The Impact of Credit Ratings on Financial Performance (ROA) and Value Creation (Tobin’s Q)

This study employs a bibliometric and systematic approach to examine the impact of credit ratings as a measure of financial performance for companies listed in the S&P 500 index.The study identified a knowledge gap as only two researches were found,one suggesting and another using credit ratings to measure financial performance.Most researches use leverage,profitability,liquidity,and Share Return measures to explain financial performance.The empirical analysis uses the data of 2,398 observations of 240 companies rated by S&P Global Ratings for the period 2009-2013,applying a Generalized Method of Moments(GMM)methodology to estimate the models due to its ability to address potential endogeneity issues.The study considers Return on Assets(ROA)and Tobin’s Q as dependent variables.It incorporates credit ratings(CRWLTA)along with variables such as Total Debt to Total Assets(TDTA),Total Shareholder Return(TSR),EBITDA Interest coverage(EBITDAICOV),Quick Ratio(QR),Altman’s Z-Score(AZS),as well as macroeconomic factors like Gross Domestic Product(GDP)growth,inflation(Consumer Price Index-CPI),and the Federal Reserve Interest Rate(FDRI)as independent variables.The study argues that credit ratings,which incorporate historical data and confidential information about companies’strategies,provide reliable forward-looking creditworthiness assessments to the market.It is supported by specialized rating agencies that employ their methodologies.However,the findings suggested that CRWLTA,had a negative relationship with Q Tobin,although it was not statistically significant,and a negative relationship with ROA that was on the verge of significance.

Corporate Credit Ratings Based on Hierarchical Heterogeneous Graph Neural Networks

order to help investors understand the credit status of target corporations and reduce investment risks,the corporate credit rating model has become an important evaluation tool in the financial market.These models are based on statistical learning,machine learning and deep learning especially graph neural networks(GNNs).However,we found that only few models take the hierarchy,heterogeneity or unlabeled data into account in the actual corporate credit rating process.Therefore,we propose a novel framework named hierarchical heterogeneous graph neural networks(HHGNN),which can fully model the hierarchy of corporate features and the heterogeneity of relationships between corporations.In addition,we design an adversarial learning block to make full use of the rich unlabeled samples in the financial data.Extensive experiments conducted on the public-listed corporate rating dataset prove that HHGNN achieves SOTA compared to the baseline methods.

A Study on The Timeliness of Credit Rating on Bond Defaults--Evidence from Chinese Bond Market

With the increase of China’s bond issuance and slowdown of the economic growth,the potential credit risks such as bond default in the bond market are gradually emerging.The frequent occurrence of bond defaults and the problem of false credit ratings make bond investors and market participants more cautious about the credit ratings issued by rating agencies.Based on the default bonds from 2016 to 2019,this paper analyzes the adjustment of rating of defaulted bonds by rating agencies before default.It also compares the impact of both the regulatory events and the entrance of international agencies on timeless of credit ratings on default bonds.At the same time,the divergence of rating timeliness between different rating agencies is compared.The research shows that after the unified supervision of regulators and the punishment of Dagong Global Credit Rating Co.Ltd in 2018,the timeliness of rating agencies'downgrading of defaulted bonds has increased significantly;Compared with other rating agencies,the timeliness of rating agencies owned by international rating agencies are better.

The impact of working capital management on credit rating

This study investigates the possible nonlinear relationship between working capital and credit rating.Furthermore,it examines the relationship between the three components of working capital(inventory,accounts receivable,and accounts payable)and a firm’s credit rating.Employing data for U.S listed firms for the period between 1985 and 2017,the results of our ordered probit model show a nonlinear relationship between working capital and its components and credit rating.Finally,we find that the deviation from the optimal working capital adversely affects the credit rating.The results of this study are of significant importance for policy makers,managers,decision makers,and credit-rating agencies,as they help highlight the importance of working capital management for a firm’s credit rating.

Detecting conflicts of interest in credit rating changes:a distribution dynamics approach

In this study,we compare the adjustments of credit ratings by an investor-paid credit rating agency(CRA),represented by Egan-Jones Ratings Company,and an issuer-paid CRA,represented by Moody’s Investors Service,vis-à-vis conflict of interest and reputation.A novel distribution dynamics approach is employed to compute the probability distribution and,hence,the downgrade and upgrade probabilities of a credit rating assigned by these two CRAs of different compensation systems based on the dataset of 750 U.S.issuers between 2011 and 2018,that is,after the passage of the Dodd–Frank Act.It is found that investor-paid ratings are more likely to be downgraded than issuerpaid ratings only in the lower rating grades,which is consistent with the argument that investor-paid agencies have harsher attitudes toward potentially defaulting issuers to protect their reputation.We do not find evidence that issuer-paid CRAs provide overly favorable treatments to issuers with threshold ratings,implying that reputation concerns and the Dodd–Frank regulation mitigate the conflict of interests,while issuerpaid CRAs are more concerned about providing accurate ratings.

The valuation of multi-counterparties CDS with credit rating migration

In this paper,the pricing of a Credit Default Swap(CDS)contract with multiple counterparties is considered.The pricing model takes into account the credit rating migration risk of the reference.It is a new model established under the reduced form framework,where the intensity rates are assumed to have structural styles.We derive from it a non-linear partial differential equation system where both positive and negative correlations of counterparties and the references are considered via a single factor model.Then,an ADI(Alternating Direction Implicit)difference method is used to solve the partial differential equations by iteration.From the numerical results,the comparison of multi-counterparty CDS contract and the standard one are analyzed respectively.Moreover,the impact of default parameters on value of the contracts are discussed.

Research on the Pricing of Convertible bonds in China Migration risk based on credit rating

At present,further research and exploration on credit risks are being carried out in the global field,and increasingly profound modem credit risks are exposed to the bond market.This requires that we cannot ignore the impact of credit rating migration risk on bond pricing,so as to adapt to the sustainable and healthy development of the bond market under the new normal of China's economy.The innovation point of this paper is to try to analyze the pricing of Convertible bonds in China from the perspective of credit rating migration risk.Tsiveriotis and Femandes(1998)model is selected,and the credit risk in the model is assumed to be caused by the credit rating migration risk,and the credit spread is used to measure the credit rating migration risk.The research conclusion of this paper is as follows:First,it is valid to consider the risk of credit rating migration in the TF(1998)model.The market price of convertible bonds is on average 1.22% higher 1han the theoretical value of the model.In general,the theoretical value obtained from the model has little deviation from the market price,and has a good fitting degree.Second,from the Angle of credit rating,the selection of 32 samples of convertible bonds only empirical research shows that the credit rating of AA-convertible bonds average deviation rate is negative,suggest that the credit rating of AA-the phenomenon of convertible bonds value is underestimated,and AAA credit rating to AA,AA+,the average deviation rate of convertible bonds is positive,that credit rating AA(containing AA)more convertible bond value is overrated phenomenon,and the higher the credit rating of the average deviation rate of convertible bond,the greater the overvalued levels.It has certain guiding significance for participants in the convertible bond market.

The Development of an Alternative Method for the Sovereign Credit Rating System Based on Adaptive Neuro-Fuzzy Inference System

The main purpose of this article is to determine the factors affecting credit rating and to develop the credit rating system based on statistical methods, fuzzy logic and artificial neural network. Variables used in this study were determined by the literature review and then the number of them was reduced by using stepwise regression analysis. Resulting variables were used as independent variables in the logistic model and as input variables for ANN and ANFIS model. After evaluating the models and comparing with each other, the ANFIS model was chosen as the best model to forecast credit rating. Rating determination was made for the countries that haven’t had a credit rating. Consequently, the ANFIS model made consistent, reliable and successful rating forecasts for the countries.

Application of a Multi-Agent System (MAS) to Rational Credit Rating

A Multi-Agent System （ MAS ） is a promising approach to build complex system. This paper introduces the research of the Inner-Enterprise Credit Rating MAS （ IECRMAS）. To raise the rating accuracy, we not only consider the rating-target＇s information, but also focus on the evaluators＇ feature information and propose the rational rating-group formation algorithm based on an anti-bias measurement of the group. We also propose the rational rating individual, which consists of the evaluator and the assistant rating agent. A rational group formation protocol is designed to coordinate autonomous agents to perform the rating job.

Does the Financial Status of Company Affect the Bond Credit Rating?--Empirical Evidence from China's Shanghai and Shenzhen Stock Exchanges

This article takes the companies that publicly issued corporate bonds on the Shanghai and Shenzhen Stock Exchanges from 2006 to 2018 as the research objects selecting six aspects that comprehensively reflect the 17 financial variables in 6 aspects:profitability,operating ability,bond repayment ability,development ability,cash flow and market value of the company.Principal component analysis method and factor analysis method are used to extract the principal factors of these financial indicator variables.That is how an ordered multi-classification Logistic regression model is constructed to test the impact of the Shanghai and Shenzhen Stock Exchanges’financial status on the corporate bond credit rating.It turns out that the financial status of the Shanghai and Shenzhen Stock Exchanges have an important impact on the credit rating of corporate bonds.The financial status has a greater impact on corporate bonds with credit ratings of A-and AA-,while it has a smaller impact on corporate bonds with credit ratings above AA.The results of this article can help individual and institutional investors prevent risks from investing.

我国不同付费模式信用评级校验机制研究

我国债券市场刚性兑付被打破后,不断出现高评级债券违约事件。为此,监管部门积极发展多种评级,以期提高债券评级质量。通过理论建模与实证分析,研究了不同付费模式信用评级如何综合发挥校验作用。基于博弈模型的理论分析发现,不同付费模式评级发挥校验作用的机制在于投资者的反应:当投资者付费和发行人付费两种模式评级差值增大,投资者不以单一评级结果进行债券定价,而是根据评级差值调整债券价格。基于2011—2020年企业债评级数据的经验证据显示,随着评级差值增大,发行人付费评级机构会降低后续评级,且评级差值对单一信用评级与债券信用利差之间的负向关系具有抑制作用。进一步检验发现,发行人付费评级机构提供的偏正面的私有信息会降低债券信用利差,而以中债资信为代表的投资者付费评级机构提供的私有信息则不会显著影响债券信用利差,均体现出单一评级模式在定价方面的弊端。因此,监管部门应继续鼓励发展投资者付费评级模式,并鼓励机构投资者发展内部评级,以充分发挥多种评级模式的交叉校验作用。

企业资源配置战略与债券信用评级

企业战略作为企业全局性、长期性资源配置的重要规划,对企业经营活动具有重要影响,也必然会影响企业的融资行为。本文从企业战略视角出发,以2008—2020年中国A股上市公司为研究对象,运用固定效应模型探究了企业资源配置战略对债券信用评级的影响。研究发现:相对于经营主导型企业,投资主导型企业的债券信用评级更低,并且该结论在处理内生性问题和进行一系列稳健性检验后依然成立。异质性分析发现,企业资源配置战略对债券信用评级的影响在非国有企业和分析师跟踪人数多的企业中更明显。中介效应检验结果表明,企业资源配置战略通过作用于企业违约风险而影响债券信用评级。本文的研究结论对发债企业、信用评级机构和债券市场投资者具有重要启示意义。

利率市场化改革的信贷配置质量效应研究

不当行政干预造成区域壁垒,诱发寻租问题,是导致要素市场分割与资源错配的重要成因,信贷市场尤为如此。基于商业银行信贷行为选择视角,运用2003—2019年我国31个省份的面板数据,对地方政府干预情形下我国利率市场化改革的信贷配置质量效应加以研究。结果表明,在干预市场条件下,商业银行具有较强的信贷寻租动机,信贷投放更倾向于迎合地方政府需求而非商业化导向;利率市场化改革带来的市场约束收紧尽管可促使商业银行增强信贷投放的商业性,显著增强区域贷款增长对盈利能力的敏感性,但无法对其信贷寻租动机形成有效抑制,以致其对信贷配置的质量效应难以发挥。而良好的地方政府治理可有效提升利率市场化改革的信贷配置质量效应。研究结论有助于厘清高质量发展要求下深化利率市场化改革的关键点,也为新形势下破除地方行政壁垒、增强地方金融监管效能以助推全国统一大市场建设提供经验参考。

外汇衍生品监管与跨境投资企业信用评级——基于“企业层面外汇衍生品监管指数”的实证分析

“一带一路”倡议和“走出去”战略加速了外汇衍生品市场的发展,而加强外汇衍生品监管是市场平稳运行的重要保障。在创新性地构建“企业层面外汇衍生品监管指数”的基础上,基于2007—2019年中国跨境投资企业数据,实证检验了外汇衍生品监管对企业信用评级的影响及作用机制。研究发现,加强外汇衍生品监管能够有效提升企业信用评级,且主要通过减少企业违规行为、增强信息透明度和抑制管理者过度自信三种渠道提升信用评级。异质性分析表明,上述提升效果在国有企业中更大。进一步研究发现,加强外汇衍生品监管可以抑制企业信用评级的调整动机。因此,监管部门应优化衍生品监管法规,倡导套期保值原则,提高评级机构违规成本,提升衍生品交易信息透明度,进而促进外汇衍生品市场的健康发展。

纳税信用评级制度与企业劳动收入份额

采用2011—2020年A股上市公司为研究样本,探讨了纳税信用评级制度对企业劳动收入份额的影响及其机理。研究发现:纳税信用评级制度显著提高了企业劳动收入份额。机制分析表明,纳税信用评级制度通过缓解企业融资约束和调整企业雇佣决策进而影响企业劳动收入份额。异质性分析发现,纳税信用评级制度对劳动收入份额的促进作用对于非国有企业、信息不对称较高企业以及普通员工更为显著。本研究对于提高劳动收入份额,促进共同富裕具有重要意义。

公开发行债券能提高企业创新产出吗?

以2006-2021年我国A股上市公司为样本,探讨了企业首次发行债券如何影响企业的创新水平。研究发现,企业首次公开发行债券能够显著提升企业的创新水平。机制分析结果表明,企业发行债券通过建立替代性融资渠道缓解融资约束,延长整体债务期限,提高信息披露水平这三个路径来提升企业创新水平。进一步研究发现,在控制了企业后续发债行为后,公开发行债券与企业创新的关系仍然存在;相比于公司债,企业首次发行债券种类为中期票据和企业债时,企业创新水平提升更显著;企业首次发行债券时,债券信用评级越高、发行利率越低,对于企业创新水平的提升越显著。研究结论丰富了债券市场经济后果的研究,也为企业创新发展和实现经济高质量发展提供了来自债券市场的建议。

纳税信用评级、信息不对称与企业债务融资成本

纳税信用评级制度是我国社会信用体系建设在税收领域的一项重要改革措施,也是税收现代化建设的重要组成部分。基于2014—2019年我国沪深A股上市公司的数据,实证检验了纳税信用评级对企业债务融资成本的影响,研究表明纳税信用评级与企业债务融资成本负相关,纳税信用评级为A级可以降低企业的债务融资成本;机制检验发现,纳税信用评级为A级可以通过降低企业和银行之间的信息不对称程度,进而降低企业的债务融资成本;进一步的异质性分析发现,相比于国有企业,纳税信用评级对企业债务融资成本的影响在非国有企业中更加显著。研究结论丰富了纳税信用评级与企业债务融资成本的研究,也为推进社会信用体系建设、完善税收征管方式提供了一定的借鉴。

证监会警示函处罚与评级机构应对

本文分析了中国证监会对评级机构的警示函处罚带来的影响和评级机构的应对。研究发现,受处罚的评级机构会显著上调债券发行人的主体信用评级,并获得更高的评级市场份额。在采用熵平衡匹配等方法进行稳健性检验后,结果仍然存在。进一步分析发现,上调发行人评级的动作在发行人与评级机构之间存在利益关联,或者发行人自身违约风险较低时更为明显。本文系统地分析了警示函处罚对评级机构行为的影响,为我国评级机构监管的发展提供了实证依据。