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.

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.

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 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.

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.

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.

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.

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.

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.

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.

Governance or reputation?Flexible tax enforcement and excess goodwill:Evidence from the taxpaying credit rating system in China

This study investigates the effect of flexible tax enforcement on firms’excess goodwill using unique manually collected data on taxpaying credit rating in China from 2014 to 2021.We document that A-rated taxpayer firms have less excess goodwill;A-rated firms reduce excess goodwill by 0.005 vis-a-vis non-Arated firms,which accounts for 100%of the mean value of excess goodwill.This finding holds after multiple robustness tests and an endogeneity analysis.Moreover,this negative effect is more pronounced in firms with low information transparency,that are non-state-owned and that are located in regions with low tax enforcement intensity.The channel test results suggest that taxpaying credit rating system as flexible tax enforcement reduces firms’excess goodwill through a reputation-based effect and not a governance-based effect.This study reveals that the taxpaying credit rating system in China as flexible tax enforcement can bring halo effect to A rating firms,thereby limiting irrational M&As and breaking goodwill bubble.

Credit ratings of Chinese online loan platforms based on factor scores and K-means clustering algorithm

The rapid development of Chinese online loan platforms(OLPs),as well as their risks,has attracted widespread attention,increasing the demand for a complete credit rating mechanism.The present study establishes a credit rating indicator system for 130 mainstream Chinese OLPs that combines 12 quantitative metrics of online loan operations similar to commercial bank credit rating indicators,including platform transaction volume and average expected rate of return.We also consider two qualitative indicators of online loan background,namely platform background and guarantee mode,that reflect Chinese characteristics.Subsequently,a factor analysis was conducted to reduce the 14 indicators dimensions.The loads of the rating indicators in the resulting rotating component matrix were refined into an OLP operation scale factor,fund dispersion factor,security factor,and profitability factor.Finally,a K-means clustering algorithm was employed to cluster the factor scores of each OLP,thereby obtaining credit rating results.The empirical results indicate that the proposed machine learning-based credit rating method effectively provides early warnings of problem platforms,yielding more accurate credit ratings than those provided by two mainstream online loan rating websites in China,namely,Wangdaitianyan and Wangdaizhijia.

Tax credit rating and corporate innovation decisions

The tax credit rating mechanism was formally implemented in 2014.As an important tax collection and management innovation,it has attracted the attention of regulatory authorities and scholars.Different from the literature that directly examines corporate tax compliance,we focus on the impact of tax credit rating implementation on corporate research and development(R&D)investment decisions.Using listed companies’data from 2014 to 2019,we find that companies with higher tax credit ratings invest more in innovation,because the system helps managers identify R&D opportunities,alleviates corporate financing constraints and reduces agency costs.We confirm that tax credit ratings have manifold impacts on corporate information environments and business decisions,with better ratings positively affecting firms’business decisions.This discovery can inform tax policy reform,encourage corporate innovation and construct social credit systems.

Seeking Universal Credit Ratings

Amid the EU’s ongoing sovereign debt crisis,the current international credit rating system has been accused of aggravating the world’s economic woes.Recently,Guan Jianzhong,Chairman of the Board and President of the Beijing-based Dagong Global Credit Rating Co.Ltd.,spoke to Beijing Review reporter Yu Yan about reforms in the current international credit rating system and Dagong’s role.Dagong is the first non-Western rating agency to assess the world’s sovereign credit and risks.

Cyberattack Ramifications, The Hidden Cost of a Security Breach

In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.

Value of big data to finance:observations on an internet credit Service Company in China

Background:his paper presents a case study on 100Credit,an Internet credit service provider in China.100Credit began as an IT company specializing in e-commerce recommendation before getting into the credit rating business.The company makes use of Big Data on multiple aspects of individuals’online activities to infer their potential credit risk.Methods:Based on 100Credit’s business practices,this paper summarizes four aspects related to the value of Big Data in Internet credit services.Results:1)value from large data volume that provides access to more borrowers;2)value from prediction correctness in reducing lenders’operational cost;3)value from the variety of services catering to different needs of lenders;and 4)value from information protection to sustain credit service businesses.Conclusion:The paper also discusses the opportunities and challenges of Big Databased credit risk analysis,which needs to be improved in future research and practice.

Portfolio optimization of credit risky bonds: a semi-Markov process approach

This article presents a semi-Markov process based approach to optimally select a portfolio consisting of credit risky bonds.The criteria to optimize the credit portfolio is based on l_(∞)-norm risk measure and the proposed optimization model is formulated as a linear programming problem.The input parameters to the optimization model are rate of returns of bonds which are obtained using credit ratings assuming that credit ratings of bonds follow a semi-Markov process.Modeling credit ratings by semi-Markov processes has several advantages over Markov chain models,i.e.,it addresses the ageing effect present in the credit rating dynamics.The transition probability matrices generated by semi-Markov process and initial credit ratings are used to generate rate of returns of bonds.The empirical performance of the proposed model is analyzed using the real data.Further,comparison of the proposed approach with the Markov chain approach is performed by obtaining the efficient frontiers for the two models.

Research and Practice of Telecommunication User Rating Method Based on Machine Learning

The machine learning model has advantages in multi-category credit rating classification.It can replace discriminant analysis based on statistical methods,greatly helping credit rating reduce human interference and improve rating efficiency.Therefore,we use a variety of machine learning algorithms to study the credit rating of telecom users.This paper conducts data understanding and preprocessing on Operator Telecom user data,and matches the user’s characteristics and tags based on the time sliding window method.In order to deal with the deviation caused by the imbalance of multi-category data,the SMOTE oversampling method is used to balance the data.Using the Removing features with low variance method and packaging method for feature selection,then the basic models are established.The empirical results of the model show that the Random Forest and XGBOOST ensemble models are better than the single models such as Bayes,SVM,KNN,and Decision Tree.The performance of Decision Tree in single models is better.Therefore,Random Forest,XGBOOST and Decision Tree models were selected to debug the hyper parameters to achieve model optimization.Based on the optimized model,the accuracy,recall,precision,confusion matrix and other indicators are evaluated,and it is concluded that lowlevel recognition is more accurate than high-level recognition and fewer misjudgments.Comparing the evaluation indicators of each level of different models,it is found that the integrated model performs better,indicating that Random Forest and XGBOOST are more suitable for solving the problem of telecommunications user rating.For this reason,this article proposes an implementation plan based on Random Forest and XGBOOST algorithm and model for the problem of telecommunications user rating.

A Method for Forecasting Credit Risk of Global Device Purchasing

With the development of the Internet and E commerce, enterprises can achieve global device purchasing with a good cost performance. But the credit risk is the key factor in selecting a device provider. Credit risk involves many qualitative and quantitative factors. We construct a multi agent credit rating model system based on CSCW, which organically combines the people's aptitude and the capability of machines. Enterprises can use this credit rating system for forecasting and defeating the credit risk of global device purchasing.

A Markov regenerative process with recurrence time and its application

This study proposes a non-homogeneous continuous-time Markov regenerative process with recurrence times,in particular,forward and backward recurrence processes.We obtain the transient solution of the process in the form of a generalized Markov renewal equation.A distinguishing feature is that Markov and semi-Markov processes result as special cases of the proposed model.To model the credit rating dynamics to demonstrate its applicability,we apply the proposed stochastic process to Standard and Poor’s rating agency’s data.Further,statistical tests confirm that the proposed model captures the rating dynamics better than the existing models,and the inclusion of recurrence times significantly impacts the transition probabilities.