 96 building construction companies listed on the Indonesia Stock Exchange, with 16 companies meeting the sample requirements. Data collection is performed using purposive sampling, and the analysis is conducted using EViews 10. Various tests, including classic assumption tests, feasibility analysis models, panel regression analysis, and coefficient of determination tests, are employed in the analysis.Result — The study results indicate a significant positive effect of profitability on the level of financial distress, suggesting that higher levels of profitability correspond to lower financial distress. Conversely, leverage demonstrates a significant negative effect on financial distress, implying that higher levels of leverage are associated with increased financial distress for the company. However, the study did not identify a significant relationship between intellectual capital and the level of financial distress, suggesting that the level of intellectual capital does not significantly influence the level of financial distress.Practical implications  — Management in the building construction sub-sector is encouraged to prioritize strategies and tactics aimed at enhancing company profitability. Focusing on efforts to improve operational efficiency, optimize asset utilization, and enhance the effectiveness of marketing strategies can contribute to an increase in the company's profitability.

… Financial distress is defined in this study as firms that either (1) were closed down by government authorities (all of which were banks and finance companies) or (2) were required by …

A method for predicting the financial status of construction companies after a medium-to-long-term period can help stakeholders in large construction projects make decisions to select an appropriate company for the project. This study compares the performances of various prediction models. It proposes an appropriate model for predicting the financial distress of construction companies considering three, five, and seven years ahead of the prediction point. To establish the prediction model, a financial ratio was selected, which was adopted in existing studies on medium-to-long-term predictions in other industries, as an additional input variable. To compare the performances of the prediction models, single-machine learning and ensemble models’ performances were compared. The comprehensive performance comparison of these models was based on the average value of the prediction performance and the results of the Friedman test. The comparison result determined that the random subspace (RS) model exhibited the best performance in predicting the financial status of construction companies after a medium-to-long-term period. The proposed model can be effectively employed to help large-scale project stakeholders avoid damage caused by the financial distress of construction companies during the project implementation process.

Purpose – The aim of this case study is to characterize the impact of the 2008 global financial crisis on the financial performance of public listed construction companies. Design/methodology/approach – Financial analysis was conducted on 32 public listed construction companies in Malaysia. Twelve financial ratios were examined to determine the profitability, liquidity, activity, leverage and solvency of these companies over the period between 2005 and 2010. This was complemented by a distress analysis using Altman’s Z-index. The study also used a content analysis of the Chairman’s or Managing Director’s statement to shareholders to uncover the responses and strategic initiatives undertaken by the management in response to the financial crisis. Findings – The only direct impact of the financial crisis was a reduction in profitability. Total revenues and total assets of these companies continue to grow due to increased demand for construction from year 2007 following two large capital investment programs initiated by the Malaysian Government to mitigate the potential effects of the financial crisis. Net profits rebounded back to 5 per cent by year 2010. These companies immediately responded to the crisis with more prudent financial management; curtailing expenses, cutting dividends, reducing bank borrowings, increasing equity; and to the extent of disposing of assets to mitigate losses. Research limitations/implications – The sample of only 32 public listed companies out of a total of more than 60,000 construction companies may be considered small, but these 32 companies represent nearly 20 per cent of the total construction volume for 2010. Practical implications – The study documents the effects of increased capital spending by the government to mitigate the loss of investor confidence followed by a slowdown in economic growth during a period of global financial distress. Key findings will inform on prudent financial management to withstand future financial crises. Originality/value – The responses and strategies adopted by the management to mitigate the effects and to enhance future performance of these companies have been uncovered. These are important considerations in managing construction companies; the analysis and observations will be invaluable to researchers intending to study how the construction industry responds to a future slump in demand.

This study examines how financial ratios affect heavy construction companies listed on the Indonesia Stock Exchange between 2018 and 2023 in terms of their likelihood of experiencing financial hardship. Utilizing logistic regression. The study analyzed 22 companies selected through purposive sampling using STATA. The findings show that return on equity has a substantial negative impact on the likelihood of financial distress; a higher retun on equity reduces the risk, while a lower retun on equity increases it, as observed in WSKT. Quick ratio significantly positively probability financial distress; lower Quick ratio raises the risk, as seen with MTPS. Debt to assets ratio also significantly increases probability financial distress; a higher Debt to assets ratio is associated with greater risk, exemplified by ASCT. Total assets turnover ratio is positively related to probability financial distress as demonstrated by TAMA. This study emphasizes how crucial it is to keep an eye on these financial measures in order to evaluate the danger of financial trouble and a company's overall stability.

Abstract In the bid process, predicting whether the contractor will suffer a financial crisis during the construction project is vital to project owners and other stakeholders for identifying problems and taking strategic action. In this context, the models for predicting financial crisis of contractor have been extensively studied. However, the previous studies have been focused on predicting a financial crisis for one-quarter or one-year ahead of prediction point, even though the duration of projects are relatively long in the construction industry, usually exceeding one year. Moreover, despite the possibility of knowing the signs of financial crisis of a contractor through predicting financial distress, no attempt has been made to predict financial distress that contractor can suffer before reaching a financial crisis including highly visible legal events, such as bankruptcy, default, and delisting. This means that there is significant gap between those models and practical application in terms of the prediction period and definition of the financial crisis. This study proposes voting-based ensemble models that predict financial distress of contractor for two- and three-year ahead of prediction point using a finance-based definition of financial distress. The prediction performance of proposed model was evaluated using financial statements of contractors in South Korea from 2007 to 2012. The proposed models showed area under the receiver operating characteristic curve (AUC) values of 0.940 and 0.910 for predicting financial distress for each of the prediction years. By predicting financial distress of the contractor from the early stages of a construction project to the end stage with high accuracy, this model can help project owners and broad stakeholders to avoid damage due to financial crisis during a project.

… Changes in the ownership structures and functions of various financial … credit risk. In the late 1970s, the Chinese government began its reform of banks, which were all state-owned and …

… , bank finance flowed to state-owned enterprises with higher subsequent productivity than did … conclude that bank employees assessed SOE credit risks substantially better than did the …

… Both banks face risks in the loan market. We show that if credit risk is sufficiently high and there is limited liability, the state-owned bank mitigates depositors’ losses by mobilizing less …

Purpose This paper aims to investigate the alterations in financing cost for non-state-owned enterprises (non-SOEs) subsequent to the introduction of state-owned shares through the lens of ownership structure. It also investigates the role of government background customer relationship as a possible factor that can mediate the relationship between state-owned shares and non-SOEs’ financing cost. Design/methodology/approach A sample of the Chinese A-shares listed non-SOEs from 2009 to 2022 is used in the study. The study applies multivariate regression analyses to investigate the impact of state-owned shares on non-SOEs’ financing cost. Baron and Kenny’s (1986) three-step model is estimated to test the mediating effect exerted by the government background customer relationship. Findings The findings indicate that state-owned shares are significantly negatively related to non-SOEs’ financing cost through extending government background customer relationship. In addition, this study finds that green innovation in non-SOEs diminishes this effect. Heterogeneity analyses reveal that the function of state-owned shares is more pronounced when state-owned shareholders are investment-oriented and during periods of heightened economic policy uncertainty. Finally, the results suggest that lower financing costs are shown to enhance enterprise resilience. Originality/value The authors analyze the possible homogeneous linkage between state-owned capital and the government and explore the possible indirect impact of state-owned shares on the financing cost of non-SOEs from the perspective of government background customers’ procurement demand. The results confirm the theoretical and practical usefulness of state-owned shares and provide new empirical evidence to alleviate the expensive financing of non-SOEs.

The global COVID-19 pandemic has greatly affected people, especially in the economic and banking sectors. The Indonesian Financial Services Authority (Otoritas Jasa Keuangan, OJK) issued a credit restructuring policy, effective from March 2020 to March 2022, to reduce credit and bank capital risk. This study proposes the bank risk scenario after the credit restructuring policy of the OJK moratorium in March 2022 and proposes the internal bank policy simulation to mitigate credit and capital risks in terms of Non-Performing Loan (NPL) and Capital Adequacy Ratio (CAR). The difficulty of this study is how to develop the risk scenario and to simulate the bank risk mitigation policy after the policy moratorium, while the COVID-19 pandemic is still ongoing and the economy is not yet normal. To that purpose, this study uses a system dynamics methodology with Powersim Studio 10© software that is able to make scenarios on the level of credit risk (NPL) and bank capital (CAR) and able to simulate internal bank policy to overcome the risk by considering the environmental and policy changes. Based on the policy simulation, it is recommended that bank can implement the restructuring policy to control the credit risk and strengthening the NPL monitoring activity in order to manage and decrease the loan impairment expenses. To increase CAR, the result shows that the combined policy consists of the NPL monitoring program, the interest rate and the operating cost management program is able to produce a significant increase in bank’s capital (CAR). The original contribution of this study is to provide new model of credit and capital risk scenario and risk mitigation simulation during the COVID-19 pandemic. The advantage of this study is that the model can be tested and implemented to other banks.

… that determines whether the state-owned commercial banks in … credit risk forms, put forward some countermeasures that aim to improve the state-owned commercial banks’ credit risk …

This article selects A-share state-owned listed companies in Shanghai and Shenzhen stock exchanges from 2007 to 2018 as samples and uses OLS together with intermediary effect tests to study the impact of state-owned enterprise’ (SOEs) policy burdens on credit resources and their allocation efficiency. The research finds that the heavier the policy burden SOEs assume, the more credit resources they obtained. However, they are also more likely to make inefficient investments after obtaining the credit resources, and these credit resources have a negative effect on the value of the SOEs which bear the policy burden. These negative impacts are more significant in SOEs with low degree of marketization in the region, low level of government control, and low information transparency. The path analysis elaborates that the policy burden of SOEs reduces the efficiency of resource allocation by increasing management agency costs and reducing financing constraints. The conclusions enrich the understanding of the consequences of policy burdens under the background of Chinese system, further broaden the analytical framework of the efficiency of credit resource allocation, and unveil the importance of relevant government departments that can optimize the efficiency of credit resource allocation.

Recent events, most notably the Global Financial Crisis and the COVID-19 pandemic, have made it increasingly apparent that liquidity is synonymous with corporate survival. In this paper, we explore how governments can fulfill an important need as suppliers of liquidity. Building on the financing advantage view of state ownership, we theorize how state-owned enterprises (SOEs) may provide capital by offering trade credit to customer firms. The data indicate a positive relation between the level of state ownership and the provision of trade credit. Using an institution-focused framework, we further determine that the nation’s institutional environment systematically affects the opportunities and motivations for SOEs to grant trade credit. Specifically, we find that SOEs grant more trade credit in countries with less developed financial markets, weaker legal protection of creditors, less comprehensive information-sharing mechanisms, more collectivist societies, left-wing governments, and higher levels of unemployment. Firm-level factors also influence the credit-granting decisions of SOEs, with SOEs with lower levels of state ownership and higher extents of internationalization offering lower amounts of trade credit. Overall, our study offers novel insights regarding the important role of state-owned firms as providers of liquidity.

Abstract The aim of this paper is to analyze the impact of credit risk on banks performance. The dataset consist of 26 commercial banks operating in Turkey between 2005 - 2017. The secondary data collected from the statistical report of the Banks Association of Turkey. Three panels’ data are considered respectively state-owned banks, privately-owned banks and foreign banks in order to compare banks according to their ownership structure. Return on Asset (ROA) and Return on Equity (ROE) were used as proxies for financial performance indicators while Non-Performing Loans (NPLs) was used as credit risk indicators. The estimation results showed that there is a negative relationship between credit risk and ROA as well as between credit risk and ROE. This result suggest that there is a relationship between credit risk management and profitability of Turkish deposit banks from the period of 2005 to 2017. Accordingly, banks should focus more on credit risk management, especially on the control and monitoring of non-performing loans. In addition, managers should focus more on modern credit risk management techniques.

… In terms of the ownership structure, the mixed-ownership reform of state-owned enterprises … environment, firms are incentivized to truthfully disclose information to reduce credit risks, …

… Therefore, in this paper we follow the Code of Good Practice of Exercise of State-owned Corporation Shares set by Chinese regulators, and use net assets per share to represent the …

… of foreign strategic investors (FSIs) on bank credit risk in China and tested the possible disclosure … Based on the superposition effect, the study concluded that FSIs improved credit risk …

This study investigates the effect of corporate environmental, social, and governance (ESG) performance on credit risk using a sample of manufacturing firms listed on China's Shanghai and Shenzhen A‐share markets from 2009 to 2021. Employing fixed effects, the generalised method of moments, and instrumental variable models, we find that stronger ESG performance is significantly associated with lower credit risk, as measured by the distance to default. Mediation analysis reveals that this relationship operates primarily through enhanced profitability and improved external governance. In contrast, Tobin's Q acts as a negative channel, potentially reflecting market overvaluation and inefficiencies. ESG's impact also varies across firm types: the risk‐reducing effect is most pronounced among non‐state‐owned enterprises (NSOEs), firms based in eastern provinces, and those in the growth or decline stage of the corporate lifecycle. Further analysis shows that environmental (E) and social (S) pillars drive credit improvements, whereas the governance (G) score has an insignificant effect. Our findings provide theoretical and empirical insights into the ESG–credit risk nexus, highlighting the importance of sector‐specific, regionally sensitive ESG strategies in emerging markets.

Purpose There is a relatively low risk management (RM) level and maturity in China’s state-owned construction enterprises (CSCEs). The purpose of this paper is to find the main factors impacting RM in practice to promote rapid, sound and sustained development in CSCEs. Design/methodology/approach There are a few state-owned CSCEs in China. Most enterprises know little about RM. Because of the limited number of RM departments in these enterprises, 200 questionnaires were sent to the enterprises to investigate the RM strategies employed by them. The research is quantitative and used a questionnaire survey to determine the important factors influencing RM practice. The collected data were analyzed with the Statistical Package for the Social Sciences to identify the most important factors affecting RM as well as the extent of influence of these factors, in order to facilitate further research. Findings The survey revealed the top eight factors (i.e. leaders’ support, personnel’s responsibility, comprehensiveness of identification, costs and benefits, risk appetite, understanding of language, frequency of training and performance management) that highly impact RM in CSCEs and the extent to which these factors impact RM. The data reveal that the average RM level is low. Some methods have been recommended to improve RM. Research limitations/implications The research lays the foundation for further RM development in CSCEs. The low RM level in CSCEs should encourage researchers to find better ways to improve RM. Some factors in the research will function as valuable guides for China’s private and public-private partnership enterprises. Practical implications A quantitative analysis methodology for RM has been developed for CSCEs that can reflect their RM level. In addition, the degree of impact of key factors on RM has been shown. The results can act as a reference to improve RM quantitatively, making the RM system more explicit in dealing with risks more accurately and instructively. Originality/value Structural RM research is utilized to evaluate RM in CSCEs by following an empirical method. With the continuous improvement in RM, CSCEs can cooperate well with construction enterprises of other countries for infrastructure projects and gain more benefits.

Purpose The purpose of this paper is to examine the association among operational risk incidents, corporate governance, credit risk and firm performance. Design/methodology/approach First, the authors regress corporate credit risk on the incurrence of operating losses (driven by operational risk events) and corporate governance variables. The purpose is to test the correlation between operational risk, corporate governance and credit risk. Second, in the authors’ next regression, the authors’ dependent variable is firm performance, and the independent variable is operational risk and corporate governance to test the correlation between operational risk, corporate governance and firm performance. In this study, the authors measure corporate governance using four surrogates, focusing on CEO duality, extent of independent board members, extent of foreign ownership and board member presence ratio. Findings The authors’ findings indicate that the higher level of operational risk incidents is linked to higher likelihood of credit default and to poorer performance. More importantly, the authors find that higher-quality corporate governance is associated with lower levels of operational risk incidents, better performance and lower likelihood of credit fault. Originality/value The authors use a rigid theoretical and empirical framework to examine the association among the incidents of operational risk, credit risk, corporate governance and firm performance. The authors’ study is important because it first facilitates understanding of causes leading to operational risk, and second if and how greater financial effects of operational risk negatively influences operating performance and credit risk of nonfinancial institutions in emerging markets.

The lack of accessible credit is a constitutive constraint on small businesses in the US. This paper proposes, evaluates, and establishes an interpretable, bias-conscious machine learning approach for small business credit risk assessment. Based on anonymized application, repayment, and organizational operational data, we compare gradient boost, regularized generalized linear models, and tree-based learning to industry-leading scorecards, leveraging monotonic constraints, fairness-conscious weight adjustments, and a SHAP explanation layer. The research hypothesis is to validate whether machine learning systems can strengthen default AUC/KS performance while decreasing disparities in group error rates, along with increasing approval rates at equal risk. Some uplift assessment measures incremental safe approvals, as well as expected loss subject to constrained decision rules. For more comprehensive implementation, the research includes model cards, feature management, WOE/IV, feature stability, as well as champion-champion comparisons. The findings of this research confirm the hypothesis, suggesting interpretable machine learning can achieve higher levels of risk differentiation (∅AUC > X), significantly close error gaps (∅gap > Y%), and achieve inclusivity gains at equal portfolio loss. The research aims to contribute a reproducible workflow, a set of metrics, as well as evidentiary validation of the applicability of transparent machine learning in credit markets.

The development of blockchain-driven supply chain finance aimed to solve the financing problems of SMEs. However, credit risk is expanded, and even transmitted to the whole supply chain, due to their connection, so that it becomes more difficult to effectively identify the credit risk of SMEs. The purpose of this paper was to examine the factors affecting SMEs’ credit risk in the mode of block-chain-driven supply chain finance. This research proposed an entropy weight method to construct independent variables and used logistic regression to examine whether the financing enterprises, core enterprises, assets position under financing, blockchain platform, and supply chain operation have significant impacts on credit risk. The panel data, originating from CSMAR on fifty-six quoted SMEs, included eight core enterprises and twenty-six blockchain enterprises, between 2016 and 2020. The results showed that the financing enterprises, core enterprises, asset position under fi-nance, blockchain platform, and supply chain operation have significant impacts on SMEs’ credit risk when the confidence level is 90%. The financial status of financing enterprises can reflect the credit status of SMEs. Core enterprises give credit guarantees to SMEs, and the business transactions between SMEs and core enterprises affect the credit risk through the asset position under financing. Meanwhile, blockchain platforms can solve the problem of the information asymmetry of the par-ticipating enterprises in supply chain operations. At the same time, the supply chain operation is also an important factor affecting the credit risk. This conclusion provides a reference for the ap-plication of blockchains in supply chains, to reduce the credit risk. At the same time, the selected indicators were more comprehensive, which provided a strong basis for the subsequent construc-tion of a credit risk assessment model using key factors.