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

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

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

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

The research and development phase is a crucial initial step in any process leading to innovation, and it aligns with the long-term vision of public and private sector strategies. The research questions in this study are as follows: (1) To determine the resulting interrelationship between R&D investments and GDP using regression analysis; (2) To investigate the amount of economic value added (EVA) that Georgia must create with the increase of R&D investments in a certain period in order to move from the group of countries with a BB sovereign credit rating to the group of countries with a BBB-investment credit rating. World Bank data from 2014-2022 was used. Using regression analysis, the impact of R&D investments (by increasing the share of artificial intelligence in R&D to 30-35%) on the country's GDP was determined. The regression analysis between R&D and GDP generated the following results: (1) the regression coefficient is 7.02502%, indicating that a 10% increase in R&D will result in a 0.70% increase in GDP; (2) the coefficient of determination is 81.1%, which demonstrates that 81.1% of the change in GDP is explained by the change in R&D; (3) the correlation coefficient is 90.1%, indicating a strong positive relationship; (4) the P-value is 0.03492, which suggests that the relationship between these two variables is significant. The calculation of a country's EVA (as a powerful tool that evaluates a country's economic growth and development) incorporates three key factors: the country's total wealth, net operating profit after tax and the Central Bank rate. The EVA model of Georgia was calculated and then analysed in order to determine the additional value that Georgia would need to generate in order to be included in the BBB-investment credit rating group. In order to determine this, the economic indicators of the countries on the BBB scale (Greece, Hungary, India, Kazakhstan) were analysed, and their average weighted index was calculated. This index is characteristically relevant according to the criteria set out by S&P, Fitch, and Moody's. The following economic indicators were considered: nominal GDP, GDP per capita, and real GDP growth. External indicators included: current account balance/GDP, gross external financing needs/CARs plus usable reserves. Fiscal indicators were: general government balance/GDP, debt/GDP, and net debt/GDP. Finally, the consumer prices index growth was considered as a monetary indicator. According to EVA model calculations, in order to achieve Georgia's BBB credit rating in the next 9 years, investments of $61.7 billion are required. Using EVA and other economic indicators in the decision-making process will contribute to a more in-depth analysis of the current economic processes in the country and increase efficiency.

AbstarctTodays one of the most important factor for dicision making process by market participants at Tehran Stock Exchange listed companice is the information published by the companies listed in If the information is not properly disclosed, without a doubt, the price discovery mechanism in the market will not work properly and the pricing of the bonds will not be done in a favorable way. Therefore, according to the importance of the level of disclosure, it is important to identify the factors affecting the quality of disclosure, therefore, in this research, the impact of credit rating changes on the quality of disclosure has been investigated. In order to carry out the research, a sample consisting of 126 companies listed in Tehran Stock Exchange during the period of 2012 to 2021 were examined. In order to perform the analysis, the data related to the credit rating, disclosure quality rating and control variables were extracted from Rahvard Navin software and the accompanying notes of the financial statements. Based on the results of the research, there is a negative relationship between credit rating changes and disclosure quality. In other words, credit rating changes are effective on the voluntary disclosure level of companies, but this effect was not statistically significant. While there was a negative and significant relationship between credit rating and disclosure quality. In other words, the higher the credit rating, the lower the company's disclosure quality. The higher the credit rating, because investors evaluate the risk as low, their demand for more information disclosure also decreases. In other words, increasing the credit rating through reducing the demand for information can lead to a reduction in disclosure.Keywords: credit rating, credit risk, disclosure qualityJEL: M41

Over time, credit ratings have gained a major role in the financial market, influencing various financial aspects. For businesses, a good credit rating can indicate financial stability to investors, suppliers, and customers. This can increase the business's reputation and bring new opportunities for growth. However, in the current global context, environment, social aspects, as well as governance policies become essential for global decision-making, but also for financial markets. This article aims to present the importance of ESG ratingsfor companies.

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

Reaching for yield, which we define as an investor preference for higher-yield bonds at a given rating or for higher-rated bonds at given yields, is associated with inflated valuation and thus negatively predicts risk-adjusted returns. Controlling for ratings, alphas are lower for higher-yield bonds, whereas, controlling for yields, alphas are lower for safer-looking, higher-rated bonds. Future bond returns are particularly low when current interest rates are low and demand from yield-reaching investors increases. These bonds experience more frequent downgrades and defaults, suggesting that they are riskier despite their high valuation. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2023.4920. [ABSTRACT FROM AUTHOR]

The research and development phase is a crucial initial step in any process leading to innovation, and it aligns with the long-term vision of public and private sector strategies. The research questions in this study are as follows: (1) To determine the resulting interrelationship between R&D investments and GDP using regression analysis; (2) To investigate the amount of economic value added (EVA) that Georgia must create with the increase of R&D investments in a certain period in order to move from the group of countries with a BB sovereign credit rating to the group of countries with a BBB-investment credit rating. World Bank data from 2014-2022 was used. Using regression analysis, the impact of R&D investments (by increasing the share of artificial intelligence in R&D to 30-35%) on the country's GDP was determined. The regression analysis between R&D and GDP generated the following results: (1) the regression coefficient is 7.02502%, indicating that a 10% increase in R&D will result in a 0.70% increase in GDP; (2) the coefficient of determination is 81.1%, which demonstrates that 81.1% of the change in GDP is explained by the change in R&D ; (3) the correlation coefficient is 90.1%, indicating a strong positive relationship; (4) the P-value is 0.03492, which suggests that the relationship between these two variables is significant. The calculation of a country's EVA (as a powerful tool that evaluates a country's economic growth and development) incorporates three key factors: the country's total wealth, net operating profit after tax and the Central Bank rate. The EVA model of Georgia was calculated and then analysed in order to determine the additional value that Georgia would need to generate in order to be included in the BBB-investment credit rating group. In order to determine this, the economic indicators of the countries on the BBB scale (Greece, Hungary, India, Kazakhstan) were analysed, and their average weighted index was calculated. This index is characteristically relevant according to the criteria set out by S&P, Fitch, and Moody's. The following economic indicators were considered: nominal GDP, GDP per capita, and real GDP growth. External indicators included: current account balance/GDP, gross external financing needs/CARs plus usable reserves. Fiscal indicators were: general government balance/GDP, debt/GDP, and net debt/GDP. Finally, the consumer prices index growth was considered as a monetary indicator. According to EVA model calculations, in order to achieve Georgia's BBB credit rating in the next 9 years, investments of $61.7 billion are required. Using EVA and other economic indicators in the decision-making process will contribute to a more in-depth analysis of the current economic processes in the country and increase efficiency. [ABSTRACT FROM AUTHOR]

Objective: Credit rating agencies play an important role in monitoring and disseminating information, and their ability to reduce information asymmetry means they have a unique role in creating value for debt and equity holders. The credit rating is an index that indicates the credit risk (default risk) of a company (debtor) or a specific debt (corporate bonds) and is a measure for determining the costs (interest rate) incurred when using other people's capital in the market; As an important indicator when issuing corporate bonds or borrowing funds, it affects the cash flow and value of the company. A credit rating is important to a company because of its impact on the valuation of stocks and bonds and the legal and contractual costs (benefits) associated with a change in credit rating. Therefore, managers are incentivized to maintain or achieve a favorable credit rating by influencing the rating agencies' perception of the firm's creditworthiness. The existing literature shows that the costs (benefits) associated with credit rating changes affect managerial capital structure decisions and firm financing choices, and firms tend to adjust leverage to influence rating agencies' decisions. However, leverage is not the only important consideration for rating agencies when determining a company's credit rating. On the other hand, the rating process requires analyzing publicly disclosed company information about a company's value. This study investigates how to use management and performance (managers' discretionary disclosure, managers' ability and accounting conservatism) to achieve a favorable credit rating. Method: The data of 90 companies admitted to the Iran Stock Exchange during the years 2013 to 2023 and a total of 990 annual financial statement reports, explanatory notes and reports of the board of directors' activities to the annual meeting of shareholders were used. Multiple regression methods have used hypotheses based on combined data. This checklist consists of 60 components for optional disclosure that each item in the checklist if it is disclosed in the annual financial statements, explanatory notes and the report of the general meeting; The number is considered to be one and otherwise zero, and finally, the optional disclosure index is calculated by dividing the sum of disclosed items by the total items that should be disclosed. Findings The results of the first hypothesis test showed that companies get higher ranks with the increase in managers' discretionary disclosure. It seems that companies tend to commit to the disclosure of credible information to influence the perception of rating agencies. A credible commitment to voluntary disclosure reduces the information asymmetry between inside and outside the firm, resulting in rating agencies' expectations of higher creditworthiness of the firm. Managers appear to use discretionary disclosure to obtain favorable ratings rationally. Conclusion: The increase in managers' voluntary disclosure may be due to opportunistic motives to obtain a favorable credit rating. An increase in conservatism will lead to a decrease in the manager's opportunistic behavior and a decrease in default risk. Therefore, companies with higher levels of conservatism and higher credit ratings are rewarded with lower debt costs. Rating agencies are likely to consider managers' ability as a signal of lower default risk, given the tendency of managers with higher ability to deliver more limited performance results in the future. By increasing managers' discretionary disclosure, companies get higher ratings. It seems that companies tend to commit to the disclosure of credible information to influence the perception of rating agencies. A credible commitment to voluntary disclosure reduces the information asymmetry between inside and outside the firm, resulting in rating agencies' expectations of higher creditworthiness of the firm. Managers appear to use discretionary disclosure to obtain favorable ratings rationally. Since a significant positive relationship exists between credit rating and discretionary disclosure, companies may have incentives for opportunistic disclosure to obtain a favorable credit rating. The results of this research are consistent with the results of Kim and An (2023) and He (2018). Since managers with high ability improve firm performance and information transparency, the idea that managers' ability positively affect credit rating is intuitive. However, it is unclear whether credit rating agencies can accurately measure managers' ability and consider it an independent risk factor because the assessment of managerial ability is subjective and difficult to separate from company performance. The results of the second hypothesis test show a positive and significant relationship between managers' ability and credit rating; companies with more capable managers are more likely to get a higher credit rating. This shows that credit rating agencies recognize managers' ability as an independent credit factor. Hence, rating agencies are likely to consider managers' ability as a signal of lower default risk, given the tendency of managers with higher ability to deliver more limited performance results in the future. Conservative reporting reduces information asymmetry between debt holders and managers, bondholder and shareholder conflicts, debt costs, and default probability. Therefore, it is considered an important accounting method that validates financial reporting. Therefore, managers can use conservative accounting to influence the credit rating analyst's perception of default risk. The results of the third hypothesis show that conservatism positively and significantly affects credit rating. This result shows that companies with higher conservatism get a higher credit rating. It seems that a company that practices more accounting conservatism reduces default risk by increasing cash holdings, and a company that increases accounting conservatism reduces default risk and agency conflict by increasing efficient investments. Hence, credit markets react to corporate reporting strategies and issue higher credit ratings to companies with lower default risk. [ABSTRACT FROM AUTHOR]

Recent evidence has shown that hybrid models for credit ratings are important when assessing the risk of firms. Within this stream of literature, we aim to provide novel evidence on how hard (quantitative), soft (qualitative), and market information predict corporate defaults for unlisted firms by implementing the Cox proportional hazard model. We address this research question by exploiting a unique proprietary dataset comprising of detailed information on internal credit ratings of European unlisted mid‐sized firms and compute their Merton's distance‐to‐default (DD) measure of credit risk with market data collected on comparable publicly listed companies. Our results show that the bank's use of hard, soft, and market information when assessing the credit ratings of borrowers has a significant influence on the prediction of their defaults. Further, we investigate the significant influence of soft information in predicting corporate defaults by drawing on two separate processes through which loan officers can inject soft information in credit scoring, that is, 'codified' and 'uncodified' discretion. Finally, when we distinguish between the loan officer's discretion to upgrade or downgrade an applicant's credit score, we find that it is the upgrade that is likely to predict a lower probability of a firm defaulting. This study contributes to the policy debate on safeguarding the banking sector's continuity by positing that integrating market information into banks' hybrid methods of credit rating helps to improve the accuracy in predicting unlisted firms' credit risk that is useful to policy makers for the design of future forward‐looking financial risk management frameworks. [ABSTRACT FROM AUTHOR]

The ordered logit (OLogit) model is a regression model for an ordinal dependent variable. For a conventional time series OLogit model, both the dependent variable and the independent variables are required to be observed at the same frequency. However, this requirement is violated under the circumstance of mixed frequency data. To this end, we introduce the unrestricted MIDAS (U‐MIDAS) method into the OLogit model and develop a novel U‐MIDAS‐OLogit model, in which high‐frequency covariates are used to predict a low‐frequency outcome with ordinal categories. The U‐MIDAS‐OLogit model enlarges the application of OLogit and enables to produce timely forecast. To verify its effectiveness, we conduct extensive Monte Carlo simulations. The numerical results show that the U‐MIDAS‐OLogit model is superior to several typical OLogit models in terms of prediction performance. We then apply the U‐MIDAS‐OLogit model to predict credit ratings of listed companies in China and the US, respectively. The empirical results also confirm its promising in practical applications. [ABSTRACT FROM AUTHOR]

This research explores the impact of financial indicators on the credit ratings of companies listed on the S&P 500, employing a Sys-GMM model to address endogeneity concerns. Three independent variables categorized as market and survival factors alongside seven control variables sourced from leverage, liquidity, interest coverage, profitability, market, survival, and macroeconomic domains were investigated. The sample consisted of 2398 observations from Capital IQ Pro, spanning nine years (2013 to 2021) and encompassing 240 public companies. The findings suggest that neither Tobin's Q (TQ) nor Total Shareholder Return (TSR) lack significant correlations with credit ratings, implying that stock market performance and total shareholder return do not directly impact credit ratings. In contrast, the Altman Z-score (AZS) emerged as a significant predictor, indicating its importance in assessing credit risk. These insights enhance the understanding of financial indicators' impacts on credit ratings, aiding financial institutions and companies in prudent lending and financing decisions. [ABSTRACT FROM AUTHOR]

This study examines the relationship between inventory performance and credit ratings and explores how this relationship is affected by supply chain concentration in the context of small- and medium-sized enterprises (SMEs). Financial indicators and bond rating data for SMEs in China's manufacturing sector are obtained from two large public data sources, and hierarchical ordered logistic regression analysis is used to investigate our research questions. The findings show that inventory performance is positively associated with SME credit ratings and that this relationship is positively moderated by customer concentration but negatively moderated by supplier concentration. This study provides valuable theoretical contributions to the interface of supply chain and finance research and offers important managerial implications that SMEs can follow to increase their credit ratings. [ABSTRACT FROM AUTHOR]

Abstract The objective of this study was to analyze how the COVID-19 crisis has affected the determinants and predictability of the domestic credit rating issued by Fitch Ratings in Argentina. Additionally, it aims to evaluate the effects of credit rating agencies using the through-the-cycle method. Given the subjective nature of credit rating categorization, researchers have developed models for explaining and predicting credit ratings. This subjectivity is significant during economic events. Therefore, it is important to investigate whether the factors that determine and predict credit ratings remained consistent before and during the COVID-19 crisis. This paper contributes significantly to understanding how the application of the through-the-cycle method affects the determinants and predictability of credit ratings in economic crises. Theapplication of the through-the-cycle method by credit rating agencies as a criterion during the COVID-19 crisis resulted in a breakdown of the usual correlation between determinants and credit rating. Understanding whether variables are permanent or transitory components is crucial for investors and borrowers to anticipate credit rating changes during economic downturns. The dependent variables are the long-term domestic credit rating categories. The independent variables are derived from the Fitch Ratings credit rating methodology and the literature, which includes quantitative and qualitative variables. The statistical methods used are ordinal logistic regression, generalized ordinal logistic regression, and support vector machines. The COVID-19 crisis was considered a transitory event due to the application of the through-the-cycle approach by rating agencies. During the pandemic, specific determinants of credit ratings are not considered due to their transitory nature. The study identifies interest coverage ratio and competitive position as transitory components. This approach led to less predictability but a more stable credit rating.

The sustainable development of the agro-industrial complex is a priority task and a factor in ensuring food security in Russia. The relevance of the study is due to the lack of transparency and limited existing ratings of agribusiness companies, the lack of consideration of the impact of sanctions, their consequences and the ability of companies to promptly reconfigure their business models. In this regard, the purpose of the study was to form a methodology for assessing the sustainability of the development of agribusiness companies in modern conditions. The presented methodology is based on the principles of prioritizing the impact of sustainable development criteria on competitive opportunities; availability of accessible information, its regularity and understandability for users; using the risk factor approach as a navigator for assessing competitive positions. The research methodology is based on an industry approach, followed by an assessment of the impact of identified risk factors on trends in production indicators, market share dynamics, efficiency of operating, financial and investment activities, and business development rates. To visualize the results, the method of constructing competitiveness polygons was used, which provides a clear assessment of the competitive advantages and management abilities of companies to quickly adapt to changing market conditions. The scientific novelty of the study lies in the development of a situational approach to assessing the sustainability of the development of agribusiness companies, based on the impact of identified industry risk factors on business performance. Approbation of the methodology was carried out on the companies, which are participants in the credit ratings for the agro-industrial complex sector of national rating agencies accredited by the Bank of Russia. The theoretical significance of the study lies in the development and adaptation of the methodology of sectoral analysis to the specifics and needs of the agro-industrial complex for the purpose of its sustainable development. The practical results are of value to the Ministry of Agriculture and private investors interested in an independent assessment of companies in order to minimize the risks of investing in sustainable development projects.

Shariah governance is the mechanism to monitor and implicate shariah compliance in Islamic banks. The study's goal of exploring the Islamic bank's governance attributes and credit scores or rating relationship in the presence of shariah board attributes as moderators. The study collected time-variant data from 22 Asian banks (286 observations) from 2006 to 2018. Applied descriptive statistics, correlations, Likelihood Ratio (LR) test and the Ordered Logistic Regression model, a suitable technique for the ordinal dependent variable. The study findings provide evidence of shariah governance's moderating role in the relationship between corporate governance attributes and credit rating. Moreover, shariah board characteristics strengthen the association between the corporate board and credit worthiness nexus. This research recommends that credit score evaluating agencies consider the shariah governance characteristics in evaluating Ib's credit rating. The shariah governance attributes as part of credit rating can be an appropriate method for investors to measure the shariah compliance level of Ibs. Accordingly, Ibs can gain the confidence of investors or sukuk investors by improving shariah compliance and can access competitive fund sources. The study's uniqueness is in determining the impact of shariah governance attributes as moderators on the board-rating nexus. This study suggested that credit rating agencies revise or amend their assessment procedures for Ibs. Abundant literature is available from the owner's point of view. Nonetheless, this research explores governance and shariah governance attributes concerning Sukuk holders. [ABSTRACT FROM AUTHOR]

In this paper, we study whether credit rating agencies (CRAs), as they claim, follow the rating through‐the‐cycle approach as opposed to a pro‐cyclical approach. In particular, we compare the behavior of CRAs during the credit crunch and normal market conditions. Using the credit rating data by S&P, we find that CRAs assign lower credit ratings to firms during credit crunch relative to normal times. Nevertheless, this result does not necessarily imply that CRAs show an excessively pro‐cyclical behavior if credit crunches have a long‐term fundamental impact on firms. Our further investigation reveals that downgrades during a credit crunch will not be reversed over the subsequent 1–5 years, which supports through‐the‐cycle credit rating. [ABSTRACT FROM AUTHOR]

Levels of government debt-to-GDP have been rising substantially for over a decade in emerging market economies such as South Africa, which has led to much debate around the implementation of large-scale debt-financed fiscal stimulus programs in response to the economic fallout of the global covid-19 pandemic. Debt-financed fiscal policies directly stimulate aggregate demand through government expenditure or tax cuts, but their effectiveness is highly dependent on direct crowding-out of private sector expenditure, spill-over effects to the private sector through a higher risk premium on interest rates, and the interaction between fiscal policy and monetary policy. Using an open-economy dynamic stochastic general equilibrium model for South Africa, we identify the effect of six different fiscal policy instruments on short-term and long-term interest rates. These disaggregated expenditure and revenue shocks raise long-term real yields between 18 and 29 basis points, but there are non-negligible differences in the dynamic responses to each fiscal instrument. Our main findings suggest that, in the context of fiscal sustainability, an investment-driven debt-financed fiscal stimulus programme would reduce the government debt-to-GDP ratio, especially in periods of economic slack when monetary policy would typically be more accommodative. In fact, since the global financial crisis, monetary policy has reduced the burden of fiscal adjustment in response to rising debt and a rising risk premium. But further shocks to the risk premium could offset any gains from the current stance of monetary policy (for example, a credit rating shock raises the long-term government bond rate 155 basis points). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Management & Business Research (2521-4306) is the property of Chinese Management Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Narcissistic managers with characteristics such as desire for fame and power seeking to protect their interests have the necessary motivation to be effective on the relationship between discretionary accruals and credit rating and, as a result, change the credit rating of companies to their desired rating. The purpose of the present study is to investigate the relationship between discretionary accruals and credit rating by considering the moderating effect of managers' narcissism. The statistical sample of the current research includes 113 companies listed on the Tehran Stock Exchange and 17 companies listed on the Iranian Foreign Exchange in the period of 2011-2021. The data of this research was collected by referring to the Kodal website, the website of the Iranian Statistics Center and Rahvard Novin software. Hypotheses were tested through multivariate regression models and generalized least squares approach using EViews 12 software. The results of the statistical test of research hypotheses confirm that discretionary accruals have a positive and significant effect on the credit rating of economic units. That is, with the increase of discretionary accrual items, the credit rating of economic units increases. Also, the findings of the research indicate that there is a moderating effect of managers' narcissism with three indicators of the size of managers' signatures, managers' cash bonus and managers' photo in the relationship between discretionary accruals and the credit rating of economic units. In other words, managers' narcissism based on the indicators of the size of managers' signatures, managers' cash bonus and managers' photos strengthens the positive relationship between discretionary accruals and credit rating. Based on these results, credit rating agencies are suggested to pay more attention to the narcissistic factors of managers and discretionary accruals in the credit rating process of economic units. [ABSTRACT FROM AUTHOR]

In China, with the ''Double Carbon'' goal within reach, Micro, Small and Medium-sized Enterprises (MSMEs) emerge as pivotal contributors to economic advancement. However, they are now confronted with the imperative of transitioning towards green and low-carbon practices. To facilitate the attainment of peak carbon dioxide emissions and carbon neutrality, a refined approach is imperative. This entails precise capital allocation, enhanced financial services, streamlined management, and robust risk mitigation strategies. Consequently, conducting thorough credit risk assessments for MSMEs becomes a crucial endeavor. However, obtaining substantial loans for them proves challenging due to their elusive credit ratings and potential defaults. To address this issue, this study leverages machine learning and intelligent optimization algorithms to construct a classification model for default and credit ratings of MSMEs, utilizing their daily invoice data. Specifically, twelve indicators pertaining to default and credit ratings are extracted. Subsequently, Principal Component Analysis is employed to reduce dimensionality and synthesize all pertinent information. Following this, the Genetic Algorithm-based Back Propagation Neural Network (GA-BPNN) is utilized to delineate the relationship between indicators and default, as well as credit rating, respectively. The results indicate a prediction accuracy of 0.92 for default risk and 0.86 for credit rating. This underscores the efficacy of GA-BPNN in effectively classifying the underlying default risk and credit ratings of MSMEs, offering a promising approach for decision-making.

Purpose: The purpose of this article was to develop a framework for the institutionalization of credit rating agencies in Iran, considering the institutional components and the socio-economic conditions of the country. Methodology: The research method was qualitative-analytical based on interpretative paradigm and Grounded Theory strategy. A combination of desk research and interviews was used to collect data; besides, judgmental and snowball methods were applied to select the statistical sample. Using open, axial, and selective coding, data were summarized in the categories of causal, contextual, intervening, interactive, strategic, and outcomes, all of which formed the main model of the research. Findings: Based on the research data, the most important components of institutionalization depend, firstly on institution-oriented culture which includes independence and objectivity, professionalism, and close supervision, and secondly, on gaining legitimacy which includes trust-building, expertise, stability, and continuity. Conflict of interests is one of the most noticeable challenges for institutionalization, the solution of which should be sought in close supervision and self-monitoring approach of rating agencies. Originality: By integrating institutional and organizational theories, a model has been presented for the institutionalization of credit rating agencies, which can affect the efficiency and effectiveness of these agencies as well as capital markets Implications: It is suggested that the supervisory bodies not only set high-level standards in the field of professional conduct and the instances of conflict of interests, but also encourage the culture of self-monitoring in credit rating agencies.

We study the effects of the Dodd–Frank Act ("Dodd–Frank") on determinants of credit ratings. We predict that the increase in regulatory oversight and litigation risk prompted by Dodd–Frank, as well as requirements for improved disclosures and governance, motivated credit rating agencies (CRAs) to increase the reliance on verifiable quantitative fundamental information in determining ratings. We find that the power of firm fundamentals in explaining credit ratings increases significantly after Dodd–Frank. Furthermore, we find that the greater reliance on quantitative firm fundamentals at least partially drives the improvement in credit ratings' ability to predict future defaults. Collectively, our evidence suggests that Dodd–Frank incentivizes CRAs to use more quantitative information in making rating decisions, which improves credit rating quality. [ABSTRACT FROM AUTHOR]

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

Copyright of Gharee for Economics & Administration Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Over time, credit ratings have gained a major role in the financial market, influencing various financial aspects. For businesses, a good credit rating can indicate financial stability to investors, suppliers, and customers. This can increase the business's reputation and bring new opportunities for growth. However, in the current global context, environment, social aspects, as well as governance policies become essential for global decision-making, but also for financial markets. This article aims to present the importance of ESG ratings for companies. [ABSTRACT FROM AUTHOR]

This paper studies the issuing pricing character of green bonds with the evidence from China and further analyzes the reason why green factor affects bond issuing pricing by analyzing credit rating's intermediary effect and exploring the mediating of policy incentives and third-party green certification. Furthermore, heterogeneity analysis is conducted to analyze how green factor impacts on bond issuing pricing at different quantiles and for companies in different industries or with different ownership. The findings include: (1) Green factor generally reduces the issuance interest rate of green bonds, though this effect is not significant at the low quantiles or at the early development stage. (2) Green factor's impact on bond issuing pricing occurs partly by improving bond credit rating, fully by this way for companies in the construction industry, but directly for companies in other industries. (3) For non-state-owned companies, green factor has direct effect on bond issuing pricing, but this effect is not significant for state-owned companies. (4) Third-party green certification and incentive policies enhance the reducing effect of green factor on the issuance interest rate. This study provides a theoretical reference for environment-friendly investors, financiers with green projects and the regulators caring about green finance. [ABSTRACT FROM AUTHOR]

Some qualitative studies have mentioned the impact of trading networks on the credit levels of countries or businesses. However, only a few studies quantified network information on simulated or actual data. This research studied the relationship between network characteristics and sovereign credit levels. Using a data set for 114 countries, we developed and compared the performance of five credit classification models with and without integrating network information. We used several measures to quantify the trading network information, such as Trading Weight, Closeness, Betweenness, PageRank, and Modularity. The analysis showed that they were closely related to credit levels. In particular, the Trading Weight usually belonged to the top five critical features for almost all classifiers. Overall, we saw that the correct prediction of the network models was consistently higher than those without a network. It meant we could improve the sovereign credit classifications with available trading network information. [ABSTRACT FROM AUTHOR]

This paper selects the data of corporate bond from 2005 to 2019, then divides them into long term bond and short term bond according to maturity of bond. Climate risk is found have significant impact on long term bond yield. Climate risk has no significant impact on short term bond yield. Callability has significant effect on both long term and short term bond yield, while callability has large effect on short term bond and relatively small effect on long term bond. The resale has significant effect on both long term and short term bond yield. The significant effect of the resale on short term bond yield explains large part of the short term bond yield. Issuance has significant impact on both long term and short term bond, with larger impact on short term bond yield. Maturity has significant effect on the yield of both long term and short term bond, and the effect is larger for short term bond. Bearish sentiment is negatively and significantly related to long term bond yield, but has no significant effect on short term bond yield. Credit ratings have large impact on long term bond yield and explain large part of this. However, credit ratings have no significant effect on short term bond yield. [ABSTRACT FROM AUTHOR]

Supply chain finance (SCF) is a financial service that provides convenient loan transactions for small- and medium-sized enterprises (SMEs) upstream and downstream of the supply chain. SCF can help to smooth the capital flow of many SMEs. However, it is difficult for agricultural SMEs to participate in SCF because this kind of SME usually has various risks. Objectively, evaluating the credit rating of agricultural SMEs is difficult for commercial banks. Furthermore, unlike general manufacturing enterprises, agricultural enterprises produce directly based on nature. Sustainable development based on the natural environment is very important for agricultural enterprises. In this paper, considering sustainability, we construct a criteria system to evaluate the credit ratings of agricultural SMEs for SCF. Moreover, we study a method for processing the hybrid heterogeneous evaluation information of SMEs. A co-decision method is proposed to classify the credit ratings of agricultural SMEs with the help of three-way decisions. Agricultural SMEs are evaluated by both criteria evaluation information and misclassification loss. Finally, a credit rating evaluation example is presented to demonstrate the application of our method. The results show that our proposed method can be used to fully evaluate agricultural SMEs with fine classification effects. It can also provide a reference for commercial banks to determine the credit of agricultural SMEs with a low decision risk. [ABSTRACT FROM AUTHOR]

We analyze green and conventional bonds during regular market periods and within times of extreme volatility, the COVID-19 pandemic. We find a negative premium (greenium) of 1.6 bp before the outbreak of COVID-19, but during the times of extreme market stress, this greenium widens to 3.5 bp as our results show a significant outperformance of green bonds. The results indicate that green bonds are more resilient during risk-off periods than non-green bonds. In addition, the greenium effect is moderated by the issuer's country environmental performance as the greenium is more pronounced for issuers from non-green countries prior to COVID-19. We do not find differences between green and non-green countries since COVID-19. [ABSTRACT FROM AUTHOR]

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

Credit rating is a matter of significance for institutions, professionals, and researchers in diverse sectors. Recently, it has garnered increased attention within the scientific community, particularly regarding various banking aspects. Our research employs bibliometric data to explore trends in credit rating. We examined 107 articles from Scopus-indexed journals in Business and Economics up to 2022, focusing on "credit rating" and "Europe" in titles, abstracts, and keywords. Our findings unveil influential journals, authors, countries, and emerging terms. This provides insights into the current state and essential terminology in credit rating research. Furthermore, we suggest potential complementary studies to enrich this research and propose future research directions. [ABSTRACT FROM AUTHOR]

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

We use firm-level data that measure forward-looking physical climate risk to examine the impact of climate risk on capital structure. We find that greater physical climate risk leads to lower leverage in the post-2015 period (i.e., after the Paris Agreement and the first step of standardization of disclosure of climate risk information). Our results hold after controlling for firm characteristics known to determine leverage, including credit ratings. Our evidence shows that the reduction in leverage related to climate risk is shared between a demand effect (the firm's optimal leverage decreases) and a supply effect (bankers and bondholders increase spreads when lending to firms with the greatest risk). Our results are consistent with the hypothesis that physical climate risk affects leverage via larger expected distress costs and higher operating costs. This paper was accepted by Colin Mayer, Special Section of Management Science on Business and Climate Change. Funding: This project benefited from the financial support of the Institut Europlace de Finance (Grant EIF 2019). Q. Moreau acknowledges financial support from the French Association of Institutional Investors. The authors have received in-kind support from Carbone 4 for this project in the form of a data set of climate risk ratings. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4952. [ABSTRACT FROM AUTHOR]

For years, the cost of capital in Poland has been among the highest in the EU. While in 2005–2015, interest rates on long-term loans for enterprises in Czechia and Estonia were at levels similar to Germany, in Poland, rates were always higher – in 2009–2013, even twice as high. Throughout the 18-year analysis period, enterprises in Poland paid 35% more interest than those in Czechia, and 41% more than in Estonia. Over the entire period, Polish enterprises paid PLN 57 bn more interest than if rates had been the same as in Czechia, PLN 74 bn more than in Estonia, and PLN 118 bn more than in Germany. This has resulted in higher debt among Polish enterprises, along with lower levels of innovation, creditworthiness, competitiveness, salaries, tax base, and investment profitability, and thus slower development and weaker future development prospects for the Polish economy.

Abstract There has been heated debate regarding credit‐rating agencies' (CRAs') reporting accuracy of corporate credit ratings, which is essential for investors because they rely on those crediting ratings to make investment decisions. We estimate the reporting accuracy using the data on corporate ratings from Standard & Poor from January 1986 to December 2011. First, there is a U‐shape in the overall misreporting pattern: the left‐hand side (the high‐rating groups) has a lower misreporting probability (3%), the middle has no misreporting, and the right‐hand side has a high misreporting probability (6%). Second, we find that there is a significant difference across the industries. The financial sector has the highest misreporting probability (35% in the lowest rating group) and misreporting magnitude (rating rank jump between true rating and reported rating), and the energy industry has the lowest misreporting probability. Last, when the economic condition is good, CRAs are likelier to inflate the rating.