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 ...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.展开更多
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 ga...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.展开更多
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 bus...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.展开更多
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 pr...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.展开更多
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 th...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.展开更多
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 ar...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.展开更多
A model was proposed for addressing investment risk of the flee reserve in the form of credit or currency risk. This risk was expressed by a constant amount K ( e. g., securitization) upon an interest-increasing eve...A model was proposed for addressing investment risk of the flee reserve in the form of credit or currency risk. This risk was expressed by a constant amount K ( e. g., securitization) upon an interest-increasing event and a random variable Z representing the recovery rate of a bond or a devaluation factor. The model equation is an integro-differential equation with deviating arguments. The analytical solutions were obtained for the probability of survival as Z is a discrete random variable and as Z is a continuous random variable respectively.展开更多
Based on Bayes' theorem we point out that the false positive rate must be lower than the intrusion base rate in order to make the Alarm Credibility Probability of the intrusion detection system exceed 50%. We pres...Based on Bayes' theorem we point out that the false positive rate must be lower than the intrusion base rate in order to make the Alarm Credibility Probability of the intrusion detection system exceed 50%. We present the methods that have been used in our developing intrusion detection system AIIDS (artificial immune intrusion detection systems) to increase the creditability of anomaly detection system. These methods include increasing the regularities of the system call trace by use of Hidden Markov Model (HMM), making every antibody or detector has finite lifetime, offering the detector a co-stimulate signal to illustrate whether there is damage in the system according to the integrity, confidentiality, or availability of the system resource.展开更多
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-&...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.展开更多
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 receiva...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.展开更多
Between states, between enterprises and enterprises, between people, it can be stated that credit is full of every corner of our lives. But the current lack of social credit is fundamental. Credit risk is particularly...Between states, between enterprises and enterprises, between people, it can be stated that credit is full of every corner of our lives. But the current lack of social credit is fundamental. Credit risk is particularly prominent. In the extensive data generation today, the information on personal credit statistics is very large, but still lack the data system processing and screening. Through the information retrieval of 200 credit information reports, this paper constructs the evaluation system of personal credit by using the basic information of the individual. The basic information of these individuals has great convenience in information collection and information statistics, and this basic information covers all aspects that are likely to result in the breach of contract. Through the use of single factor analysis and logistic model to solve the index system, you can not only find the impact of individual indicators on the degree of personal credit, but also see the overall impact of indicators on the degree of credit, that is, the weight of the indicators. Finally, four different credit ratings are divided by assigning the indicators to the scores. Credit rating can clearly measure the respective credit situation. Through the classification of these levels, measuring the credit line when a person in the individual credit operation, at the same time, it can provide reference and proval to administrative departments, which is benefit for managing credit risks. It has a substantial meaning and value in use. The solution to the rating system cannot only be applied to individuals, but also to the enterprises, with a wide range of versatility.展开更多
Regarding KMV model identification credit risk profile of small and medium-sized listed companies, at present, domestic scholars has made some achievements in the process of the KMV model combined with China’s nation...Regarding KMV model identification credit risk profile of small and medium-sized listed companies, at present, domestic scholars has made some achievements in the process of the KMV model combined with China’s national conditions. In this paper, we will amend the model by using uncertain interest rate instead of fixed rate on the basis of existing research. Comparing the uncertain KMV model to traditional KMV model with ST-listed companies and non-ST-listed companies in Shanghai and Shenzhen stock exchange, we find that it performs slightly better as a predictor in uncertain KMV model and in out of sample forecasts.展开更多
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 estab...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.展开更多
Implementing new machine learning(ML)algorithms for credit default prediction is associated with better predictive performance;however,it also generates new model risks,particularly concerning the supervisory validati...Implementing new machine learning(ML)algorithms for credit default prediction is associated with better predictive performance;however,it also generates new model risks,particularly concerning the supervisory validation process.Recent industry surveys often mention that uncertainty about how supervisors might assess these risks could be a barrier to innovation.In this study,we propose a new framework to quantify model risk-adjustments to compare the performance of several ML methods.To address this challenge,we first harness the internal ratings-based approach to identify up to 13 risk components that we classify into 3 main categories—statistics,technology,and market conduct.Second,to evaluate the importance of each risk category,we collect a series of regulatory documents related to three potential use cases—regulatory capital,credit scoring,or provisioning—and we compute the weight of each category according to the intensity of their mentions,using natural language processing and a risk terminology based on expert knowledge.Finally,we test our framework using popular ML models in credit risk,and a publicly available database,to quantify some proxies of a subset of risk factors that we deem representative.We measure the statistical risk according to the number of hyperparameters and the stability of the predictions.The technological risk is assessed through the transparency of the algorithm and the latency of the ML training method,while the market conduct risk is quantified by the time it takes to run a post hoc technique(SHapley Additive exPlanations)to interpret the output.展开更多
The way investors, banks and constituents rely on rating agencies will drastically change with the implementation of the Dodd-Frank Act. The historical background of rating agencies including potential changes in the ...The way investors, banks and constituents rely on rating agencies will drastically change with the implementation of the Dodd-Frank Act. The historical background of rating agencies including potential changes in the process of issuing their reports after the Dodd-Frank act is explored by the authors. CPAs (Certified Public Accountant) audit the financial statements of Securities and Exchange Commission [SEC] regulated issuers and are subject to the provisions of the Dodd-Frank act. Accountants may have new potential liabilities with clients that rely on credit agencies representations in financial statements. Analysis is made and conclusions are drawn on the effects of new credit rating agency responsibilities and that of auditors.展开更多
This paper assesses the effect of credit risk management (CRM) on the profitability of Nigerian banks with a view to discovering the extent to which default rate (DR), cost per loan asset (CLA), and capital adeq...This paper assesses the effect of credit risk management (CRM) on the profitability of Nigerian banks with a view to discovering the extent to which default rate (DR), cost per loan asset (CLA), and capital adequacy ratio (CAR) influence return on asset (ROA) as a measure of banks' profitability. Data were generated from secondary sources, specifically, the annual reports and accounts of quoted banks from 2002 to 2011. Descriptive statistics, correlation, as well as random-effect generalized least square (GLS) regression techniques were utilized as tools of analysis in the study. The findings establish that CRM as measured by three independent variables has a significant positive effect on the profitability of Nigerian banks as indicated by the coefficient of determinations "R2 value" which shows the within and between values of 40.89% and 58.35% (which are impressive) while the overall R2 iS 43.91%, indicating that the variables considered in the model account for about 44% change in the dependent variable, that is, profitability. The study recommends that banks' management should be more scientific (application of risk evaluation techniques) in their credit risk assessment and management of loan portfolios in order to minimize the high incidence of non-performing loans and their negative effect on profitability.展开更多
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...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.展开更多
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 t...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.展开更多
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
基金Project supported by National Natural Science Foundation of China (Grant Nos. 10471088, 60572126)
文摘A model was proposed for addressing investment risk of the flee reserve in the form of credit or currency risk. This risk was expressed by a constant amount K ( e. g., securitization) upon an interest-increasing event and a random variable Z representing the recovery rate of a bond or a devaluation factor. The model equation is an integro-differential equation with deviating arguments. The analytical solutions were obtained for the probability of survival as Z is a discrete random variable and as Z is a continuous random variable respectively.
文摘Based on Bayes' theorem we point out that the false positive rate must be lower than the intrusion base rate in order to make the Alarm Credibility Probability of the intrusion detection system exceed 50%. We present the methods that have been used in our developing intrusion detection system AIIDS (artificial immune intrusion detection systems) to increase the creditability of anomaly detection system. These methods include increasing the regularities of the system call trace by use of Hidden Markov Model (HMM), making every antibody or detector has finite lifetime, offering the detector a co-stimulate signal to illustrate whether there is damage in the system according to the integrity, confidentiality, or availability of the system resource.
基金funded by Research Grants Council,Hong Kong,Grant Number UGC/FDS14/B20/16the Hong Kong Polytechnic University,Grant Number P0030199.
文摘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.
文摘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.
文摘Between states, between enterprises and enterprises, between people, it can be stated that credit is full of every corner of our lives. But the current lack of social credit is fundamental. Credit risk is particularly prominent. In the extensive data generation today, the information on personal credit statistics is very large, but still lack the data system processing and screening. Through the information retrieval of 200 credit information reports, this paper constructs the evaluation system of personal credit by using the basic information of the individual. The basic information of these individuals has great convenience in information collection and information statistics, and this basic information covers all aspects that are likely to result in the breach of contract. Through the use of single factor analysis and logistic model to solve the index system, you can not only find the impact of individual indicators on the degree of personal credit, but also see the overall impact of indicators on the degree of credit, that is, the weight of the indicators. Finally, four different credit ratings are divided by assigning the indicators to the scores. Credit rating can clearly measure the respective credit situation. Through the classification of these levels, measuring the credit line when a person in the individual credit operation, at the same time, it can provide reference and proval to administrative departments, which is benefit for managing credit risks. It has a substantial meaning and value in use. The solution to the rating system cannot only be applied to individuals, but also to the enterprises, with a wide range of versatility.
文摘Regarding KMV model identification credit risk profile of small and medium-sized listed companies, at present, domestic scholars has made some achievements in the process of the KMV model combined with China’s national conditions. In this paper, we will amend the model by using uncertain interest rate instead of fixed rate on the basis of existing research. Comparing the uncertain KMV model to traditional KMV model with ST-listed companies and non-ST-listed companies in Shanghai and Shenzhen stock exchange, we find that it performs slightly better as a predictor in uncertain KMV model and in out of sample forecasts.
基金Supported by the National Natural Science Foundation of China(11671301,12071349).
文摘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.
文摘Implementing new machine learning(ML)algorithms for credit default prediction is associated with better predictive performance;however,it also generates new model risks,particularly concerning the supervisory validation process.Recent industry surveys often mention that uncertainty about how supervisors might assess these risks could be a barrier to innovation.In this study,we propose a new framework to quantify model risk-adjustments to compare the performance of several ML methods.To address this challenge,we first harness the internal ratings-based approach to identify up to 13 risk components that we classify into 3 main categories—statistics,technology,and market conduct.Second,to evaluate the importance of each risk category,we collect a series of regulatory documents related to three potential use cases—regulatory capital,credit scoring,or provisioning—and we compute the weight of each category according to the intensity of their mentions,using natural language processing and a risk terminology based on expert knowledge.Finally,we test our framework using popular ML models in credit risk,and a publicly available database,to quantify some proxies of a subset of risk factors that we deem representative.We measure the statistical risk according to the number of hyperparameters and the stability of the predictions.The technological risk is assessed through the transparency of the algorithm and the latency of the ML training method,while the market conduct risk is quantified by the time it takes to run a post hoc technique(SHapley Additive exPlanations)to interpret the output.
文摘The way investors, banks and constituents rely on rating agencies will drastically change with the implementation of the Dodd-Frank Act. The historical background of rating agencies including potential changes in the process of issuing their reports after the Dodd-Frank act is explored by the authors. CPAs (Certified Public Accountant) audit the financial statements of Securities and Exchange Commission [SEC] regulated issuers and are subject to the provisions of the Dodd-Frank act. Accountants may have new potential liabilities with clients that rely on credit agencies representations in financial statements. Analysis is made and conclusions are drawn on the effects of new credit rating agency responsibilities and that of auditors.
文摘This paper assesses the effect of credit risk management (CRM) on the profitability of Nigerian banks with a view to discovering the extent to which default rate (DR), cost per loan asset (CLA), and capital adequacy ratio (CAR) influence return on asset (ROA) as a measure of banks' profitability. Data were generated from secondary sources, specifically, the annual reports and accounts of quoted banks from 2002 to 2011. Descriptive statistics, correlation, as well as random-effect generalized least square (GLS) regression techniques were utilized as tools of analysis in the study. The findings establish that CRM as measured by three independent variables has a significant positive effect on the profitability of Nigerian banks as indicated by the coefficient of determinations "R2 value" which shows the within and between values of 40.89% and 58.35% (which are impressive) while the overall R2 iS 43.91%, indicating that the variables considered in the model account for about 44% change in the dependent variable, that is, profitability. The study recommends that banks' management should be more scientific (application of risk evaluation techniques) in their credit risk assessment and management of loan portfolios in order to minimize the high incidence of non-performing loans and their negative effect on profitability.
文摘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.
文摘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.