Bank loan ratings are employed to investigate the interrelationship between the rating agencies and banks. Valuation effects of rating announcements on investors in the market are also examined. Similar functions are performed by rating agencies and banks, however, it is found that investors perceive information provided by rating agencies and banks differently. In the first essay, the results indicate that investors recognise the value of rating agencies in the presence of banks as information providers and monitors. The value of rating agencies relies on their recognition of deteriorating prospects in a firm's financial position, as the market reacts significantly to bank loan rating announcements of placement on CreditWatch with negative implications and downgrades. In the second essay, the results indicate that investors recognise the value of high quality banks in the presence of rating agencies as information providers and monitors. When the deteriorated firms are associated with high quality banks, the negative reaction toward announcements of negative placement and downgrade is mitigated. This indicates that investors are willing to trust high quality banks' speciality in information and monitoring, and reassess the value of deteriorated firms. In the third essay, the results show that the value of rating agencies; via announcements of negative placement and downgrade; also expands to non-rated firms smaller than the rated firms, in the same industry. Announcements of negative placement indicate firm-specific deterioration and, therefore, smaller rival firms benefit from the change in competitive balance. From the announcements of downgrade, however, smaller rival firms experience contagion effect.

Thesis (M.A.) - Carleton University, 2006. / Includes bibliographical references (p. 122-128). Also available in electronic format on the Internet.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

This thesis contains three empirical studies on the US corporate bond market; each chapter is self-contained and can be read independently. Chapter 1 studies the impact of credit rating changes on corporate bond returns. This study uses a large dataset of corporate bond transactions from the TRACE database for the US corporate bond market, combined with credit rating changes from Fitch, Moody's and Standard and Poor's (S&P), to analyse over 22,000 bonds, coupled with approximately 28,400 rating events over nearly six years. The results show that the bond market responds to news on credit quality asymmetrically: credit rating downgrades, representing bad news for bond holders, produce the strongest response in returns, whilst upgrades do not generate a statistically significant increase in returns. Chapter 2 analyses how order flow (investor "buy" and "sell" trades), impacts corporate bond prices. Order flow plays an important informational role, acting as a conduit through which private information about fundamental value is aggregated into prices. Using intraday transaction data from the TRACE database, I analyse over 1,000 of the most liquid corporate bonds, a total of 9.5 million trades. Drawing on similar studies of other markets, the relationship between returns and order flow is modelled using a vector autoregression, and the information content of a trade is measured as the long-run price impact of a shock to order flow. Price impacts are particularly strong and significant for order flow from institutional investors and for bonds with higher default risk, higher volatility and lower liquidity. Chapter 3 provides novel evidence on the importance of high frequency measures of volatility and correlation for the corporate bond market. Realized measures of volatility have been shown to be important in modelling and forecasting equity, exchange rate, and Treasury bill return volatility. We merge the NYSE's TAQ database of high frequency equity prices with the TRACE database, and show that the information contained in high frequency data is valuable in modelling the dynamics of the firm-level covariance matrix of bond and stock returns, for over 100 individual U.S. firms.

Correlated ordinal data typically arises from multiple measurements on a collection of subjects. Motivated by an application in credit risk, where multiple credit rating agencies assess the creditworthiness of a firm on an ordinal scale, we consider multivariate ordinal regression models with a latent variable specification and correlated error terms. Two different link functions are employed, by assuming a multivariate normal and a multivariate logistic distribution for the latent variables underlying the ordinal outcomes. Composite likelihood methods, more specifically the pairwise and tripletwise likelihood approach, are applied for estimating the model parameters. Using simulated data sets with varying number of subjects, we investigate the performance of the pairwise likelihood estimates and find them to be robust for both link functions and reasonable sample size. The empirical application consists of an analysis of corporate credit ratings from the big three credit rating agencies (Standard & Poor's, Moody's and Fitch). Firm-level and stock price data for publicly traded US firms as well as an unbalanced panel of issuer credit ratings are collected and analyzed to illustrate the proposed framework.

Correlated ordinal data typically arise from multiple measurements on a collection of subjects. Motivated by an application in credit risk, where multiple credit rating agencies assess the creditworthiness of a firm on an ordinal scale, we consider multivariate ordinal models with a latent variable specification and correlated error terms. Two different link functions are employed, by assuming a multivariate normal and a multivariate logistic distribution for the latent variables underlying the ordinal outcomes. Composite likelihood methods, more specifically the pairwise and tripletwise likelihood approach, are applied for estimating the model parameters. We investigate how sensitive the pairwise likelihood estimates are to the number of subjects and to the presence of observations missing completely at random, and find that these estimates are robust for both link functions and reasonable sample size. The empirical application consists of an analysis of corporate credit ratings from the big three credit rating agencies (Standard & Poor's, Moody's and Fitch). Firm-level and stock price data for publicly traded US companies as well as an incomplete panel of issuer credit ratings are collected and analyzed to illustrate the proposed framework. / Series: Research Report Series / Department of Statistics and Mathematics

One of the assumptions upon which modern portfolio theory is based is the efficient market hypothesis which postulates that market prices fully reflect all available information, which implies that an abnormal return cannot be made. Evidence has amassed in contradiction to the efficient market hypothesis as demonstrated by Jegadeesh and Titman (1993); Mohanram (2005); Montier (2009) and Piotroski, (2000). However these studies demonstrated earning an abnormal return by buying an asset as opposed to selling an asset. Evidence by Altman (2000) and Beneish, Lee and Nichols (2013) affirmed that abnormal returns may be earned by selling a declining asset. There has been no published work conducted on the South African market pertaining to an instrument that may be used to detect a decline in share price due to prior earnings manipulation, thereby providing the scope of this research. In recent years the focus of the discipline of asset pricing has shifted away from theoretical modelling towards empirical analysis. The C-score by Montier (2008) is a binary earnings manipulation detection model, designed to identify stocks that may be shorted for an abnormal return. An exploratory study of stocks on the Johannesburg Stock Exchange (JSE) from 2002 to 2010 was conducted. Vital focus areas included the resources and industrials sector. Results of this research prove that C-score is insufficient as a stand-alone tool for detecting shortable stocks on the JSE. Whilst negative relative returns were earned for certain holding periods of certain sectors, a consistent trend could not be isolated. / Dissertation (MBA)--University of Pretoria, 2013. / pagibs2014 / Gordon Institute of Business Science (GIBS) / MBA / Unrestricted

Credit Rating Agencies (CRAs) play a key role in financial markets by helping to reduce informative asymmetry between lenders and investors, on one side, and issuers on the other side, with regard to the creditworthiness of banks or countries. This crucial role has expanded alongside financial globalisation and received an additional boost from Basel II which integrates the ratings of CRAs into the rules for setting weights for credit risk. Ratings adjustment tends to be sticky, lagging behind markets, and often overreact when they do change. This overreaction may have aggravated the recent financial crises, contributing to financial instability and cross-country contagion. Criticism has been especially directed towards the high degree of concentration of the ratings industry. Promotion of competition may require policy action at the international level to encourage the establishment of new agencies and to discover alternative rules or regulatory requirements in order to achieve promising results. The recent growth of Middle Eastern and North African countries (MENA) and their commercial banking system has increased the need of paying widespread attention to this region of the world. This thesis crucially identifies, and estimates, the robust determinants of credit ratings for MENA countries and their commercial banks, incorporating a set of bank level accounting and financial risk factors, as well as country-specific characteristics, including indicators for regulatory, supervision, legal and economic environments. The research contributes, firstly, to the theoretical literature on credit ratings industry by reviewing extant methodologies specifically as they apply to banks and sovereign countries. Secondly, it conducts a systematic, cross-country empirical investigation using panel data econometric methodology for the purpose of estimating MENA countries sovereign and bank credit rating models. Thirdly, it provides tangible and statistically significant evidence on the different factors that determines the estimation of credit ratings and influencing bank's risk. The extant literature reviewed serves as a basis to achieve and develop the research aim, objectives and hypotheses of the thesis. The research then constructs an appropriate panel dataset from different sources, containing bank-level and country-level information for a sample of 108 commercial banks covering 13 MENA countries over the period 2000 - 2012. The methodological framework for estimating credit rating models (linear regression, logit and probit) is also reviewed and the procedures for panel data estimation are implemented using the econometric package STATA (version 13). All relevant data are drawn from public sources including Reuters, Bankscope, IMF and the World Bank. Using the random effects ordered probit and logit methodologies to estimate both sovereign (country) and bank level credit ratings models for the MENA countries, the evidence shows that real GDP growth, capital requirements, restrictions on banking activities and control of corruption all contribute negatively to the sovereign ratings. Furthermore, internal management and organisational requirements is considered as an additional regulatory factor not studied in previous research. The statistically significant and inverse relationship of the latter is considered an important and interesting outcome of MENA countries’ sovereign ratings. On the other hand, GDP per capita, investment (as a percentage of GDP), political stability, government effectiveness and the rule of law all reveal significant and positive impact on the sovereign credit ratings. In general, this research finds that improved macroeconomic conditions are correlated with higher ratings, while greater reserve regulations are correlated with lower ratings. The study also does find the significance of governance and regulatory variables plays a key role into the final credit rating. With regard to the impact on banks’ ratings, the results show that higher return on average assets and equity, larger bank size, more restrictions on bank activities, as well as higher official disciplinary power and higher standards of internal management, will yield higher credit ratings. Apart from having direct and positive impact on banks credit ratings, these variables are important for examining the risk-sharing incentives in MENA countries’ banks. In contrast, the estimation results indicate that net interest margin, net loans to deposits, liquid assets to deposits, capital requirements, deposit insurance scheme, liquidity requirements, unemployment rate and government effectiveness have an inverse and negative impact on banks ratings. In general, this study also finds various financial, macroeconomic, and regulatory effects on banks’ credit ratings. To a much lesser extent than government ratings, various macroeconomic variables also helped predict banks’ ratings, including real GDP growth and the unemployment rate. The thesis concludes by arguing that the combined use of financial and non-financial factors for estimating credit ratings models supports the relevant hypotheses examined and adds value to all stakeholders in improving and obtaining a better quality of credit ratings. This study also demonstrates that a diversity of bank-level and country-level factors influence the MENA sovereign and bank ratings differently, implying that policy makers, regulators alongside rating agencies should distinguish the different environmental factors between nations before any judgment and issuance can be model of the ratings. To conclude, there is no study which exclusively investigates credit rating models for the MENA region exploiting the richness of the data and methodology employed, and the current research aims to fill this gap.

The purpose of this research is to examine whether U.S. banks that announced material operational risk loss (oprisk loss) events can still enjoy a lower cost of capital. I use the bank's credit rating as a proxy for the cost of debt capital, and the actual oprisk loss amounts announced by publicly traded U.S. banks for $10 million and over during the period 1998 to 2012 compiled from my own database. I also investigate whether the type of oprisk loss event and business line in which the loss event was incurred matter to credit rating agencies. I perform additional analysis to determine whether a downgrade in a bank's credit rating associated with the announcement of a material oprisk loss amount impacts the bank's reputation. This study focuses on the U.S. banking industry because of the increased market and regulatory scrutiny of oprisk losses; especially during the financial crisis of 2008 to 2010. The logistic analysis shows that banks' announcement of material oprisk loss amount is associated with a decline in credit ratings. The findings did not support the position that the type of loss event and business line in which the loss event was incurred matter to credit rating agencies. The results for the event study show that a downgrade in a bank's credit rating associated with an announcement of a material loss amount has a robust, statistically significant negative stock market reaction. Furthermore, the results reveal that the losses in market value significantly exceed the announced loss amounts associated with credit rating downgrades, indicating reputational loss to the banks. This research was limited to announcements of material oprisk loss amounts by U.S. banks publicly traded on major U.S. stock exchanges. Investigating the impact of announcements of material oprisk loss amounts by financial institutions publicly listed on major stock exchanges worldwide provides an avenue for future research. This study contributes to the literature on operational risk and the cost of debt capital as reflected in credit ratings by providing empirical evidence of the impact of oprisk losses on credit ratings of U.S. banks.

Issuing environmentally aligned green bonds has become an increasingly popular way to raise capital for green investments during the last decade. This thesis explores potential pricing differences between green and conventional bonds, known as the green premium, on the Swedish secondary bond market. Prior green bond research is inconclusive regarding the direction, size and even existence of such a premium. By creating a sample of 50 matched pairs of green and conventional bonds, we show an average positive green premium of 10 bps on the Swedish market, indicating that Swedish green bonds trade at higher yields than their conventional counterparts. We also study whether the size of the green premium is affected by credit ratings and third-party green certification but find no evidence of such effects. Overall, the results from this thesis add to current green bond research by showing a positive green premium, but the lack of shown effects from credit ratings and green certification indicate that further study is needed to fully understand the pricing mechanisms of green bonds.