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Diamond (1991) argues that a firm's reputation determines whether it borrows directly or through an intermediary. We test the Diamond model by examining the quantity response of commercial paper issued by bank holding companies to a rating downgrade. From 1986 to 1991, cumulative abnormal declines averaged 6.69 percent in the first two weeks after the downgrade and 11.05 percent in the subsequent 12 weeks. In contrast to commercial paper issued by bank holding companies, large CDs issued by affiliated banks did not change significantly in the period around a downgrade, suggesting that deposit insurance may have removed market discipline from the CD market. [ABSTRACT FROM AUTHOR]

The present paper investigates the relationship between stock prices, credit ratings, and ESG scores for banks internationally. First, it describes stock prices and ESG scores at an annual frequency, as well as stock price and credit risk at a daily frequency. The relationships between (a) stock price and credit rating returns with ESG score returns and (b) among ESG scores are examined by pairwise annual correlation, and daily correlations are examined between price and credit rating returns. Furthermore, Granger causality is used to examine the relationships between the following: (a) price and ESG score annual returns; (b) price and credit rating daily returns; and (c) total and pillar annual ESG scores. This study makes a significant contribution to the literature by providing a detailed temporal analysis using both annual and daily data frequencies, which is relatively rare in the field. There is evidence of statistically and empirically important relations in the form of pairwise correlations. The regressions reveal a low significance of few ESG score changes in explaining credit rating changes. A unique aspect of this paper is the comprehensive analysis of 16 granular ESG scores, including overall scores, pillar scores, and sub-scores, allowing for a multi-faceted understanding of how specific ESG factors impact financial metrics. We found evidence of the significance of COVID-19 in all research questions. Additionally, this paper highlights the impact of the COVID-19 pandemic on the relationships between ESG scores, credit ratings, and stock prices, offering timely insights into the heightened importance and volatility of ESG factors during crisis periods. Future research needs to shed more light on this relationship, however. [ABSTRACT FROM AUTHOR]

Credit rating agencies (CRAs) play an essential role in efficiently allocating capital in the financial system. However, several researchers have highlighted shortcomings of CRAs, leading to drastic consequences such as the 2008 financial crisis. The paper focuses on the competition among CRAs as one of the critical drivers of issues plaguing the credit rating industry. The paper uses quantitative and regression techniques to check the impact of competition on a firm's credit rating. The paper utilizes firm-level financial and credit rating data from India. The paper uses dual ratings to check whether competition leads to rating inflation and shopping practices in the credit rating industry. The paper finds that CRA inflates a firm's credit rating due to competition from other CRAs. Rating shopping is also evident in the credit rating industry, driven by competition between CRAs to gain new clients. The paper's findings also indicate that increased competition for large-size firms business leads to CRAs showing leniency when rating such firms. The results raise the need for regulators to actively monitor and control the competition among CRAs to ensure the accuracy of credit ratings. [ABSTRACT FROM AUTHOR]

With the rapid development of e-commerce systems, the centralized service model gradually fails to meet the needs of SMEs. In the existing centralized e-commerce system, users' transaction data and reputation scores are stored in a centralized cloud server, which has high storage cost, low processing efficiency, and the data is vulnerable to attacks and leaks. However, the existing decentralized e-commerce systems more than its reputation system to store the average credit score evaluation are receiving unfair evaluations against risk. The system of malicious nodes and no disciplinary measures, is not conducive to system development. To solve this problem, this paper proposes a blockchain-based decentralized e-commerce transaction system. The system commodity information is stored in the Interplanetary File System (IPFS) and the returned commodity addresses are stored in the blockchain to enhance the service performance. This paper proposes a reputation evaluation model based on multi-criteria decision making (MCDM), which can effectively resist unfair evaluation and collusion attacks, and proposes an incentive mechanism based on reputation value to reward and punish nodes, thus promoting the good circulation of the system. We implement the proposed system based on Ethereum. The experimental results show that the system has a small communication cost, accurately reflects the user's reputation value, and has good availability and reliability. [ABSTRACT FROM AUTHOR]

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

Consensus plays a crucial role in blockchain technology, with the deleted proof of stake (DPoS) consensus mechanism commonly utilized in both public and hybrid chains. However, the current DPoS mechanism faces challenges such as low node engagement in voting and potential security risks posed by malicious nodes. In response, we propose the DL-DPoS (deep link–delegated proof of stake) mechanism, which builds upon the DPoS framework. The DL-DPoS incorporates the LINK incentive mechanism to encourage inactive nodes to participate in voting and leader selection. Furthermore, a comprehensive credit scoring system based on wealth, performance, and stability is introduced to enhance the security of elected nodes. The verification process is optimized to involve all nodes except the leader node, and mechanisms are in place to handle malicious attacks by degrading or removing offending nodes and redistributing their responsibilities to the LINK group. Performance testing of the DL-DPoS mechanism, conducted through blockchain simulation tests using the GO language, shows a 23% increase in throughput compared to DPoS, with over 95% node participation and improved distribution of rights and equity. These results indicate the enhanced performance, security, and stability of the DL-DPoS consensus mechanism. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to assess and predict sovereign credit risk for Egypt, Morroco and Saudi Arabia using credit default swap (CDS) spreads obtained from the DataStream database for the period from 2009 to 2022. Our approach consists of generating the implied default probability and the corresponding credit rating in order to estimate the term structure of the implied default probability using the Nelson–Siegel model. In order to validate the prediction from the probability term structure, we calculate the transition matrices based on the implied rating using the homogeneous Markov model. The main results show that, overall, the probabilities of defaulting in the long term are higher than those in the short term, which implies that the future outlook is more pessimistic given the events that occurred during the study period. Egypt seems to be the country with the most fragile economy, especially after 2009, likely because of the political events that marked the country at that time. The economies of Morocco and Saudi Arabia are more resilient in terms of both default probability and credit rating. These findings can help policymakers develop targeted strategies to mitigate economic risks and enhance stability, and they provide investors with valuable insights for managing long-term investment risks in these countries. [ABSTRACT FROM AUTHOR]

To enhance the precision of predicting enterprise credit risk related to carbon emission reduction, this study focuses on publicly traded companies. It introduces a risk early warning model grounded in MLP deep learning. Primarily, this research employs the FA-TOPSIS fusion model to comprehensively assess the credit risk associated with carbon emission reduction in enterprises. Subsequently, it employs K-means clustering to compute enterprise similarities, which forms the basis for supervised learning in the MLP model to assign credit risk grade labels. Furthermore, the study tackles the challenge of imbalanced enterprise grade distribution using the ADASYN over-sampling algorithm. Ultimately, the effectiveness of the model proposed herein is confirmed through a series of multi-model comparison experiments. The results show that: First, carbon emission reduction indicators exhibit differing degrees of influence on enterprises at various credit risk levels. Notably, the most influential indicator is carbon emission intensity, while the development capacity indicator exerts the least influence. Second, the adoption of the XGBoost algorithm for screening carbon emission reduction indicators significantly enhances the prediction accuracy of the early warning model by 4.27%. Third, compared to other models, the MLP model achieves an impressive prediction accuracy of 99.48%, representing an average improvement of 15.24%. These results underscore the model's feasibility and its potential to provide technical support for financial institutions and government entities in conducting credit ratings for enterprise carbon emission reduction. [ABSTRACT FROM AUTHOR]

This paper aims to effectively reduce CO2 emissions by examining the impact of three distinct incentive and constraint policies on the quality of rating and certification information in China's green bond issuance market. To accomplish this, the government has implemented incentives, while regulators have introduced constraints to curb the spread of inflated rating and certification information. We build on the integrated rating and certification regulation mechanism by presenting a two-stage Stackelberg game model that involves four key participants: the China Securities Regulatory Commission, local governments, green evaluation and certification agencies, and credit rating agencies. We incorporate environmental effects indicators into the expected utility of rating and certification agencies to investigate the equilibrium conditions under three policy scenarios: a single financial incentive policy, a single regulatory constraint policy, and a combined incentive and constraint policy. The paper employs Stackelberg game theory to analyze how different policies mitigate the occurrence of "inflated" ratings and "greenwashing" in certifications. Numerical analysis is conducted to validate the theoretical findings. Moreover, we assess the impact of these policies on the quality of rating and evaluation information, using data from China's green bond issuance market between 2016 and 2021. Our research offers valuable management insights and regulatory recommendations for both regulators and local governments. [ABSTRACT FROM AUTHOR]

The key to the sustainable development of family farms and ranches is to solve the problem of financing difficulty, and the construction of the credit rating system of family farms and ranches is the basis of solving the financing difficulty. Therefore, the construction of the credit rating system of family farms and ranches is not only a theoretical problem but also has strong practical significance. On the basis of dichotomous method and improved DEA technology, this paper sets up a credit rating model based on dynamic cycle dichotomous DEA method with the goal of maximizing the efficiency value of credit scoring of family farm and ranch and the constraint of increasing default loss rate. The first feature of this paper is that it constructs a direct mapping from the credit index system to the credit score through the three-stage DEA model, which avoids the disadvantages of the classical linear weighting formula that relies heavily on the measurement results of different weights, and obtains more accurate and stable credit score results of family farms and pastures. Second, the three-stage DEA efficiency value is used as the credit score value of the family farm and pasture, and whether it is located in the optimal preset interval is taken as the basis for efficiency dichotomy. The DEA credit score of the family farm and pasture is calculated by dynamic cycle, and the samples belonging to each level of the family farm and pasture are calculated successively according to the order of the credit grade from high to low. Finally, the result of reasonable credit rating of family farm and ranch with high default loss rate and low credit score was obtained. The pyramid test results of matching credit rating and default loss rate show that this research method is effective and has a good pyramid shape. [ABSTRACT FROM AUTHOR]

Commercial banks are of great value to social and economic development. Therefore, how to accurately evaluate their credit risk and establish a credit risk prevention system has important theoretical and practical significance. This paper combines BP neural network with a mutation genetic algorithm, focuses on the credit risk assessment of commercial banks, applies neural network as the main modeling tool of the credit risk assessment of commercial banks, and uses the mutation genetic algorithm to optimize the main parameter combination of neural network, so as to give better play to the efficiency of neural network. After verification of various evaluation models, the accuracy of the evaluation model designed in this paper is more than 65%, while the acceptability of the evaluation results optimized by the mutation genetic algorithm is more than 85%. Compared with the accuracy of about 50% of the traditional credit scoring method, the accuracy of the credit risk evaluation using neural network technology is improved by more than 10%. It is proved that the performance of the optimized algorithm is better than that of the traditional neural network algorithm. It has important theoretical and practical significance for the establishment of the credit risk prevention system of commercial banks. [ABSTRACT FROM AUTHOR]

This paper is oriented to the credit investigation scenario of power grid supply chain enterprises and proposes a blockchain user credit assessment method based on improved Softmax regression in Power IoT. This method first designs a credit-rating mechanism that meets industry characteristics based on business needs. Second, it proposes a user credit evaluation model based on the blockchain architecture. Finally, the improved Softmax regression algorithm is used to train the proposed credit evaluation model, which effectively solves the credit rating. The multiclassification problem has achieved the goal of categorizing the credit rating of the enterprise. The simulation results show that the credit evaluation mechanism proposed in this paper can accurately evaluate the multisource credit data that lacks trust foundation and effectively realize the credit rating of power grid material supply chain enterprises. The credit evaluation mechanism proposed for Power IoT in this paper could have high potential for entity identity authentication and rating for securing mobile video communications. [ABSTRACT FROM AUTHOR]

This paper studies the effects of default risk on expected equity option returns. In the cross-section, expected delta-hedged equity option returns have a negative relation with default risk measured by credit ratings or default probability. In the time series, credit rating downgrades (upgrades) lead to a decrease (increase) in the firm's delta-hedged option return. Our results are consistent with a stylized capital structure model in which the negative relation between option returns and default risk is driven by firm leverage and asset volatility. This paper was accepted by Lukas Schmid, finance. Funding: A. Vasquez thanks the Asociación Mexicana de Cultura A.C. for financial support. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4796. [ABSTRACT FROM AUTHOR]

The debt default risk of local government financing vehicles (LGFVs) has become a potential trigger for systemic financial risks. How to effectively prevent hidden debt risk has always been a hot issue in public-private partnership (PPP) financing management research. In recent years, machine learning has become more and more popular in the study of enterprise credit evaluation. However, most scholars only focus on the output of the model, and do not explain in detail the extent to which variables affect the model and the decision-making process of the model. In this paper, we aim to apply a better credit rating method to the key factors and analysis of LGFV's default risk, and analyze the decision-making process of the model in a visual form. Firstly, this paper analyzes the financial data of LGFVs. Secondly, the XGBoost-logistic combination algorithm is introduced to integrate the typical characteristics of PPP projects and construct the credit evaluation model of LGFVs. Finally, we verify the feasibility of the model by K-fold cross validation and performance evaluation. The results show that: (1) net worth, total assets, operating income, and return on equity are the most critical factors affecting the credit risk of LGFVs, asset-liability ratio and tax revenue are also potentially important factors; (2) the XGBoost-logistic model can identify the key factors affecting the credit risk of LGFVs, and has better classification performance and predictive ability. (3) The influence of each characteristic variable on model decision can be quantified by the SHAP value, and the classification decision visualization of the model improves the interpretability of the model. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analysis the impact of the COVID-19 pandemic on European banks' default risks, as measured by foreign long-term issuer credit ratings published by the main credit rating agencies. Two hypotheses are put forward: (1) The macroeconomic situation has a stronger negative impact on banks' financial conditions during COVID-19; (2) changes in the capital adequacy, assets, management, earnings, and liquidity indicators have a significant impact on changes in banks' credit ratings. The analysis has been prepared for the 2000–2021 period for listed and unlisted banks on the European stock exchanges, that received long-term issuer credit ratings from the main credit rating agencies. To the analysis have been used the ordered logit panel data models and the research has been made on the first differences to analyse the impact of the changes of the financial and macroeconomic conditions on the credit ratings changes. The obtained results suggest a direct and significant impact of the COVID-19 pandemic on the credit rating changes, but a delayed reaction. [ABSTRACT FROM AUTHOR]

Objective: This paper studies the impact of the subjective life expectancy of the elderly on their commercial pension insurance participation at micro perspective, providing new evidence to explain the motivation of commercial pension insurance participation in China. Methods: Using 4 waves of data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018, a multiple linear regression model is constructed to investigate the effect of subjective life expectancy on commercial pension insurance participation among the Chinese elderly, and IV model estimation shows that the results are robust. Meanwhile, the heterogeneity of the effect of elderly life expectancy on commercial pension insurance participation behavior among different characteristic groups is also studied. Results: A one standard deviation improvement in our measure of subjective life expectancy predicts a 0.9% point higher participation rate in commercial pension insurance. We also find that there is significant heterogeneity in the effects of subjective life expectancy on the participation of elderly people in commercial pension insurance with respect to gender, education, hukou, and wealth. Conclusion: This paper provides a new perspective to explain the factors influencing commercial pension insurance participation in China. We suggest that improving residents' awareness of life expectancy is beneficial to their reasonable retirement planning, in the background of stepping into an aging society in China. [ABSTRACT FROM AUTHOR]

This paper studies whether credit ratings can alleviate the hold-up problem in the loan market. We exploit a refinement of the rating information produced by a certifier that rates private bank-dependent firms in France on a vast scale. The refinement causes some firms to receive a positive rating surprise that is not due to an improvement in firm fundamentals. We show that affected firms become less reliant on lenders that have greater ability to extract informational rents. These firms receive greater and less expensive bank credit from new and less informed lenders and invest more. We deduce that credit ratings reduce the monopoly power of informed banks, helping firms to expand their access to bank credit. This paper was accepted by David Sraer, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4779. [ABSTRACT FROM AUTHOR]

In this paper, we propose an extended credit migration model with asymmetric fixed boundaries and multiple ratings, for a more precise depiction of credit changes in the real world. A model with three ratings is established and analyzed as an example, and then the results are generalized to a general multirating form model. We prepare the model meaningfully by arranging the asymmetric boundaries in a suitable order. A PDE system problem is deduced, and the existence and uniqueness of the solution for the problem are obtained using PDE techniques, which further ensure the rationality of the model. Due to the flexible configuration of asymmetric boundaries, the multirating model has various types of structures in the buffer zones where the credit rating keeps its original state. For instance, the two buffers in the three-rating model may be separated, connected, or intersected, as presented in the numerical results for different boundary parameters. [ABSTRACT FROM AUTHOR]