Perceptions of postretirement benefit obligations by bond rating analysts

This research examines the measurement and impounding of alternative measures of a corporation's other postretirement benefits obligation (OPEBs) by an important segment of the capital markets. The Kaplan and Urwitz (1979) model is used as a benchmark from which to assess the importance of an added OPEB variable in the bond rating process. Using the corporate bond rating as the dependent variable, multiple measures of the OPEB obligation are inserted individually as an added independent variable into an N-chotomous probit model. The results for 1987 and 1988 indicate that measures calculated from publicly available information produce highly significant results. The developed postretirement liability measures are found to provide relevant and material information regarding the risk level of a firm's bonds as represented by its bond rating. This insight concerning the additional risk represented by a firm's postretirement benefits is beyond that supplied by the firm's pension information. This suggests that the additional investor default risk attributed to a firm's OPEB can be reasonably proxied by data found in the company's annual report footnote disclosures.     

What Is in a Municipal Bond Rating?

Studies on the determinants of municipal bond ratings contain two conspicuous patterns: the use of financial accounting variables and the application of discriminant analysis to them. Over 70 different financial accounting variables have been specified, leading to different findings across the studies. In addition, discriminant analysis has been applied in these studies without correcting for violations of its underlying assumptions. Akaike's information criterion and Lachenbruch's U method are used to show how a probit model specified with economic base diversification, economic expansion, and fiscal management variables may be an improvement over the application of discriminant analysis to financial accounting variables in the determination of a triple A bond rating.     

The ordered qualitative model for credit rating transitions

Information on the expected changes in credit quality of obligors is contained in credit migration matrices which trace out the movements of firms across ratings categories in a given period of time and in a given group of bond issuers. The rating matrices provided by Moody's, Standard & Poor's and Fitch became crucial inputs to many applications, including the assessment of risk on corporate credit portfolios (CreditVar) and credit derivatives pricing. We propose a factor probit model for modeling and prediction of credit rating matrices that are assumed to be stochastic and driven by a latent factor. The filtered latent factor path reveals the effect of the economic cycle on corporate credit ratings, and provides evidence in support of the PIT (point-in-time) rating philosophy. The factor probit model also yields the estimates of cross-sectional correlations in rating transitions that are documented empirically but not fully accounted for in the literature and in the regulatory rules established by the Basle Committee.     

A re-examination of the impact of credit ratings and economic factors on state bond yields

In this article we re-examine the impact of credit ratings and economic factors on state bond yields using a two-step model. In the first step, we adopt an ordered probit technique to obtain consistent estimates of state bond default risk. In the second step, we estimate state bond risk premiums using a regression analysis with a categorized risk variable obtained from the first step. Similar to Terza (1987) and Hsiao (1983), the model involves a categorized ordinal explanatory (rating) variable. However, our two-step model deals with a case where category thresholds are unknown and dependent on economic factors. The model provides consistent estimates for the effects of ratings and economic factors on state bond yields. Contrary to previous findings, we find that state bond yields are mainly affected by fundamental economic variables.     

“投资人付费”vs.“发行人付费”:谁的信用评级质量更高？

本文选择2011-2015年被中债资信覆盖的发债A股上市公司作为主要研究对象,比较了“投资人付费”与“发行人付费”模式下的评级质量高低。研究发现:(1)与“发行人付费”评级相比,采用“投资人付费”模式的中债资信所作评级显著更低。(2)与“发行人付费”评级相比,当采用“投资人付费”模式的中债资信所作评级越低时,发行人未来盈利能力越差、预期违约风险越高,投资者要求的风险补偿也越高,这表明“投资人付费”模式下的信用评级质量更高。(3)“发行人付费”模式的评级结果可以在一定程度上反映公司的内部私有信息,但由于同时存在独立性缺失问题,“发行人付费”模式的信用评级质量仍然不如“投资人付费”模式的信用评级质量,这说明独立性对于评级机构尤其重要。     

多重信用评级与债券融资成本——来自中国债券市场的经验证据

本文揭示了多重信用评级的信息生产机制和信用认证机制,排除了“信用评级购买”假说对于多重评级动机的解释,研究发现:多重信用评级有利于降低债券融资成本;相对于不一致的多重信用评级,一致的多重信用评级更有利于降低债券融资成本。在多重信用评级中,相对于评级机构均为“发行人付费”模式,兼有“投资者付费”模式的信用评级更有利于降低债券融资成本。此外,当多重信用评级的评级意见不一致时,平均评级的信息含量最高,即综合不同信用评级所包含的多种信息比任何单一信用评级更加具有信息含量。本研究为我国债券市场双评级制度提供了理论和经验证据的支持,有助于完善多元化信用评级制度和债券市场监管制度,并提示发债企业可以通过多重信用评级向市场传递更多和更具效度的评级信息,以弥补单一信用评级的信息不足和评级结果失准,减少投资者决策的不确定性,从而降低债券融资成本。     

Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms

Ratings issued by credit rating agencies (CRAs) play an important role in the global financial environment. Among other issues, past studies have explored the potential for predicting these ratings using a variety of explanatory factors and modeling approaches. This paper describes a multi-criteria classification approach that combines accounting data with a structural default prediction model in order to obtain improved predictions and test the incremental information that a structural model provides in this context. Empirical results are presented for a panel data set of European listed firms during the period 2002–2012. The analysis indicates that a distance-to-default measure obtained from a structural model adds significant information compared to popular financial ratios. Nevertheless, its power is considerably weakened when market capitalization is also considered. The robustness of the results is examined over time and under different rating category specifications.     

信用评级变动能反映企业真实财务信息吗?——基于财务重述的视角

本文以2007-2018年拥有主体信用评级的A股上市公司为研究对象,利用企业财务错报在未来被重述这一场景,检验主体信用评级变动能否反映企业真实财务信息。研究发现,评级机构在发债企业财务错报年显著下调了主体信用评级,而在重述公告发布年没有上述现象,这表明主体信用评级下调反映了企业的真实财务信息。在控制内生性影响后,结论仍然成立。进一步研究发现,发债企业当期财务错报涉及盈余时,主体信用评级被下调的幅度更大,说明评级机构更加关注与盈余相关的财务信息。机制分析表明,评级机构维护自身声誉是主体信用评级变动能够反映企业真实财务信息的主要机制。此外,主体信用评级被下调还导致了资本市场投资者的负面反应。本文的研究结果为主体信用评级变动反映企业真实财务信息提供了直接的证据支持,揭示了主体信用评级的信息含量,也对理解中国情境下评级机构调整主体信用评级的行为动机提供参考。     

The Information Content of Australian Managed Fund Ratings

Abstract:  We examine the information content of managed fund ratings for Australian retail investors. Because fund ratings, premised on a quantitative-qualitative model, are highly transitory, we question whether investors formulate their investment decisions with respect to changes in ratings and whether ratings, in turn, react to fund flows. We find that information regarding fund flows can be obtained from ratings, and that rating changes can have far-reaching effects. Investors flock to newly upgraded funds while they penalize those that have been downgraded by withdrawing funds. Investors are constantly anticipating ratings revisions, particularly downgrades, and we attribute this phenomenon to the role of qualitative factors in the ratings.     

Default and Recovery Risk Dependencies in a Simple Credit Risk Model

This paper provides evidence for the relationship between credit quality, recovery rate, and correlation. The paper finds that rating grade, rating shift, and macroeconomic factors provide a highly significant explanation for default risk and recovery risk of US bond issues. The empirical data suggest that default and recovery processes are highly correlated. Therefore, a joint approach is required for estimating time‐varying default probabilities and recovery rates that are conditional on default. This paper develops and applies such a model.     

The Declining Credit Quality of U.S. Corporate Debt: Myth or Reality?

In recent years, the number of downgrades in corporate bond ratings has exceeded the number of upgrades, leading some to conclude that the credit quality of U.S. corporate debt has declined. However, an alternative explanation of this apparent decline in credit quality is that the rating agencies are now using more stringent standards in assigning ratings. An ordered probit analysis of a panel of firms from 1978 through 1995 suggests that rating standards have indeed become more stringent, implying that at least part of the downward trend in ratings is the result of changing standards.     

Estimation of rating class transition probabilities with incomplete data

This paper shows that the well known “duration” and “cohort” methods for estimating transition probabilities of external bond ratings are not suitable for internal rating data. More precisely, the duration method cannot and the cohort method should not be used in connection with banks’ ratings generated from internal models. Structural differences within the borrower monitoring process of banks and rating agencies are responsible for this result. A Maximum Likelihood (ML) estimation procedure, which accounts for the peculiarities of internal bank ratings, is introduced and applied to data from a German bank. The empirical results indicate that the differences between cohort and ML transition matrices are both, statistically and economically significant. Furthermore, evidence of rating reversals, business cycle dependent transition probabilities and on the factors which determine the borrower monitoring intensity of banks is provided.     

MULTIVARIATE ANALYSIS OF CORPORATE BOND RATINGS AND INDUSTRY CLASSIFICATIONS

Previous statistical models designed to predict bond ratings use a variety of financial data and ratios; however, the possible effect of the industry in which the firms operate is generally disregarded. Although the exact bond rating process used by the rating agencies is not known, industry analysis is an important component of the process. This study evaluates the use of industry-specific multiple discriminant functions for predicting bond ratings. The results support using such a technique.     

The impact of meeting or beating analysts' operating cash flow forecasts on a firm's cost of debt

Extant literature provides conflicting results with respect to the usefulness and accuracy of analysts' operating cash flow forecasts. Our study empirically examines the importance and influence of meeting or beating analysts' operating cash flow forecasts on a firm's cost of debt. Results indicate that firms meeting/beating analysts' cash flow forecasts have higher initial bond ratings as well as lower initial bond yields. Additionally, based upon an analysis of rating changes, firms meeting or beating cash flow forecasts have a higher probability of receiving a debt rating upgrade and a lower probability of a ratings downgrade compared to firms missing cash flow forecasts. A direct comparison of the importance of meeting/beating cash flow versus earnings benchmarks indicates that debt market participants appear to incrementally value both types of forecasts, and contrary to selected equity market findings, neither forecast subsumes the other for debt market participants.     

Bond Yields, Ratings, and Financial Information: Evidence from Public Utility Issues

This study investigates the relative roles of bond ratings and financial information in the setting of bond yields. Structural equation modeling techniques are used to learn whether ratings determine yields or whether both ratings and yields are determined by a concurrent set of economic and financial factors. Tests of alternative structural model configurations provide evidence regarding the associations between bond ratings, financial information, and bond yields. Both ratings and financial information are found to play an important role in determining bond yields. In addition, yields are consistent with the higher rating in cases of split ratings.     

信用评级在中国债券市场的影响力

信用评级能够为市场提供风险信息从而增强市场对企业的约束,因此对资本市场的健康发展起着重要的作用。在中国,评级市场刚刚起步。本文利用2007至2009年间发行的企业债数据,用实证的方法研究本土信用评级在债券市场上的影响力。我们建立真实利息成本(TIC)回归模型,以信用评级和其他相关因素对债券的真实利息成本进行计量回归,分析信用评级在一级市场中对债券发行成本的影响。我们发现债券评级和主体评级分别对发行成本具有解释力,但债券评级对发行成本的影响力大于主体评级。     

Predicting Bank Failures: A Comparison of On- and Off-Site Monitoring Systems

On-site examinations are regulators' primary tool for monitoring the financial condition of federally insured depository institutions. In this paper, we assess the speed with which the information content of the supervisory rating assigned during bank exams—the CAMEL rating—decays. This is an important issue because cost and regulatory burden considerations often cause CAMEL ratings to be assigned relatively infrequently. As a benchmark for information content, we use econometric forecasts of bank failures generated by applying a probit model to publicly available accounting data. When compared with all CAMEL ratings available at a given point in time, the econometric forecasts provide a more accurate indication of failure. Further analysis reveals that this overall finding reflects the tendency for a CAMEL rating's information content to deteriorate noticeably beginning in the second or third quarter after the rating initially was assigned.     

INFORMATION OPACITY AND FITCH BOND RATINGS

We examine the marginal impact of Fitch ratings on the at‐issuance yields of industrial and utility bonds rated by Moody's and Standard & Poor's. We find that Fitch ratings reduce the yield premiums on information‐opaque bonds by about 30%, or 15 basis points. The finding is robust even when a Fitch rating exactly equals the two major ratings or their average. The findings suggest that Fitch ratings are not redundant but bring additional information to investors. Increased competition in the rating industry enhances the information efficiency of the bond market, and the existence of smaller rating agencies is economically justified.     

Meeting new peers: The effects of Morningstar category reassignment on fund flows and star ratings

We investigate how the reassignment of a fund's Morningstar category affects fund flow and Morningstar star rating. We find that funds assigned to a different category gain positive abnormal flows and this effect is significant mainly for high-rated funds. Category reassignment does not improve a fund's star rating on average, and flows are less responsive to a star-rating change if the rating change is likely to be driven by category reassignment. The positive abnormal flows captured by high-rated funds after category reassignment are consistent with a visibility story: some investors filter funds by Morningstar category and star rating, and category reassignment makes a fund more visible to a new group of investors if the fund is highly rated. In contrast, a low-rated fund is likely to be selected only by investors who do not refer to the fund's Morningstar information and, hence, gains little visibility from category reassignment. We also find evidence that more sophisticated investors are more likely to consider not only fund rating but also fund category when evaluating fund performance.     

Do bond credit ratings lead to excess comovement?

We investigate whether non-fundamental comovement results from investors using credit ratings to group assets into different “styles”. We find that bonds that join a new rating class start comoving more with the bonds in this class, even when fundamental factors suggest otherwise. We show that this comovement effect varies according to the nature of the bond considered, and the modalities of the rating action. Downgrades have a larger impact than upgrades, and rating reviews matter as much as actual movements. Finally, rating changes between grades BBB and BB, which lead bonds to be reclassified as either “high-yield” or “investment grade” assets, seem to be of particular importance.