Despite the meaningful impact that credit ratings have on sovereign countries and financial markets, research has not fully explored the determinants of these ratings in many emerging markets (EMs). The aim of this study was to identify and quantify the extent to which different macroeconomic factors impact sovereign ratings. Based on a review of the literature, an analysis of rating agencies' methodology papers, and economic intuition, it was hypothesised that measures of wealth, economic growth, monetary stability, fiscal trajectory, external accounts and governance would predict EM credit ratings. This hypothesis was largely supported by regression models that anticipated actual ratings with predictive power comparable to extant research, but across a much broader set of EM countries. By identifying the key drivers of these ratings, the current research suggests several areas that policymakers can address to improve their own sovereign ratings. / Economics / M. Com (Economics)

Thesis (M. Com. (Economics)) -- University of Limpopo, 2021 / The main aim of the study is to analyze determinants of sovereign credit ratings (SCRs) for Southern African Development Community (SADC) countries, namely Angola, Botswana, Mozambique, South Africa and Zambia. The analysis is based on the SCRs given by Standard and Poor’s (S&P). The selected explanatory variables are gross domestic product (GDP) per capita, inflation, external debt, foreign direct investment (FDI) inflows and control of corruption for the period 1990-2016, based on annual data. The panel root test results, namely IPS, LLC, ADF Fisher and PP Fisher, show that GDP per capita, external debt and FDI are stationary at 5% level of significance. The Hausman test results indicates that the identified explanatory variable explains 80% of SCRs. The model observed a positive relationship between SCR, inflation and control of corruption. It also observed a negative correlation between SCR, GDP per capita, external debt and FDI. The Pedroni residual cointegration test results indicate that there is no long-run relationship among variables and no autocorrelation as shown by serial correlation LM test results. The study recommends that the selected member states of SADC develop strategic plans for reducing budget deficits. This will help countries to manage their debts, especially foreign currency denoted debt and to attract foreign investment. Keywords: Sovereign credit ratings, fixed effects model, random effects, Hausman test.

Thesis (MBA (Business Management))--University of Stellenbosch, 2009. / ENGLISH ABSTRACT: For many organisations who wish to sell their debt, or investors who are looking to invest in an organisation, company credit ratings are an important surrogate measure for the marketability or risk associated with a particular issue. Credit ratings are issued by a limited number of authorised companies – with the predominant being Standard & Poor’s, Moody’s and Fitch – who have the necessary experience, skills and motive to calculate an objective credit rating. In the wake of some high profile bankruptcies, there has been recent conjecture about the accuracy and reliability of current ratings. Issues relating specifically to the lack of competition in the rating market have been identified as possible causes of the poor timeliness of rating updates. Furthermore, the cost of obtaining (or updating) a rating from one of the predominant agencies has also been identified as a contributing factor. The high costs can lead to a conflict of interest where rating agencies are obliged to issue more favourable ratings to ensure continued patronage. Based on these issues, there is sufficient motive to create more cost effective alternatives to predicting corporate credit ratings. It is not the intention of these alternatives to replace the relevancy of existing rating agencies, but rather to make the information more accessible, increase competition, and hold the agencies more accountable for their ratings through better transparency. The alternative method investigated in this report is the use of a backpropagation artificial neural network to predict corporate credit ratings for companies in the manufacturing sector of the United States of America. Past research has shown that backpropagation neural networks are effective machine learning techniques for predicting credit ratings because no prior subjective or expert knowledge, or assumptions on model structure, are required to create a representative model. For the purposes of this study only public information and data is used to develop a cost effective and accessible model. The basis of the research is the assumption that all information (both quantitive and qualitative) that is required to calculate a credit rating for a company, is contained within financial data from income statements, balance sheets and cash flow statements. The premise of the assumption is that any qualitative or subjective assessment about company creditworthiness will ultimately be reflected through financial performance. The results show that a backpropagation neural network, using 10 input variables on a data set of 153 companies, can classify 75% of the ratings accurately. The results also show that including collinear inputs to the model can affect the classification accuracy and prediction variance of the model. It is also shown that latent projection techniques, such as partial least squares, can be used to reduce the dimensionality of the model without making any assumption about data relevancy. The output of these models, however, does not improve the classification accuracy achieved using selected un-correlated inputs. / AFRIKAANSE OPSOMMING: Vir baie organisasies wat skuldbriewe wil verkoop, of beleggers wat in ʼn onderneming wil belê is ʼn maatskappy kredietgradering ’n belangrike plaasvervangende maatstaf vir die bemarkbaarheid van, of die risiko geassosieer met ʼn betrokke uitgifte. Kredietgraderings word deur ʼn beperkte aantal gekeurde maatskappye uitgereik – met die belangrikste synde Standard & Poor’s, Moody’s en Fitch. Hulle het almal die nodige ervaring, kundigheid en rede om objektiewe kredietgraderings te bereken. In die nadraai van ʼn aantal hoë profiel bankrotskappe was daar onlangs gissings oor die akkuraatheid en betroubaarheid van huidige graderings. Kwessies wat spesifiek verband hou met die gebrek aan kompetisie in die graderingsmark is geïdentifiseer as ‘n moontlike oorsaak vir die swak tydigheid van gradering opdatering. Verder word die koste om ‘n gradering (of opdatering van gradering) van een van die dominante agentskappe te bekom ook geïdentifiseer as ʼn verdere bydraende faktor gesien. Die hoë koste kan tot ‘n belange konflik lei as graderingsagentskappe onder druk kom om gunstige graderings uit te reik om sodoende volhoubare klante te behou. As gevolg van hierdie kwessies is daar voldoende motivering om meer koste doeltreffende alternatiewe vir die skatting van korporatiewe kredietgraderings te ondersoek. Dit is nie die doelwit van hierdie alternatiewe om die toepaslikheid van bestaande graderingsagentskappe te vervang nie, maar eerder om die inligting meer toeganklik te maak, mededinging te verhoog en om die agentskappe meer toerekenbaar vir hul graderings te maak deur beter deursigtigheid. Die alternatiewe manier wat in hierdie verslag ondersoek word, is die gebruik van ‘n kunsmatige neurale netwerk om die kredietgraderings van vervaardigingsmaatskappye in die VSA te skat. Vorige navorsing het getoon dat neurale netwerke doeltreffende masjienleer tegnieke is om kredietgraderings te skat omdat geen voorafkennis of gesaghebbende kundigheid, of aannames oor die modelstruktuur nodig is om ‘n verteenwoordigende model te bou. Vir doeleindes van hierdie navorsingsverslag word slegs openbare inligting en data gebruik om ʼn kostedoeltreffende en toeganklike model te bou. Die grondslag van hierdie navorsing is die aanname dat alle inligting (beide kwantitatief en kwalitatief) wat benodig word om ʼn kredietgradering vir ʼn onderneming te bereken, opgesluit is in die finansiële data in die inkomstestate, balansstate en kontantvloei state. Die aanname is dus dat alle kwalitatiewe of subjektiewe assessering oor ‘n maatskappy se kredietwaardigheid uiteindelik in die finansiële prestasie sal reflekteer. Die resultate toon dat ʼn neurale netwerk met 10 toevoer veranderlikes op ‘n datastel van 153 maatskappye 75% van die graderings akkuraat klassifiseer. Die resultate toon ook dat die insluiting van kollineêre toevoere tot die model die klassifikasie akkuraatheid en die variansie van die skatting kan beïnvloed. Daar word verder getoon dat latente projeksietegnieke, soos parsiële kleinste kwadrate, die dimensies van die model kan verminder sonder om enige aannames oor data toepaslikheid te maak. Die afvoer van hierdie modelle verhoog egter nie die klassifikasie akkuraatheid wat behaal is met die gekose ongekorreleerde toevoere nie. 121 pages.

Thesis (MBA (Business Management))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: NamPower is the current power utility in Namibia and needs to access the debt capital markets over the next few years, in order to be successful to finance its capital expenditure programme of NAD 13.9 billion. NamPower intends to raise the funding from its operations, shareholders equity injection and debt, in the form of bonds and development finance. In order to be successful in its bond issuance programme, NamPower must at least maintain an investment grade credit rating. Credit Rating Agencies play an important role to provide investors with their credit ratings and reports. Many investors base their investment decision making on certain levels of credit ratings. A credit rating is the probability that an issuer or instrument will default on its debt repayment obligation. Depending on the circumstances, investors usually require a minimum of an investment grade rating (AFP, 2009:20). Looking at the current financial crisis investors felt left down by the credit rating agencies, as investors relied on the credit rating reports and the underlying credit rating. Investors literally lost billions in financial crisis of 2007-8/9 as corporate and structured products defaulted on meeting financial obligations. As a result of the defaults and financial crisis the credit rating agencies have been criticised for inadequate disclosure and potential conflicts of interest. Many critics argue that credit rating agencies are not asking inquisitive questioning and probing into issues when doing credit reviews. Evidence was not that conclusive, but big corporate failures like Enron and WorldCom are examples of the credit rating agencies’ failures. Furthermore, credit rating agencies are not particular about creating predictions of future developments, but the last crisis has shown that credit rating agencies were fairly successful with corporate or issuer ratings as default has been fairly limited to the higher credit rating categories. Evidence provided in the research supports that investors still rely on credit ratings more so for corporate, institutions and fixed income products, but are very insure about structured products, due to recent market failures. Therefore it is still of critical importance for NamPower to maintain its investment grade credit rating. NamPower has maintained and even improved on its local national scale credit rating. Investors are still risk adverse since the financial crisis but as economic conditions improve investors should be coming back to emerging markets. To bring back the investors to invest in the emerging markets will require a certain appetite returning to the investor, but surely there will be a premium or funding will be more costly in future and not in demand as previously. As for NamPower, the opinion is therefore that although smaller in size, it poses as an attractive investment opportunity for investors as there is shortage in investment grade assets in Sub-Sahara Africa to fill the portfolio gaps and give diversification.

published_or_final_version / Economics and Finance / Master / Master of Economics

published_or_final_version / Real Estate and Construction / Doctoral / Doctor of Philosophy

本研究以2001至2006年台灣上市、上櫃公司為研究對象，探討信用評等與資本結構的關係。參考Kisgen (2006)，以融資順位理論和靜態抵換理論為基礎，本研究探討：(1) 面臨信用評等調等之公司，是否會減少其負債水準，以避免(促使)信用評等調降(調升)，(2) 面臨信用評等調等之公司，是否會背離融資順位理論及靜態抵換理論，而減少長期債務水準。 分析信用評等調等與負債水準關聯性之實證結果顯示，信用評等為影響資本結構之重要因素。企業會因信用評等面臨調等，而減少其負債。此外，企業利用資本結構的改變以避免信用評等調降的動機較促使信用評等調升之動機強，而此種現象又以規模較大之公司較為顯著。 分析信用評等調等對資本結構理論之影響之實證結果顯示，在納入信用評等變數後，面臨信用評等調等之公司有較高傾向背離融資順位理論和靜態抵換理論，進一步減少其長期債務之水準。 / Based on a sample of listed companies in Taiwan over the period of 2001 to 2006, this research investigates the relationship between credit ratings and capital structure. Refers to Kisgen (2006), and result on the Pecking order theory and the Static trade-off theory, this research examines:(1) whether firms near a credit ratings upgrade or downgrade would issue less debt relative to equity. (2) whether firms near a credit ratings upgrade or downgrade would issue less long-term debts and thus depart from the Pecking order theory and the Static trade-off theory. The findings reveals that credit ratings is an important factor to determination of capital structure. The results show that firms near a credit ratings upgrade or downgrade would issue less debt relative to equity. The findings also indicates that firms are more inclined to avoid the downgrade of their credit ratings than to instigate the upgrade of their credit ratings. Such phenomena is more obviously for larger firms. In addition, this research also finds that firms near a credit rating upgrade or downgrade would issue less long-term debts and thus depart from the Pecking order theory and the Static trade-off theory, after taking their credit ratings into consideration.

This paper introduces a model framework for dynamic credit rating processes. Our framework aggregates ordinal rating information stemming from a variety of rating sources. The dynamic of the consensus rating captures systematic as well as idiosyncratic changes. In addition, our framework allows to validate the different rating sources by analyzing the mean/variance structure of the rating deviations. In an empirical study for the iTraxx Europe companies rated by the big three external rating agencies we use Bayesian techniques to estimate the consensus ratings for these companies. The advantages are illustrated by comparing our dynamic rating model to a naive benchmark model. (authors' abstract)

O risco é um aspecto importante a ser considerado nas avaliações empresariais e, diante das crises financeiras globais, os ratings divulgados pelas agências de classificação de riscos são fundamentais para o gerenciamento de riscos nas empresas, bem como para a tomada de decisão dos investidores ao escolher em qual empresa investir. Diante do exposto, o presente trabalho se propôs a identificar os fatores que podem explicar as atribuições dos ratings com especial atenção ao impacto do uso de derivativos. A partir disso, a principal novidade apresentada nesta pesquisa foi a de averiguar o reflexo do uso de derivativos juntamente com as posições de proteções assumidas pelas empresas nas classificações de créditos - ajudando a suprir, assim, essa lacuna na literatura da área. Para isso, foram utilizados 2.090 ratings e analisadas as empresas não financeiras da B3 entre os anos de 2010 e 2016 por meio de análise dos dados em painel, conferindo maior robustez às análises e aos achados. Os resultados indicaram que as empresas que utilizam instrumentos financeiros derivativos não recebem os melhores ratings. Esses resultados contestam a teoria de que o uso de derivativos é visto positivamente pelos investidores. No entanto, apesar de nenhum impacto significativamente estatístico ter sido encontrado nos ratings das empresas que utilizam derivativos, observou-se que as empresas que usam derivativos e possuem os maiores valores nocionais foram as que receberam as melhores notas da agência Moody\'s. / Risk assessment is an important aspect concerning business valuation and, considering the global economic crisis, the information disclosed by rating agencies is essential to developing a risk management plan and making investment decisions. The purpose of the present study is therefore to identify the factors that may explain the attribution of risk ratings, focusing on the impact of derivatives. Thereafter, ascertaining the effects of derivatives combined with protective business behaviors regarding credit ratings is innovative and assists in filling knowledge gaps in research and literature. In this study, 2.090 ratings were considered and B3\'s non-financial companies were examined between 2010 and 2016 by using panel data analysis, which lends robustness to the analysis and the findings. Results indicate that companies that use derivative instruments are not attributed the best ratings, thus opposing the theory that the use of derivatives attracts investors. Although ratings showed no significant statistical impact on companies that use derivatives, companies with the highest notional values, which also use derivatives, were attributed the best ratings by Moody\'s.

Thesis advisor: Thomas J. Chemmanur / Thesis advisor: Arthur Lewbel / The dissertation aims to investigate the role of asymmetric information in capital structure, investment, compensation of mortgage servicers, and bond and equity returns. Specifically, I evaluate the impact of credit ratings on debt issuance and investment of private and public firms, as well as the effect of asymmetric information on compensation of loan servicers in the mortgage backed securities market. Further, I study the relationship between ratings issued by investor and issuer-paid credit rating agencies and equity analyst recommendations. Finally, I evaluate the effect of the aforementioned signals on bond and equity returns as well as firm leverage and investment decisions. Chapter one in the dissertation is the first study to empirically evaluate the effect of credit ratings on capital structure and investment for private U.S. firms, relative to equivalent public firms. I find that private firms constrain debt issuance and investment by 4.5 and 6.5 percentage points more than public firms, respectively, when their credit ratings are on upgrade or downgrade thresholds. Consistent with these results, private firms that become public through an IPO constrain debt issuance by 10 percentage points before going public, if their ratings are on an upgrade or downgrade boundary. The second chapter studies the impact of asymmetric information between mortgage sellers and servicers on mortgage servicer compensation. We proxy for asymmetric information using the decision to retain mortgage servicing rights, which creates a principal-agent problem between sellers and servicers. Using loan-level data on Fannie Mae-insured, full documentation mortgages, we first find that loans in which sellers retain servicing rights default and foreclose at a significantly lower rate, and lose less in foreclosure than those in which they are not retained. Since it is more costly to service non-performing loans, these ex-post differences in default rates should be reflected in servicer compensation. However, using Fannie Mae MBS pool-level data, we find no difference in servicing fees for pools in which servicing rights are retained relative to pools in which they are not retained. In order to identify the impact of seller/servicer affiliation on servicing fees, we exploit a post-crisis regulatory change which altered the incentive to retain servicing rights for small sellers of MBS relative to large sellers. Finally, in the third chapter, we evaluate the information flows to the stock and bond markets of issuer versus investor-paid rating agencies and equity analysts. Equity analysts' forecasts and ratings assigned by issuer-paid credit rating agencies such as Standard and Poor's (S&P) and by investor-paid rating agencies such as Egan and Jones (EJR) all involve information production about the same underlying set of firms, even though equity analysts focus on cash flows to equity and bond ratings focus on cash flows to bonds. Further, the two types of credit rating agencies differ in their incentives to produce and report accurate information signals. Given this setting, we empirically analyze the timeliness and accuracy of the information signals provided by each of the above three types of financial intermediary to their investor clienteles and the information flows between these intermediaries. We find that the information signals produced by EJR are the most timely (on average), and seem to anticipate the information signals produced by equity analysts as well as by S&P. We find that changes in leverage are associated with lower EJR ratings but higher equity analyst recommendations; further, credit rating changes by EJR have the largest impact on firms' investment levels. We also document an "investor attention" effect (in the sense of Merton, 1987) among stock and bond market investors in the sense that changes in equity analyst recommendations have a higher impact than either EJR or S&P ratings changes on the excess returns on firm equity, while EJR rating changes have a higher impact on bond yield spreads than either S&P ratings changes or changes in equity analyst recommendations. Finally, we analyze differences in bond ratings assigned to a given firm by EJR and S&P, and find that these differences are positively related to the standard proxies for disagreement among stock market investors.