case study on credit rating

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A Practical Use Case of Textual Data Analysis on Credit Ratings Research

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• 6 Oct, 2021
• Author Arsene Lui CFA/FRM
• Theme Custom
• Segment Investment Management
• Tags Global RatingsXpress on Xpressfeed Credit Analysis Textual Data

This article is written and published by S&P Global Market Intelligence, a division independent from S&P Global Ratings. Lowercase nomenclature is used to differentiate S&P Global Market Intelligence credit scores from the credit ratings issued by S&P Global Ratings.

Introduction

In a previous article , we explored the ability to extract forward-looking and credit-sensitive keywords from S&P Global Ratings’ research reports via the machine-readable RatingsXpress®: Research dataset. In this article, we demonstrate the creation of a credit signal that can be used by risk managers to anticipate potential rating moves.

In our research, we collected over 65,000 S&P Global Ratings’ research reports (including Full Report, Summary Report, and Research Update) of corporates in the period from 2013 to the latest year, via RatingXpress. Since the machine-readable research reports were divided into sections and tagged, we easily obtained the parts we were interested in: Downside Scenario and Upside Scenario sections.

With the textual data extracted from the downside/upside scenario sections, we first used regular expressions to filter the sentences which have numbers or percentages. Then, we applied Natural Language Processing (NLP) algorithms to identify, separate, and extract the financial keywords, signs, and numerical thresholds. Table 1 below shows a few examples of the algorithm outputs.

Table 1: Examples of NLP algorithm outputs from downside/upside scenario sections

Source: S&P Global Market Intelligence. As of August 20, 2021. For illustrative purposes only.

We randomly selected 1,000 research reports, manually tagged the Downside/Upside Scenario sections, and compared the manual outputs against the algorithm outputs. The algorithm achieves an accuracy of >95% for numerical thresholds and >80% for financial keywords.

Financial Ratio Thresholds for Rating Downgrade/Upgrade

In the S&P Global Ratings methodology for rating corporate industrial companies and utilities, two core financial ratios and five supplementary financial ratios are defined, with corresponding benchmark ranges, for the assessment of a company’s financial risk. [1]

In the Outlook sections of rating reports, we can find the downside/upside scenarios under which a rating action may be triggered if the specific financial ratios breach pre-defined thresholds. The top five most frequently mentioned financial ratios in downside/upside scenarios are:

Table 2: Top five financial ratios in downside/upside scenario sections

The list of extracted financial ratios are, in general, consistent with the financial risk ratios listed in the rating criteria. When we look into the relative frequencies of extracted threshold values, we can see some threshold values, which are mentioned in the upside/downside scenarios, but are not explicitly defined in the rating criteria. An example is given in Table 3.

Table 3: Threshold values of FFO/Debt extracted from downside/upside scenario sections

A Credit Signal for Potential Rating Downgrade

In the second part of our empirical study, we defined a credit signal using the financial thresholds extracted from the downside scenarios and the annual financial statements from S&P CreditStats dataset. We tested the signal on the two core financial ratios (i.e., FFO/Debt and Debt/EBITDA) individually and assessed its performance in predicting rating downgrades over a one-year time-horizon.

To construct the credit signal, we compared the threshold values in the downside scenarios of each rating reports against the latest financial ratios of the corresponding company as at the report publication date. The financial ratios and thresholds were normalized, and the differences were computed. The sign of a credit signal indicates whether a financial ratio has breached its downside scenario threshold or not, while the magnitude of a credit signal indicates the normalized distance of a financial ratio from its downside scenario threshold. For example, a positive credit signal for FFO/Debt (Debt/EBITDA) means the actual financial ratio is higher (lower) than the downside scenario threshold. Our hypothesis was that the higher the value of credit signal (i.e., the actual financial ratio is more distant from the threshold mentioned in the report), the less likely a downgrade to happen. This was tested by using the area under curve (AUC) measure of receiver operating characteristic (ROC) curve.

The ROC measures of the credit signal are just slightly above 0.5, [2] which indicates the distance of a historical financial ratio from its downside scenario threshold is not a good predictive classifier of rating downgrade. This may not be surprising because historical financial performance is not necessarily predictive of future financial performance.

Then we tested the usefulness of the credit signal as a surveillance indicator. We examined the annual financial statements published after the rating reports to see if the newer financial ratios breach the original downside scenario thresholds or not. We grouped our samples into 4 groups according to the signs of credit signal as at the report publication date (T) and after the report publication date (T+1). The percentages of samples downgraded within one year are reported in Table 4. The significant differences between the downgrade percentages of positive and negative credit signals (after the report publication) show the credit signal is a good surveillance indicator.

Table 4: Percentage of samples downgraded

Additional Considerations

Our empirical study demonstrates the use of NLP techniques on rating reports for the automatic generation of credit signals. The results show that the credit signals are suitable for credit surveillance purposes. Obviously, the financial thresholds mentioned in the downside/upside scenario sections are just part of the many considerations for rating migrations. There are lot of useful information, which may require manual extraction or advanced NLP techniques, to enhance our surveillance signal.

[1] S&P Global Ratings, “General Criteria: Corporate Methodology”, May 27, 2021.

[2] A random binary classifier has a ROC measure of 0.5.

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• Arsene Lui CFA/FRM
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Open Access

Peer-reviewed

Research Article

Modelling sovereign credit ratings and assessing the impartiality: A case study of China

Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation College of Economics and Management, Taiyuan University of Technology, Taiyuan, China

• Published: September 8, 2023
• https://doi.org/10.1371/journal.pone.0289321
• Reader Comments

The post-COVID-19 era presents a looming threat of global debt, elevating concerns regarding sovereign credit ratings worldwide. This study develops a new index system, divides the rating variables into long- and short-term factors, performs rating fitting and prediction, and investigates the fairness of China and relevant countries. Our findings reveal that sovereign credit ratings have a deterrent effect on the global financial market due to the ceiling effect and quasi-public goods characteristics. A high and stable credit rating demands long-term enhancements in economic fundamentals, budget balances, external surpluses, and overall solvency. Concurrently, effective short-term debt management strategies, including reduction, repayment, and swaps, are essential. Moreover, we introduce the concept of a "rating gap" to assess rating fairness, revealing both undervaluation and overvaluation among countries. Notably, China’s sovereign rating was underestimated between 2009 and 2011 and overestimated between 2013 and 2016. These findings underscore the criticality of government vigilance in monitoring sovereign debt and credit ratings to navigate potential post-COVID-19 sovereign debt crises.

Citation: Su M (2023) Modelling sovereign credit ratings and assessing the impartiality: A case study of China. PLoS ONE 18(9): e0289321. https://doi.org/10.1371/journal.pone.0289321

Editor: Burcu Kapar, American University in Dubai, UNITED ARAB EMIRATES

Received: February 18, 2023; Accepted: July 17, 2023; Published: September 8, 2023

Copyright: © 2023 Min Su. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data involved in the study can be obtained from two sources. Part of the data related to sovereign credit ratings can be obtained from S&P Global Ratings, https://disclosure.spglobal.com/sri/ . Another set of data, including macroeconomic, monetary, and fiscal data, can be obtained from the World Bank Open Data, https://data.worldbank.org.cn/ .

Funding: This work was supported by the general project of the National Social Science Foundation of China, grant number 21BJY125. Min Su is the host of this project. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The COVID-19 pandemic brought about various changes, and one significant consequence was the substantial increase in global public debt. This surge in debt is poised to be one of the pandemic’s most burdensome legacies. In this high-debt environment, debtors will grapple with the delicate task of balancing multiple constraints, akin to walking on a knife’s edge, and the financial system’s fragility will be heightened. Particular attention should be given to sovereign credit ratings among the challenges arising from mounting debt.

In this context, it is important to note that China’s SCR experienced a downgrade from Aa3/AA- to A1/A+ in 2017 by two leading credit rating agencies, Moody’s and Standard & Poor’s. It followed 2016’s "negative" outlook and was Moody’s second downgrade in China since 1988. Despite the limited impact on China’s bond, stock, and exchange markets, the downgrading may harm market perceptions of China’s economy, especially if the downgrading trend continues to cross the critical point , where it transcends from a mere investment-related concern to a speculative one. Such a juncture could yield a considerably amplified impact on the financial markets compared to other variations in credit ratings. The decrease in a country’s SCR also has implications for the cost of borrowing for the government, banks, and businesses.

The downgrade of China’s SCR was the result of a complex interplay between domestic and international factors. Domestically, China’s high public debt and leverage ratio were cited as contributing factors to the downgrade. Additionally, the profound and complex international environment at the time also played a significant role. The global economy has undergone significant changes since the outbreak of the US subprime crisis and the European debt crisis, leading to an increase in sovereign debt problems and defaults on debt in some developed countries. These events have had a significant impact not only on these countries’ economies but also on the global economy.

Since Moody’s initial assignment of China’s sovereign credit rating in 1988, the two other leading credit rating agencies have also assigned credit ratings to China. Over the past three decades, China’s economy has undergone tremendous growth and development, as indicated by various economic indicators such as GDP per capita, total savings, total foreign exchange reserves, and total foreign debt. In turn, this growth and development have corresponded with a generally upward trend in SCR, with an improvement of four notches from an initial Baa1 to an Aa3 rating. The correlation between SCR and GDP per capita in China is depicted in Fig 1 , which highlights the positive relationship between a strong economy and a higher SCR.

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Note: 1. This figure takes a linear numerical transformation of the rating letters and adjusts the positive/negative outlook to the 0.5 level. 2. The left axis of the figure is per capita GDP (unit: RMB), and the right axis is the rating values of the three leading agencies. 3. Source: World bank and Wind database.

https://doi.org/10.1371/journal.pone.0289321.g001

In the aftermath of the US subprime crisis, the economic indicators associated with China’s SCR have experienced a decline. Despite an overall increase in economic output, the growth rate has diminished, with GDP growth rates of 6.7% and 6.9% in 2016 and 2017, respectively, falling below the sustained 7% growth rate since 1991. Additionally, China’s foreign exchange reserves, the largest in the world for many years, have begun to drop, reaching $3.13 trillion at the end of 2017, a decrease of approximately $670 billion from its peak of $3.8 trillion. Furthermore, the fiscal deficit has increased yearly, surpassing the 3% threshold in 2015 and staying above that level in 2016 and 2017. These factors present significant challenges to China’s future SCR.

As countries embrace greater financial openness, the significance of sovereign ratings becomes increasingly apparent due to the rising demand for external financing. Furthermore, in the aftermath of the COVID-19 pandemic, concerns surrounding sovereign debt have become more prominent, highlighting the need to monitor and study sovereign credit ratings. The current characteristics of research in this field involve extensive data collection, the establishment of a scientific and accurate evaluation system, as well as the development of models, all of which cannot be accomplished overnight. Additionally, despite their significant impact, sovereign ratings do not directly generate economic income for rating agencies. These factors collectively define the nature of research in this field.

We conduct empirical analyses to examine the factors that influence SCRs and assess the impartiality of the three largest CRAs. Our research objective is to develop insights into how to mitigate the potential risks associated with a possible sovereign debt crisis in the future. The COVID-19 pandemic has resulted in a significant increase in government debt, which presents a challenge for the global economy in the post-pandemic era. As the governments withdraw supportive policies, the debt situation will deteriorate, making the financial system more fragile. In this context, it is imperative to pay close attention to SCRs, as they are a crucial indicator of a country’s creditworthiness. While the three leading CRAs did not widely downgrade SCRs during the pandemic [ 1 ], this may not continue.

This study makes fivefold contributions, with the first three being quantitative in nature, while the latter two are qualitative. Firstly, we enhance the conventional rating index system by introducing new indicators (outlined in Table 2 ), which are unique and not commonly used in other research papers. These additions, inspired by S&P’s rating system and incorporating qualitative factors, refine the evaluation framework. Secondly, utilizing the latest rating metrics, we categorize rating factors into short-term and long-term dimensions, building upon Alfonso’s framework. Our approach yields distinct findings and allows for targeted recommendations to address the debt crisis from both perspectives. Thirdly, we introduce a novel "rating gap" method to assess the impartiality of ratings, addressing a literature gap and contributing to the field of rating fairness. Moreover, it provides a basis for nations to implement measures safeguarding their rights. Fourthly, we assert the quasi-public goods attributes of sovereign credit ratings, a seldom discussed aspect. As these ratings at the national level do not directly generate income for rating agencies, smaller agencies hesitate to engage in this domain. Therefore, recognizing the long-term, public, and foundational nature of sovereign ratings, governments should increase financial support for scientific research in this field. Lastly, this study highlights the "critical point" effect of sovereign credit ratings. The critical point denotes the transition from an investment-grade to a speculative-grade level, which exerts a more significant impact on the financial market compared to other rating changes. Overall, these contributions enhance our understanding of sovereign credit ratings, provide practical recommendations, shed light on rating fairness, and highlight the quasi-public goods nature of SCRs.

The remainder of the paper is structured as follows: Section 2 provides a literature review; Section 3 describes the selection of indicators and the setting of the model (Methodologies); Section 4 reports empirical results; Section 5 evaluates and discusses the predictive power of the model; Section 6 investigates the rating fairness of China and related countries; Section 7 conducts the robustness checks; and Section 8 is the conclusion and policy implications.

2. Literature review and theoretical analysis

The existing literature on Sovereign Credit Ratings (SCRs) is abundant and encompasses a broad range of topics, including: (1) the determinants of SCRs; (2) the impacts of SCRs on macroeconomics and finance, particularly on bond, stock, exchange, and financial derivatives (e.g., CDS); (3) the fairness of SCRs; (4) the relationship between SCR and bank rating; (5) the impact of the "Sovereign ceilings" rule; (6) the comparative study of SCRs on EMEs versus developed countries; (7) the difference of rating methodologies of three leading CRAs; (8) the impact of political risks and uncertainty on ratings; (9) sovereign shadow ratings; (10) the relationship between SCRs and corporate strategic decisions. In this study, we have selected three relevant topics to be covered in the literature review.

2.1. The deterrent force of sovereign ratings

Due to information asymmetry and the requirement for knowledge when making investment and portfolio decisions on the global asset market, investors rely heavily on CRA reports [ 2 , 3 ]. The three leading CRAs (S&P, Moody’s, and Fitch) have a monopoly on the SCR industry, accounting for more than 95% of the market. SCR reports will influence investment behavior and market stability [ 4 , 5 ]. In most cases, bond prices are set by investors based on the bonds’ credit ratings [ 6 , 7 ]. Higher rated bonds typically have lower financing costs than lower rated bonds.

More importantly, SCR is a benchmark for a nation’s credit system, especially for the external financing rating, with sovereign credit ceiling effects. The concept of sovereign credit ceiling entails that the credit ratings assigned to all entities within a country, including banks, corporations, and local governments, are constrained by the sovereign credit rating. Consequently, any downgrade in the sovereign rating inevitably triggers cascading downgrades in the ratings of other entities, thus establishing a pervasive ceiling and benchmark for the entirety of the credit rating landscape [ 8 – 11 ]. In most cases, a local government, bank, or business can only have a lower credit rating than the SCR of that country. Consequently, the change of SCRs will affect bond, stock, and foreign exchange markets. A downgrade of a nation’s SCR may lead to a domino effect of negative events and impacts the creditworthiness and funding costs of all rated entities in the nation.

Despite never doing so before, in 1997, S&P announced it would begin to change its standards and give some firms in dollarized economies higher ratings than their respective sovereigns. Has this breached the sovereign ceiling norm and exceeded it on a large scale? Based on data from developed and emerging economies throughout 1995–2009, Borensztein and Cowan [ 9 ] discovered that SCRs continue to affect the company’s ratings, particularly for nations with strong capital controls and high political risks. The three major CRAs have only marginally violated this guideline. The SCR is still employed as a key benchmark and factor in establishing the enterprise’s rating. Additionally, the impact on financing costs is not equal when switching from low to high SCRs; for example, moving from the n-1st to the nth grade may not be similar to moving from the n-3rd to the n-2nd grade. Under the actual ratings, the Critical Point effect is typically triggered during the transition from Investment to Speculative Grade, which significantly impacts the market more than the general migration.

2.2. The fairness of sovereign ratings

Fairness is a critical aspect of determining a country’s sovereign creditworthiness. In recent years, concerns have been raised about the reliability of SCRs, with increasing public scrutiny regarding their impartiality and objectivity [ 12 – 14 ]. The immense impact of SCRs on financial markets has resulted in ongoing debates about their fairness [ 15 , 16 ]. When a rating agency announces a downgrade of a country, it can become a focal point of public opinion and face accusations and criticism from the downgraded nation. Furthermore, CRAs often cite protecting trade secrets as a reason for not disclosing their practices, exacerbating the issue. The downgrading of a country’s SCR is highly publicized and widely debated, attracting attention and scrutiny worldwide.

The dependability of CRAs in evaluating sovereign creditworthiness has been a subject of intense scrutiny. Some studies have pointed to the pro-cyclical nature of CRAs and inability to predict financial crises. For example, Ferri et al. [ 17 ] found that the CRAs not only failed to anticipate the onset of the East Asian financial crisis but also downgraded countries in its aftermath, exacerbating the situation. Host et al. [ 18 ] similarly explored how CRAs responded to the Eurozone debt crisis and concluded that they faced criticism for not giving market danger signals prior to the crisis. Using linear panel data and ordered probit methods, Fuchs [ 19 ] found that Moody’s and Fitch assign the United States a higher rating than it deserves. He also suggested the need for stronger oversight of SCR agencies to prevent the three leading agencies from monopolizing the market’s excess revenue. In contrast, Mora [ 20 ] argued that ratings are sticky, not pro-cyclical, and that CRAs provide independent assessments of a country’s likelihood of default. Kaminsky and Schmukler [ 21 ] emphasized that investors should not be overly reliant on these assessments and that the market should not be over-interpreted.

The results of sovereign credit rating processes have been a much-debated topic in the literature. However, a closer examination of the evidence suggests that the situation may be more complex than previously thought. Doluca [ 22 ] investigated the potential biases in the SCR process and found no significant evidence of Home Country Bias or Profit Maximization influencing the ratings. On the other hand, Reputation concerns were found to be a significant factor in the ratings, being more important than economic interests [ 23 , 24 ]. To maintain their reputation and credibility, Standard & Poor’s, Moody’s, and Fitch have declared that they will not engage in any form of manipulation that could affect a country’s economic growth, such as artificially raising or lowering a country’s credit rating [ 25 ]. This is further evidenced by Standard & Poor’s downgrade of the credit rating of the U.S., its home country, in 2011. However, there is ongoing debate as to whether these agencies follow scientific procedures in their work. For instance, it has been noted that the three agencies generally award higher scores to developed countries in their ratings [ 26 ]. This approach is understandable, as the solvency of high-income countries is generally more robust than that of low-income countries.

The ongoing disputes surrounding sovereign credit ratings highlight the need for an objective criterion in the rating process. To achieve this, it is important to understand the metrics and models utilized in determining SCRs. If this process is publicly disclosed, the fairness of SCRs will be comparatively solved, and it will be simple to determine whether countries are overstated or understated.

2.3. Determinants of sovereign rating

Transparency and disclosure of the factors used in determining sovereign credit ratings are crucial in reducing controversies and increasing the credibility of the ratings [ 13 , 27 ]. However, the models utilized by the three major CRAs are considered confidential information, leading to disparities in their ratings [ 28 ]. According to the indicators and ratings released by the three leading CRAs, scholars have found that the construction of a rating model primarily consists of several factors, including rating indicators, number of countries, measurement methodologies, and rating conversion methods, Etc. Despite the limited number of published indicators available for fitting the rating models, these factors are critical in determining the SCRs.

In the existing literature, Cantor and Packer [ 29 ] were pioneers in the study of sovereign credit ratings and proposed a method known as the CP model, which identified six significant criteria in determining SCRs. Subsequently, several academics have continued to improve the evaluation methods and procedures of SCRs from various perspectives: Firstly, the range of explanatory variables has been broadened. The six explanatory variables in the initial CP model were found to be limited, and some were insignificant in subsequent regression tests. Some studies [ 18 , 30 ] substituted the CP model’s total foreign debt indicator with the current account surplus and foreign exchange reserve, thereby improving the model’s goodness of fit. Other studies [ 31 , 32 ] have chosen different explanatory variables, but with the common trend of increased quantity and improved quality. To avoid collinearity and overlap, Mellios et al. [ 33 ] used principal component analysis (PCA) to optimize the explanatory variables with the addition of more variables to the equation.

Secondly, in the process of modeling credit ratings, the selection and preprocessing of dependent variables is crucial. To begin with, the credit rating notations, such as "A, B, C, D," and "+, -" symbols, must be transformed into numerical scales [ 34 , 35 ]. There are two primary conversion methods, linear and nonlinear. The linear method equalizes the intervals between each rating, while the nonlinear method does not [ 36 , 37 ]. Thirdly, econometric methods are refined and optimized. Given that the credit rating data is both cross-sectional and time-series, most studies utilize panel data regression techniques [ 38 , 39 ]. On the other hand, since credit rating results are expressed in an ordered discrete form, the multi-ordered selection model is a suitable alternative for this type of research.

In recent years, several new factors, including ESGs, machine learning, and artificial intelligence, have been incorporated into the field of sovereign credit ratings (SCRs), driving its development. The significance of Environmental, Social, and Governance (ESG) factors in determining sovereign credit ratings has been discussed. Pineau et al. [ 40 ] emphasize the importance of integrating ESG considerations into the assessment of countries’ creditworthiness. Similarly, Karaman [ 41 ] investigates the impact of countries’ ESG ratings on sovereign credit default swaps, offering empirical evidence from OECD countries between 2008 and 2019. The study highlights the influence of ESG ratings on credit risk and underscores the relevance of ESG factors in sovereign credit analysis. Overes and van der Wel [ 42 ] employ machine learning techniques to model sovereign credit ratings, assessing their accuracy and determining the driving factors behind these ratings.

In conclusion, while research in this field has continuously grown and evolved, deterrence, fairness, and determinants remain three interrelated and critical aspects of analyzing sovereign credit ratings. Firstly, due to the deterrent effect of SCRs on financial markets, market participants pay closer attention to the ratings and strive for their fairness. Secondly, to assess the objectivity of the rating system, it is essential to examine the factors that determine a nation’s credit rating. Improving the transparency of the rating process and objectively disclosing the variables that influence SCRs can mitigate disputes and enhance the credibility of the rating results.

Despite the extensive literature available on sovereign credit ratings, there are still several gaps in our current understanding of this field. These gaps encompass issues such as the fairness of sovereign credit ratings (SCRs), the inherent lag in these ratings, and the challenges posed by the quasi-public goods nature of SCRs. However, one primary challenge or bottleneck in sovereign ratings lies in the construction of the rating model. The moment the rating model is disclosed, the rating results gain more credibility. The impact and influence of sovereign ratings on financial markets have been extensively examined by researchers. However, these studies have significantly lagged the needs of financial practice, particularly in terms of adopting new theories and methodologies. Accurately revealing the rating model would be advantageous in protecting the interests of specific countries, uncovering the truth, and countering intentional distortions.

To address these gaps, this study aims to contribute to the literature by presenting novel and comprehensive indicators, as well as utilizing more accurate measurement techniques. The purpose of this study is to improve the existing literature on SCRs and provide policy recommendations in the context of the ongoing COVID-19 pandemic and its potential impact on triggering a global sovereign debt crisis.

3. Indicators selection and model setting

3.1. dependent variables.

The dependent variable in this study is the SCRs level assigned by credit rating agencies. The rating level is expressed as a combination of letters and symbols (e.g., "AAA/Aaa" and "+ -"), with AAA/Aaa indicating the highest rating and D representing the lowest default level. On average, CRAs assign more than 20 levels of ratings, with the majority falling in the moderate range. It is commonly recognized that ratings above "BBB-" are considered "investment grades," while those below "BBB-" are considered "speculative grades." Additionally, CRAs often place a country on a separate list when anticipating a change in its SCR. For instance, Standard & Poor’s classifies such positions as "Credit Watch" and assigns them a "positive," "negative," or "stable" rating, which is then officially published one to three years later.

In the realm of financial modeling, it is not feasible to model letter symbols as they are. Hence, a conversion process is necessary to represent these symbols in numerical form. This conversion can be accomplished through either objective or subjective techniques. In this study, we adopt a more objective linear transformation method, which assigns numerical values ranging from 20 to 1 to represent the credit ratings, with 20 being assigned to the highest rating of AAA/Aaa and 1 to the lowest rating of D ( Table 1 ). This approach offers a more objective and systematic representation of credit ratings for financial modeling purposes.

https://doi.org/10.1371/journal.pone.0289321.t001

3.2. Explanatory variables

The selection of explanatory variables for sovereign credit ratings in this study is guided by two perspectives: ability and willingness . A comprehensive literature review was conducted, encompassing seminal works such as Cantor and Packer [ 29 ] and Anfoson [ 43 ], as well as 51 indicators from Standard & Poor’s. Based on this, a screening methodology was developed, categorizing variables into six distinct categories that capture different aspects of sovereign ratings: macroeconomics, monetary borrowing, government balance, the balance of payments, external balance sheet, and central government debt and borrowing. By eliminating duplicate and missing data, 22 explanatory variables were identified as the most relevant, with 19 about debt repayment capacity and the remaining three focusing on willingness (detailed information in Table 2 ).

https://doi.org/10.1371/journal.pone.0289321.t002

Our sample includes data from 73 nations and spans from 2009 to 2016, which covers the European debt crisis. The choice of this time period was driven by two factors: first, the European debt crisis represents the most recent severe sovereign debt crisis; and second, the observed parallels between the debt position during this period and the current global economic situation post-COVID-19. The data used in this study was obtained from the World Bank, IMF, and S&P databases.

In the credit rating process, the assessment of the borrower’s willingness to repay is of utmost importance, but it is often challenging to accurately gauge. Some previous studies have neglected the willingness aspect and focus solely on the ability to repay. However, it is widely acknowledged that not all entities with the capacity to repay their debts will fulfill their obligations. In the past, some governments have demonstrated the ability to repay but lacked the willingness to do so. To address this issue, our study employs three proxies for measuring repayment willingness: default history, government efficiency, and regulatory quality. Default history provides insight into an individual’s or government’s pattern of debt compliance and likelihood of defaulting in the future. Meanwhile, government effectiveness and regulatory quality serve as indicators of a nation’s level of civilization, legalization, and adherence to international standards.

3.3. Sovereign credit rating model

In this study, we employed two primary modeling approaches: panel linear regression and ordered probit regression. The linear regression model, being a conventional approach, is suitable for capturing pertinent information from both cross-sectional and time-series perspectives, given the inclusion of such data in sovereign credit ratings. Model selection among the three potential models was performed through F-tests and Hausmann tests. Additionally, considering that credit ratings are expressed in an ordered discrete form, ordered probability models prove suitable for this research. These models encompass the general ordered probability approach as well as the panel ordered probability approach. By utilizing ordered probability models, the determinants of sovereign ratings exhibit enhanced explanatory significance, overcoming the limitations of assuming equal rating distances in linear models and enabling estimation of the rating curve’s shape. However, it is important to note that ordered probability models possess certain drawbacks, including more demanding modeling conditions and potentially poorer regression results.

3.3.1. Linear regression model.

Where i and t are the subscripts for country and year, respectively. R it denotes the transformation of rating variable, X it is the vector of explanatory variables, and α i represents the individual effect of country i . The disturbance term μ it is further assumed to be independent of the cross-section and time-series variables. This model provides a basic framework for analyzing the relationship between the credit rating and a set of relevant explanatory variables. The estimated coefficients can be used to assess the impact of each explanatory variable on the credit rating. The implementation of this panel linear regression model will provide insights into the determinants of sovereign credit ratings.

Eq ( 1 ) can be estimated using three methods: pooled Ordinary Least Squares (OLS), fixed effects, or random effects. When E ( α i | X it ) = 0, it implies no correlation between the national effect and the explanatory variable, and thus the estimators for all three methods are equivalent. According to Afonso [ 43 ], the random effect model is the most efficient estimation technique, followed by the fixed and mixed effect models. However, in practice, the country effect α i is often correlated with the explanatory variable X it , leading to inconsistent estimators when using the random or mixed effect models. As a result, the fixed effects model is often considered the most effective, as some researchers have demonstrated. Additionally, since the dependent variable (the credit rating) is typically sticky and changes little over time, the limited variation in the credit rating over time results in a lack of explanatory power.

Given the above concerns, we adopt Wooldridge’s [ 44 ] method as a solution. Our objective is to establish a clear association between the section error and explanatory variables. Wooldridge’s method provides a rigorous framework for addressing this issue, and has been widely accepted in the academic community as a reliable approach. The objective of this approach is to establish a robust relationship between the two variables.

The coefficients in Eq ( 4 ) provide insight into the factors affecting sovereign ratings. The term δ , defined as the sum of η and β , represents the overall impact of the rating on the long-term stability of the economy. The component β specifically highlights the short-term effects, offering important information for policymakers in formulating effective strategies to improve sovereign ratings. These distinctions provide a valuable tool for considering both short- and long-term perspectives in enhancing the stability of the economy.

3.3.2. Ordered probit model.

Where F in Eq ( 7 ) is the residual term’s cumulative distribution function (CDF), and depending on how the residual term distribution differs, we can use the probit, logit, and extreme value estimating techniques.

4. Empirical results

4.1. Panel linear regression results

4.1.1. the pooled ols model..

Table 3 presents the mixed-effect model estimations. The unrestricted model (1) and restricted model (2) demonstrate strong explanatory power, as indicated by the R 2 values of 0.97 and 0.94, respectively. In the unrestricted model (1), 31 out of 40 explanatory variables are found to be significant. Meanwhile, in the restricted model (2), 29 variables are significant, except for the short-term influence of per capita GDP. The coefficients of the short and long-term variables for the two models differ. Some variables display significant long-term effects, while their short-term effects are relatively insignificant. GDP per capita and real GDP growth exhibit a positive and significant impact on sovereign credit ratings in both the short and long run. This implies a direct association between higher levels of GDP per capita and stronger real GDP growth with elevated ratings. The underlying rationale for this relationship can be attributed to several factors. Firstly, a higher GDP per capita serves as an indicator of a country’s economic well-being and income level, reflecting its capacity to fulfill debt obligations. Secondly, stronger real GDP growth signifies a growing economy with increased revenue potential, reducing the likelihood of default. These favorable economic indicators enhance investor confidence and contribute to the assignment of higher ratings to countries demonstrating robust economic performance. Inflation is positively related to the rating in the short term but significantly lowers the rating in the long run. This finding aligns with economic theories suggesting that moderate inflation promotes growth, while prolonged high inflation is detrimental to the economy. In the short term, the positive correlation between inflation and the rating can be attributed to the beneficial effects of moderate inflation on economic activity, including stimulating demand and reducing the real burden of debt. However, in the long run, persistent high inflation has adverse consequences such as eroding purchasing power, distorting price signals, and undermining investor confidence. These factors contribute to a significant decline in the rating. This finding underscores the importance of maintaining inflation at moderate levels for long-term economic stability and creditworthiness.

https://doi.org/10.1371/journal.pone.0289321.t003

In conclusion, the Pooled OLS model, which combines cross-sectional data with time series data, is a suitable method for situations with limited data. The model allows for the efficient utilization of different categories of data and helps overcome limitations in data availability, leading to improved results and analysis.

4.1.2. Panel data random effect regression.

Despite some advantages, the above basic mixed-effect regression does not allow further study of national effects. Therefore, we applied the Hausman test to the panel data to determine the suitability of the random effects model. The results of the Hausman test, as presented in Table 4 , indicated that the null hypothesis was accepted, making the random effects model an appropriate choice for further analysis. Table 4 presents the results of the random effects model. The unrestricted model (1) has an R 2 of 0.59, whereas the restricted model (2) has an R 2 of 0.54, indicating that the restricted model’s explanatory power was reduced after the removal of insignificant variables. In the unrestricted model (1), 33 out of the 40 explanatory variables were significant, whereas in the restricted model (2), all variables were significant. The results show that GDP per capita has a small and close to zero coefficient, suggesting that its impact on credit rating has weakened. The coefficient for GDP growth is positive, indicating that an increase in GDP growth by 1% leads to an increase in the credit rating by 0.47 points in the long run. The coefficient for the unemployment rate is negative, suggesting that a higher unemployment rate is detrimental to the credit rating. Additionally, the long- and short-term effects of inflation on credit rating are distinct. In the short term, inflation has a positive effect on credit rating, but in the long term, it has a negative effect. This is consistent with the economic viewpoint that moderate inflation can benefit economic growth, while consistently high inflation is detrimental.

https://doi.org/10.1371/journal.pone.0289321.t004

4.2. Ordered probit regression results

The ordered probit model overcomes the linear model’s assumption of equalization of the rating distance and estimates the rating curve’s form, providing additional explanatory implications for the factors influencing SCRs.

4.2.1. General ordered probit model.

According to our findings (see Table 5 ), the ordered-probit model has more significant explanatory variables than the mixed-effect model, and some of the variables’ symbols and coefficients are different from previous model. Some macroeconomic indicators, such as investment and savings, do not significantly impact the rating. In all government expenditure and debt indicators, only the government balance has a short-term impact, while the remaining variables have a substantial long-term impact on ratings. The useable reserve/GDP and narrow net external debt/CARs are insignificant among the external income and spending indicators. Whether or not a country is a developed economy has no significant effect on ratings, suggesting that the influence of this indicator is diminishing and confirming the results of earlier models.

https://doi.org/10.1371/journal.pone.0289321.t005

Panel B of Table 5 provides an estimation of 17 cutoff points for the model. The distance between the cutoffs is not uniform, and notably, there is a significant increase in the threshold between the BBB- and BBB ratings. The distance between the levels varies, with a decrease and subsequent increase in the portion above the investment level, suggesting that achieving a higher rating is increasingly difficult.

4.2.2. Panel order probit model.

Table 6 presents the results of the panel ordered probit model. Compared to the random effects model discussed in Section 4.1.2, this model includes three additional insignificant explanatory variables and some differences in symbols and coefficients. Our results show that the variables of investment, GDP growth rate, long-term unemployment, and short-term savings do not have a significant impact on ratings. Regarding the impact of government spending and debt variables on sovereign ratings, we find that a balanced budget and a medium debt/GDP ratio are favorable for sovereign ratings, as reflected in the sign and significance of the long-run coefficients of the variables in the model. This suggests that countries with disciplined fiscal policies, characterized by a balanced budget and a manageable debt-to-GDP ratio, are more likely to receive higher ratings. A balanced budget signifies responsible financial management, where a country’s revenue matches its expenditure. Similarly, a manageable debt-to-GDP ratio implies that the country’s debt burden is sustainable and not excessively burdensome. These factors contribute to higher sovereign ratings as they reflect stability and the ability to meet financial obligations. Overall, the findings highlight the importance of maintaining fiscal discipline in order to enhance a country’s rating in the sovereign market. The variables of current account income/GDP, narrow net external debt/CARs, and short-term external debt by remaining maturity/CARs have a short-term effect on ratings. These variables are crucial in capturing key aspects of a country’s external position. A surplus in the current account signifies a robust economic performance and the ability to meet external obligations, as it indicates that a country is earning more from exports and investments than it is spending on imports and interest payments. Furthermore, a lower level of narrow net external debt relative to CARs indicates reduced vulnerability to external shocks and a lower likelihood of default. Similarly, a reduced reliance on short-term debt suggests a decreased risk of liquidity pressures. Consequently, maintaining a favorable external position characterized by a surplus in the current account, an adequate level of foreign reserves, and a diminished reliance on short-term debt is beneficial for sovereign credit ratings. We also find that the country’s status as a developed economy remains insignificant, while the history of default has a higher negative impact on ratings. On the other hand, government efficiency and regulatory quality have a significant positive correlation with the rating.

https://doi.org/10.1371/journal.pone.0289321.t006

Panel B of Table 6 provides an estimation of 17 cut points. Our results indicate that the distance between the levels is shorter at lower ratings but increases as the rating improves, suggesting that achieving a higher rating becomes increasingly challenging.

5. Evaluation and discussion of model predictive power

5.1. evaluation of model predictive power.

In line with previous literature [ 44 , 45 ], we assess the predictive power of several models by comparing the fitted to the actual values. We utilize the estimated coefficients obtained in Section 4 and substitute each country’s data into the relevant model, as illustrated in Table 7 . In addition to the four types of eight models discussed earlier, this table includes nine models, including the differentiation of panel random-effects models into two models, one with and one without the national effect ( V i ).

https://doi.org/10.1371/journal.pone.0289321.t007

Table 7 displays the results of an assessment of the predictive power of several models. The panel RE-ordered probit model has the highest prediction accuracy, at 82.2%. When allowing for a single error notch, the accuracy of the prediction increases to 93.9%. The panel random-effect model with the national effect V i has the second-highest predictive power, with an accuracy of 81.1%. With the allowance for a single error notch, the accuracy of prediction can reach 89.4%. In comparison, the model with the weakest predictive performance is the Pooled OLS, with an accuracy rate of approximately 40%.

5.2. Discussion

The existing literature on the predictiveness of econometric models presents a mixed picture. Afonso [ 43 ] compares the panel random-effects model with the ordered probit model and finds that the former exhibits superior predictive potential, with an accuracy rate of 70% compared to the latter’s 35%. Conversely, Erdem and Varli [ 45 ] report that the panel mixed-effect model was the most predictive among the five models they evaluated, achieving a 61% correct rate within a rating notch. Our empirical investigation, however, reveals that the panel mixed-effect model has a lower correct rate of 42%, which diverges from the results obtained in previous studies.

The conflicting views in the literature on model predictiveness could be due to two potential causes. Firstly, it could be attributed to the characteristics of the explanatory variables. The rating model typically employs data from over 100 nations, incorporating more than 10 explanatory factors and 10 years of data. Our paper includes more than 20 explanatory variables, which increases the likelihood of complex calculation errors in the presence of missing data [ 45 ]. Secondly, the conflicting views could be related to the dependent variables, given that the rating model has many classified variables, typically over 16 categories and 20 rating levels. When there are too many categories, the coefficients calculated using the panel-ordered probit approach may need to be more accurate, and using a Binary Choice model may enhance the predictive power if the number of classifications is reduced to consolidate all categories into investment and speculative level categories. However, this would result in significant information loss.

6. Exploring rating fairness: China and related countries

Where ΔRatings i refers to the rating gap, Ratings act , i represents the actual rating values, and Ratings fit , i denotes the model fitted values. A positive rating gap indicates that the actual values are higher than the fitted values, implying a potential for overestimation. Conversely, a negative rating gap suggests that the actual values are lower than the fitted values, indicating a possibility for underestimation. This conclusion is supported by the static and trailing nature of static credit ratings, which are updated gradually in response to changing economic conditions. It is therefore important to consider the potential impact of the rating gap in evaluating credit risk.

We selected three distinct groupings of nations for the purpose of forecasting Sovereign Credit Ratings:

• Greater China, which encompasses the mainland of China, Hong Kong, and Taiwan.
• The G7, a representative group of developed nations consisting of Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States. The aggregate Gross Domestic Product (GDP) of these nations accounts for approximately two-thirds of the world economy.
• BRICS, consisting of Brazil, China, Russia, India, and South Africa, representing emerging market economies.

The selection of these three groupings provides a comprehensive representation of various economic conditions, ranging from developed to emerging markets. This allows for a thorough examination of the differing factors that influence the Sovereign Credit Ratings within each grouping.

In the Panel A of Fig 2 , the forecast for the Standard and Poor’s Credit Rating (SCR) in Greater China is demonstrated. Our analysis shows that there was a substantial difference in the SCR between 2009 and 2011 in mainland China. The calculated rating gap was approximately -0.8, implying that the SCR was underestimated by almost one notch during that time period. However, the situation improved from 2013 to 2016 as the rating gap shifted from negative to positive, reaching a peak of 1.3 points in 2016. This may suggest that the SCR was overestimated by one to two notches at this stage. In contrast, the credit rating of Taiwan showed a positive trend until 2015 but has since decreased, which can be attributed to the economic slowdown during those years. The credit rating of Hong Kong, on the other hand, remained constant despite significant changes in its socio-economic environment in recent years. It is worth noting that credit ratings are generally inflexible, and rating agencies provide outlooks before making revisions. If Hong Kong’s economy does not improve, the credit rating is likely to be modified.

https://doi.org/10.1371/journal.pone.0289321.g002

Panel B of Fig 2 displays the trend in Standard and Poor’s Credit Ratings (SCRs) for the G7 developed countries. Our findings show that the SCRs of the U.S., the U.K., France, and Italy increased over the study period, despite the impact of the subprime mortgage crisis and the European debt crisis. This can be attributed to the improvement in their economies, leading to a tendency for the fitted values of the SCRs to surpass their actual values. Except for Canada, the remaining six countries in the G7 group showed a negative rating gap that continued to widen, with the highest negative gap being observed in the U.S. This suggests that the U.S. may return to a higher SCR in the future. Panel C of Fig 2 presents the SCRs of Emerging Market Economies (EMEs), represented by the BRICS countries. The results indicate that the SCRs of the BRICS countries are relatively stable and have only declined slightly. The SCR of Russia showed a downward trend, with both the actual and fitted values trending downward. India’s trend was mildly ascending and relatively stable, whereas Brazil and South Africa exhibited modest rating gaps, indicating that the actual and predicted values were close.

7. Robustness test

Robustness tests were conducted to enhance the reliability and validity of the findings, as mentioned above, across diverse settings. The study employed several approaches to ensure robustness. Firstly, this research focused on the top 20 developing countries globally, aiming to demonstrate the robustness of the findings across a broad range of study subjects. The selection of countries was based on the Standard & Poor’s Top-20 Emerging Markets (by outstanding debt) list, comprising 18 countries. From this list, four countries, namely Brazil, China, India, and South Africa, which were previously examined in Section 6, were excluded. This resulted in a final set of 14 countries for analysis. The countries included in the sample for the robustness test are Mexico, Indonesia, Saudi Arabia, Türkiye, Poland, Argentina, Thailand, Malaysia, Philippines, Egypt, Colombia, Qatar, and Hungary. Notably, all these 14 countries, except for Pakistan, which holds a CCC+ rating, possess speculative-grade ratings of BBB- or higher, indicating a relatively favorable credit standing. For further information, the official website for Standard & Poor’s ratings can be accessed at https://disclosure.spglobal.com/sri/.Secondly , the study period was adjusted to span from 2017 to 2021, thereby corroborating the temporal robustness of the results. Lastly, methodological improvements were implemented, including adopting a first-order lagged form for the explanatory variables. This modification aimed to mitigate endogeneity issues arising from reverse causality and address concerns regarding rating lag.

Furthermore, this study examines the fairness of ratings for the 14 countries mentioned above, as illustrated in Fig 3 . Due to the distinct circumstances of these countries, they exhibit individual characteristics with a need for commonalities. Certain countries, such as Turkey and Thailand, demonstrate significant fluctuations. Additionally, with the onset of the COVID-19 pandemic in 2020, the overall ratings of these countries tend to be overestimated.

https://doi.org/10.1371/journal.pone.0289321.g003

The robustness test results, as presented in Table 8 and Fig 3 , exhibit consistency with the baseline results in terms of the magnitude of coefficients and the signs of key explanatory variables. This evidence supports the robustness and consistency of the conclusions drawn from this study, from various perspectives such as across different time periods, samples, and methodologies.

https://doi.org/10.1371/journal.pone.0289321.t008

8. Conclusion and policy implications

The international community is concerned about the impartiality and determinants of sovereign credit ratings. we developed a new index system, divided the rating variables into long-term and short-term factors, performed rating fitting and prediction, and investigated the fairness of China and relevant countries.

Our findings are summarized as follows: Firstly, in the aftermath of the COVID-19 pandemic, the SCR indicator system has become increasingly crucial in adapting to the evolving global economic landscape. As such, we have sought to enhance the traditional SCR indicator system by incorporating a variety of new explanatory variables. This undertaking has been informed by the latest academic research and S&P rating indicators, resulting in the development of 22 comprehensive index systems covering macroeconomics, sovereign debt, and the international economy. Our efforts have refined and improved the rating model, enhancing its effectiveness and accuracy. Secondly, from the policymaker’s perspective, we separate the determinants of SCRs into long-term and short-term factors. Our research discovered that economic growth, unemployment, inflation, savings, government effectiveness, regulatory quality, and default history have long-term effects on ratings. In contrast, government revenue and expenditure and external debt have short-term effects on ratings. Thirdly, we proposed the "rating gap" as a means of evaluating rating fairness, with a particular focus on the G7, BRICS, and Greater China regions. Our analysis shows that China’s sovereign credit rating was undervalued by nearly one notch from 2009 to 2011 and overvalued by one to two notches from 2013 to 2016. In contrast, the G7 countries exhibited relatively strong ratings after 2013, with the United States’ performance being particularly noteworthy. Russia’s position among the BRICS nations has seen a decline, while India’s rating has remained relatively stable with a slight upward trend. The rating gaps in Brazil and South Africa are modest, with no significant deviation from the actual values.

Based on the above main research conclusions, the following policy recommendations were put forward: Firstly, in response to a sovereign debt crisis, we offer the government short- and long-term recommendations. To fundamentally enhance the sovereign credit rating and maintain a high rating in the long run, the country must prioritize improvement in the long-term determinants of its financial operations, such as increasing fiscal balance and foreign trade surplus, thus fortifying the overall solvency of the nation. In the short term, the government must also adopt effective measures for emergency debt management, including debt reduction, repayment, and swapping. Secondly, given the ceiling effect and indirect profitability, sovereign credit ratings exhibit characteristics of quasi-public goods. As a result, small to medium-sized rating firms are often reluctant to undertake national-level ratings, as they do not typically yield clear profits. However, as a country’s financial openness expands, its dependence on external funding also increases, elevating the significance of its sovereign credit rating. Furthermore, conducting a sovereign credit rating project necessitates collecting a substantial amount of data and developing an exact evaluation model, which cannot be achieved rapidly. Given the long-term, fundamental, and public nature of sovereign credit ratings, governments should offer increased financial and policy support for related research initiatives. Thirdly, emerging market economies have faced challenges in their development of credit ratings. The process started relatively late and has progressed slowly, leaving many domestic firms that finance abroad in these nations vulnerable to the ratings assigned by the three largest CRAs. Many EMEs, including China, must establish and enhance their SCR agencies and research organizations to address this challenge. This will increase their rating discourse power, strengthen their presence in international markets, and resist potential manipulation by international oligopolies. Furthermore, domestic rating agencies require a greater voice in international markets and a change in international rating agency legislation. Lastly, the COVID-19 pandemic has brought about significant shifts in the global political economy, presenting the potential for adverse conditions in sovereign debt. Governments should engage in ongoing monitoring and early warning strategies to mitigate these risks. EMEs must consider modifying the currency structure of their loans and claims and diversifying their reserve moderately. Concerning China, the promotion of the internationalization of the RMB and the diversification of foreign reserve investments are crucial steps in strengthening its financial stability. Additionally, establishing and effectively managing sovereign investment funds are crucial for mitigating financial risks. Following the downgrades by Moody’s and S&P, China must remain vigilant and implement appropriate policies to prevent the recurrence of severe financial disasters, including sovereign credit downgrades.

Despite providing empirical evidence for responding to a potential sovereign debt crisis in the post-COVID-19 era, this study has several limitations. The first limitation is the availability of data. This study collected from 73 countries from 2009 to 2016, with several indicators lacking data for specific years. In future research, more extensive data periods and an increase in the national data utilized would be beneficial for testing the validity of the results, especially regarding intra-sample grouping and inter-group heterogeneity. Secondly, the design of the study has room for improvement. For instance, principal component analysis (PCA) could be implemented instead of the manual empirical selection. Furthermore, using a dynamic panel data model, such as SYS-GMM, would be more effective in addressing potential issues such as sequential correlation, heteroscedasticity, and endogeneity, as opposed to using a static model. Thirdly, the Chinese and global economies are undergoing unprecedented transformations. Future research should consider incorporating elements of this dynamic transformation to gain a deeper understanding of the complex relationships between sovereign credit risk and economic, political, and social concerns.

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Credit rating announcement and bond liquidity: the case of emerging bond markets

Journal of Economics, Finance and Administrative Science

ISSN : 2218-0648

Article publication date: 16 February 2022

Issue publication date: 12 July 2022

This study examines the effect of the informational content of local credit rating announcements in emerging markets on the liquidity of their bond markets. This study analyses the liquidity of bonds in various emerging bond markets using a sample of nine countries: Argentina, Mexico, Peru, Hungary, Poland, Spain, Turkey, Hong Kong and Greece. The sample includes daily data on sovereign bonds that go from July 2009 to July 2017. The main focus is on the period before and after the sovereign debt crisis. This study notes that the bond liquidity is affected due to the sign of the rating granted by the rating agencies for each country.

Design/methodology/approach

This study aims to question the sources of liquidity problem of sovereign bonds issued by the emerging countries. The study’s database consists of daily data of all nine emerging countries for the period from July 2009 to July 2017. Panel data were collected from the Datastream database.

This study first directly tests the information content of bond ratings announcements and their effect on bond market liquidity. Next, the impact of rating changes on sovereign bond liquidity around the rating announcements is studied. Rating changes can affect sovereign bond's price, trading and liquidity around the announcement date. In particular the rating changes that move the bonds out of the investment grade category can elicit selling pressure or even fire sale of the fallen angels.

Originality/value

This research aims to present data on the prices of sovereign bonds that react to changes in credit rating by studying the price movements around the announcement of changes in credit rating. The literature is very rich in studies on credit rating changes on stocks and corporate bonds, but this study is perhaps the first attempt on sovereign bonds.

• Sovereign bond market
• Credit ratings

Saadaoui, A. , Elammari, A. and Kriaa, M. (2022), "Credit rating announcement and bond liquidity: the case of emerging bond markets", Journal of Economics, Finance and Administrative Science , Vol. 27 No. 53, pp. 86-104. https://doi.org/10.1108/JEFAS-08-2020-0314

Emerald Publishing Limited

Copyright © 2022, Amir Saadaoui, Anis Elammari and Mohamed Kriaa

Published in Journal of Economics, Finance and Administrative Science . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The information extracted from the changed notes has been an appropriate issue in recent years. The ratings disclosed by the rating agencies do not contain complete information, being able to help investors. Unlike these agencies, the informational content is disclosed while transferring assessments without providing specific details to the public. Thus, the agencies' actions will have some effects on the market returns and asset prices.

Fridson and Sterling (2006) show that the credit rating agencies will summarize public information and that the changes in bond ratings do not transmit any new information to the market.

Recent studies have shown that negative rating announcements, especially reviews about decommissioning and downgrades, do not actually reflect information relevant to the pricing of shares, bonds and credit default swap (CDS) spreads ( Chodnicka-Jaworska, 2017 ; Wengner et al. , 2015 ). Overall, most of this literature estimates the price and/or returns. In this paper, we have moved further away from the traditional analysis of prices by looking into the effects of the rating agency's announcement on the liquidity of the emerging bond markets.

During recent years, sovereign ratings have been in the interest of research in the sovereign euro-zone, including Greece and Spain have experienced a drop in their ratings by Standard & Poors (S&P) in August 2011 ( Andreasen and Valenzuela, 2016 ). The IMF, in its report of 2010, showed that the sovereign credit risk is one of the main obstacles to global economic stability. Consistent with this, Duggar et al. (2009) have found that 71% of business failures and under-rated sovereigns in the emerging markets have been improving during the sovereign crises.

Recent literature has shown that the changes in the sovereign rating and outlook/watch signals affect equity and emerging debt markets, respectively ( Rusike and Alagidede, 2021 ). Indeed, it has also shown that these effects are not only significant at the national level since sovereign ratings are assigned to affect markets in other countries. In particular, the bad news has a negative effect that the new sovereign rating causes a significant impact on the equity and bond markets of other countries. However, the good news has a negligible effect (e.g. Böninghausen and Zabel, 2015 ). Banier and Hirsch (2010) have shown that these instruments were valued for providing a considerable economic benefit. Vazza et al. (2005) have analysed the behaviour of ratings of assets issued by companies and found that these issues have a higher probability of a rating change in the direction indicated.

In fact, Alsakka and Ap Gwilym (2009) have analysed the dynamics of the sovereign ratings for six rating agencies in the emerging economies, including status monitoring. They observed that the assets placed under surveillance have a higher probability of a rating change in the direction indicated by the status within 12 months after being placed on the watch list. Several studies prove that the sovereign ratings represent approximations of the ability and willingness of governments to highly regard their financial conditions. They also substantiate that these ratings capture the dynamics of capital markets and influence the capital cost.

Brooks et al. (2004) point out that the degradations of sovereign asset ratings have a large negative effect on the stock markets. Gande and Parsley (2005) and Ferreira and Gama (2007) reveal that the degradations of sovereign bond ratings – mainly during times of crisis – aggregate stock returns of other countries, especially in the emerging economies and neighbouring countries, while the progressions of ratings have a ridiculous impact. Ismailescu and Kazemi (2010) and Sovbetov and Saka (2018) have studied the relationship between CDS of the emerging markets and changes in sovereign notation and found that the spread of CDS responds to changes in sovereign ratings. These authors have also evinced that the positive signals add new information on markets, against the negative news which is expected, and therefore reflected in CDS spreads. These results are contradictory to those found in the previous studies having stated that the negative signals of negative ratings have more effect on CDS spreads. For instance, Norden and Weber (2004) and Salvador (2017) mention that the negative signals greatly expand CDS spreads for issuers of investment grade, while the strong positive signals significantly expand CDS spreads for speculative-grade issuers.

Similarly, Sovbetov and Saka (2018) have studied the interaction between credit risk swaps and domestic stock indexes. It has shown that any increase in the CDS entails a reduction in the stock market index in the short and long terms, respectively, and vice versa. In fact, he also found that the stock market index and the CDS deviate too much and converge towards a long-term equilibrium at a moderate monthly rate.

Owing to the role of credit rating agencies in the sovereign debt crisis in the financial phase (2007–2008) of the current crisis, as in the Asian crisis of 1997, many criticisms are made due to the sovereign debt crisis, notably from investors affected by the performance of certain financial assets having the best ratings. For structured products, lots of tranches rated AAA has thus experienced large losses because of the shortcomings of the methodology of some agencies. Since the beginning of the public debt crisis, a variety of questions about the practices of these agencies have been raised. They focus mainly on the amplifying effects of their decisions, and even on their legitimacy to record sovereign debts ( Alsakka et al. , 2014 ).

Several proposals have been made to address the above-mentioned problems, especially in Europe, where the sovereign debt crisis has been aggravated by the accentuation of ratings on certain economic trends such as the indebtedness of Member States.

This study first directly tests the information content of bond rating announcements and their effect on bond market liquidity.

Next, it investigates the impact of rating changes on sovereign bond liquidity around the rating announcements. Rating changes can affect the sovereign bond price, trading and liquidity around the announcement date. In particular, the rating changes that move the bonds out of the investment-grade category can elicit selling pressure or even fire sales of the fallen angels.

To meet our objectives, our study expresses the impact of changes in sovereign credit ratings on the liquidity of financial markets in emerging economies, mainly during the sovereign debt crisis.

The rest of the paper is organized as follows. Section 2 reviews the relevant literature. Section 3 discusses the data and methodology. Section 4 presents the empirical results, and section 5 concludes the paper.

2. Literature review

Generally, the role of rating agencies, as expressed by the new notes, could be a factor in the price volatility of sovereign bonds ( Voorhees, 2011 ). The rating is considered as a new information transmission channel on the market since it reduces the information asymmetry so that asset prices in the market are moving in the direction of the appreciation expressed ( Pagano and Volpin, 2010 ; Deb et al. , 2011 ; Freixas and Laux, 2012 ).

Recent literature has shown that the role of rating agencies is to provide information on the market through the publication of a note attributed to the situation of an investor (e.g. Deb et al. , 2011 ; De Haan and Amtenbrink, 2011 ; Schroeter, 2011 ).

Actually, Deb et al. (2011) demonstrated that the information extracted from the assigned ratings could affect the future behaviour of the sovereign issuer whose choice of economic and financial policies can be either confirmed or amended according to the rating, whether positive or negative. The area of credit rating has increased in line with the regulations of supply and demand ( He and Xiong, 2012 ). According to De Haan and Amtenbrink (2011) and Schroeter (2011) , this process has addressed more than 150 rating agencies that are widespread all over the world. They think that about 140 agencies are in one country and/or in one oriented sector, while around five to ten ones based in Japan, the USA and Canada provide new assessments whether as a country or industry. The global market is dominated by three major agencies, which are S&P, Moody's and Fitch. These agencies are dominant in the market with an estimated share of 40% each to S&P, Moody's and Fitch 15%. In addition, the number of issuers rated by S&P increased from 1,386 in 1981 to 5,860 in 2009, with a significant increase in revenues. The emergence of new notes is carried away by the strong investor demand in the financial markets for information about issuers of shares and bonds. Schroeter (2011) shows that the novella score gives new information about the issuer of the bond, and the likelihood that the issuer can face these engagements.

The ratings of sovereign debt by the rating agencies are considered as assessments of the default probability of the public debt. Indeed, these agencies use economic and political factors to make a qualitative and quantitative assessment of the asset. Through this process, the change in the rating sovereign debt may give new information on the financial situation of a country, requiring considerable externalities to the private sector of the country, which can appropriate the investors to keep the assets.

2.1 The effect of the sovereign rating actions on the financial market

Sovereign ratings are the ratings of the ability and willingness of governments to meet their financial markets ( Gusdinar and Koesrindartoto, 2014 ). These ratings affect the dynamics of capital markets and affect the cost of capital. Brooks et al. (2004) show that the degradation of ratings has a negative effect on the stock markets.

Gande and Parsley (2005) and Ferreira and Gamma (2007) show that the sovereign downgrades have injected new valuable information for the spreads of sovereign bonds and aggregate stock returns of other countries, especially in the emerging economies, countries' neighbours and during times of crisis, for against the improvements they are immaterial.

In their study on the swap market, Ismailescu and Kazemi (2010) show positive signals that had better affected the price of CDS. However, Norden and Weber (2004) have found the results quite inconsistent. These authors find that negative scoring signals have more effect on CDS spreads. However, they find that negative signals significantly expand CDS spreads for issuers of investment grade, and significantly narrower CDS spreads positive announcements for issuers of investment grade.

Kim and Wu (2008) attempted to examine the role of attracting sovereign ratings S&P international capital. They find that the new sovereign rating is an important incentive for the three types of international capital flows. Similarly, they find a great evolution of the bond market after the improvement of sovereign ratings. Borensztein et al. (2013) show that the sovereign ratings generally represent a measure of a country's credit risk.

A large literature has shown that the ratings issued by credit rating agencies have an impact on different segments of the financial system ( Canh et al. , 2021 ). Indeed, previous studies have found very significant relationships between the bad news provided by the agencies and stock market returns, currency, bond spreads, CDS spreads and volatility in asset prices, while the good news has an insignificant or limited impact (e.g. Kaminsky and Schumkler, 2002 ; Afonso et al. , 2012 ; Alsakka and Ap Gwilym, 2012 ).

Afonso et al. (2014) show that in moments the stock yields, currencies and sovereign bonds are highly correlated with the new credit quotes, particularly bad news. Several studies have shown that the sovereign ratings granted by the rating agencies generate cross-border effects ( Gande and Parsley, 2005 ; Ferreira and Gama, 2007 ; De Santis, 2012 ), and others have found that there is a correlation of cross-country in the stock and bond markets ( Christopher et al. , 2012 ).

Investors on the market must distinguish between the credit ratings granted by the different credit rating agencies. However, Afonso et al. (2012) find in their study comparing the rating agencies, the obligations of credit spreads react significantly with S&P announcements, while for Moody's and Fitch advertisements have limited information content on the market. In addition, Alsakka and ApGwilym (2012) find that a number of empirical studies have shown that the prospect signals are less important than the actual rating changes in terms of the impact on financial markets.

3.1 Data and procedures

The sample includes daily data on sovereign bonds that go from July 2009 to July 2017.

Our sample includes more than 140 bonds issued by the following emerging markets: Argentina, Mexico, Peru, Hungary, Greece, Poland, Spain, Turkey and Hong Kong. We used daily data from July 2009 to July 2017, for the empirical configurations, which is historically limited by the availability of data on the bid-ask spread, Datastream. We obtained the assessments of these obligations of the Fitch Ratings rating agency. Then, we converted the ratings assigned to numbers from 23 (AAA) to 1 (D). For the remaining variables, data is extracted from the database Datastream (see Table 1 ). Further, rating collected is shown in Table 2 .

This study aims to question the sources of the liquidity problem of sovereign bonds issued by emerging countries. Our database consists of daily data of all nine emerging countries for the period from July 2009 to July 2017. Panel data were collected from the Datastream database.

To determine the factors explaining the lack of liquidity in the emerging bond markets, we used panel data as a technique for econometric analysis. Indeed, Greene, 2003 has defined panel data as a technique that uses cross-sectional data from the time domain to predict economic relations.

Similarly, Wooldridge (2002) shows that the panel uses the effects of cross-sections. Thus, the analysis makes use of data, which has both time and the number of bonds. Among the reasons why this technique was preferred over other techniques is that the technology allows us to control the secret effects that may be related to the parameters in the model of liquidity. In addition, we expect that the modelling of financial data is set so that it will have both the time dimension and the number of bonds dimension leading us to more accurate results.

Indeed, the use of panel data is preferable to the use of time-series data and the cross. First, in the panel data analysis, we do not meet the compliance deficiency problem that is common in many time-series and analysis sections. In addition, Sun and Parikh (2001) show that the observations collected during a period are arranged and the number of observations increases.

Similarly, Hsiao et al. (1999) demonstrates that the range of data reduces the interaction between the variables and parameters that will be more reliable. This improves the variation and the flow of information. In addition, the panel data could be used to analyse an infinite number of complex models concerning the time-series analysis and the analysis of the cross-section. However, Baltagi (2001) and Balestra and Nerlove (1992) demonstrated that the use of panel data reduces several estimation problems such as accurate autocorrelation ( Bayrakdaroglu et al. , 2013 ).

Absence of fixed effects: when the probability is greater than 5%.

Presence of fixed effects: when the probability is less than 5%.

That is, if the probability is less than 5%, we use the fixed effect, and if it is bigger, the random effect is issued. Indeed, the results presented in Table 4 show that the hypothesis H0 is rejected for the lever models with the 1% level of significance, so note the very individual effect on the total lever models is random but are fixed. In other words, hypothesis H1 expresses the fixed effects model is more effective than the random effects model. In addition, the fixed effects model in this study analyses regression panel data.

3.2 Econometric estimation

CR it : Credit rating announcement of bond i at time t ;

Vol it : Volatility of price of the bond i at time t ;

AI it : Asymmetric information between investors;

Coup it : Coupon of bond;

Age it : Age of the obligation; and

IR it : Interest rate.

3.3 Variables

This is considered central to the functioning of the financial markets. Indeed, structural changes in the financial systems that have been ongoing for some time have increased the importance of market liquidity. The literature provides a menu of measures and considers proxies to estimate the liquidity of the emerging markets. Lesmond (2005) expresses five different proxies for the liquidity measure. The first one expresses the bid-ask spread costs and the Commission. This measure is expressed as follows: Q u o t e d   S p r e a d Q = 1 / 2 [ ( A Q − B Q ) / ( A Q + B Q ) / 2 + ( A Q − 1 − B Q − 1 ) / ( A Q − 1 + B Q − 1 ) / 2 ]

Saadaoui and Boujelbene (2014) express this measure differently: L i q = [ ( A t − B t ) / ( A t + B t ) + ( A t − 1 − B t − 1 ) / ( A t − 1 + B t − 1 ) ]

The second measure is expressed by turnover. It is expressed as follows: 1 / D q ∑ 1 q V o l u m e   t / S h a r e   O u t s t a n d i n g where D Q is the number of days in the quarter, Q .

The third one expresses this measurement given by Amihud (2002) . In fact, the measure is expressed as follows: 1 / D q ∑ 1 Q | R | / ( P r i c e   t ∗ V o l u m e   t )

The fourth one expresses the extent of Roll (1984) . In fact, we tried to use another measure that is used by Dastidar and Phelps (2009) , and that expresses the cost of liquidity score: L C S = { ( B i d − A s k ) S p r e a d ∗ O A S D → i f   b o n d   i s   s p r e a d   c o t e d ( A s k   P r i c e − B i d   P r i c e ) B i d   P r i c e → i f   b o n d   i s   p r i c e   c o t e d

The choice of a liquidity model in the interest rate markets will ultimately depend on the data available, such as Liquidity Cost Scores (LCS) for our purposes. Although the LCS does not directly account for the price impact for large orders, Dastidar and Phelps (2009) find that it is strongly correlated with the price impact. The LCS seems persistent on average: bonds with a low LCS should remain liquid for a long time according to the LCS measure.

Credit rating

This explanatory variable expresses the quality of the borrower. Several measures for this variable differ from one author to another. This variable is expressed by the rating that reflects the credit quality of the borrower in the form of notations that differ from one agency to another. The three main rating agencies are the following: Fitch, Moody's and S&P. The notations used by these three agencies are detailed in Table 3 . These notations are transformed linearly in a digital form, as shown in Cantor and Packer (1996) . The ratings after transformation are displayed in Table 4 .

Information asymmetry is an explanatory variable that expresses the existing asymmetry between the seller and the buyer of a product or asset. On the credit market, banks give loans, and they do not know the risks associated with some loans that they give. On the contrary, borrowers know how likely the success of their projects is. This allows banks to raise interest rates on loans granted, and essentially to risky borrowers. Therefore, there are two situations: the first is the extant risk, which is determined at the time of signing the contract. The second one is the ex-post risk that emerges after the purchase or signing of the contract.

Volatility can be expressed as a measure of the variance of a security, an index relative to its average price. It, therefore, measures the historical variation in the price of a bond. It can be assessed over a short, medium or long history. Considered in finance as the basis for measuring risk, volatility is a measure of the amplitudes of changes in the price of a financial asset.

Thus, the higher the volatility of an asset is, the more the investment in this asset will be considered risky, and therefore the higher the expectation of gain (or risk of loss) is.

Conversely, a risk-free or very low-risk asset will have very low volatility because its repayment is almost certain. Actually, the volatility of a bond corresponds to the evolution of the price following a variation of 1% in interest rates. Commonly used to designate short-term oscillations of a financial asset, the concept of volatility concerns all horizons (short, medium and long term) and does not care about the direction of movement (only the amplitude of movements is taken into account).

The volatility of a bond is calculated, using the standard deviation formula: σ ( x ) = V ( x ) = ∑ i = 1 n ( X i − X ¯ ) 2 / n

Asymmetric information

Information asymmetry occurs when some investors have more information than others. In fact, this asymmetry hardly causes problems linked to anti-selection and moral hazard in the event of negotiation ( Akerlof, 1970 ). Verecchia (2001) shows that in order to mitigate the risk, uninformed investors reduce the price they are willing to pay for a bond or increase the asking price to sell it to minimize the spread between supply and demand for asset prices.

Several empirical studies conducted since the 1960s have indicated the relationship of the bid-ask spread, or simply the spread, with liquidity in trading assets and with information received by the market. The first analyses of the determinants of the bid-ask spread on the bond market followed the founding article by Demsetz (1968) . However, Aitken and Frino (1996) use the median spread in the calculation to alleviate the problems caused by the bid-ask rebound, as there are sometimes quoted supply and demand changes that are quickly reversed.

Indeed, other studies in the 1990s have used the intraday spread. The main justification for this methodology is the objective of the analysis, which focused on models for the propagation of Intraday volumes ( Lee et al. , 1993 ; McInish and Wood, 1992 ) and the calculation of liquidity ( Fleming, 2003 ; Fleming and Sarkar, 1999 ). In addition, other indicators have been used in the research to estimate information asymmetry: the probability of informed trading (PIN), market liquidity and bond volatility. Indeed, according to subsequent studies, the bid-ask propagation is the most frequently used measure of information asymmetry in university research. Due to its measurement characteristic, the bid-ask spread reflects the uncertainty in the value of the asset, and the greater the uncertainty is, the greater information asymmetry between the parties in a negotiation is. Finally, based on the studies presented above, we can say that the bid-ask gap is an adequate indicator of information asymmetry. Indeed, the formula that we used is expressed as follows: S i , t = ∑ ( P i , t a − P i , t b ) / ∑ P m i , t where P i , t a  = quoted ask in time period t of bond i ; P i , t b  = quoted bid in time period t of bond i ; Pm i,t  = midpoint spread in time period t of bond i and S i,t  = average daily bid-ask spread in time period t of bond i . The midpoint spread ( Pm i,t ) is given by ( P i , t a − P i , t b ) / 2 .

Age is considered as one of the main characteristics of the bond ranging from a few months to 50 years before the capital is repaid. Over this period, the risk is greatly high as there is more chance that the bond will be sold before maturity if it is remote.

The issuer offers the interest to the investor as compensation for the duration of the loan. It is expressed as a percentage of per-value. In principle, the amount of the coupon is more than the issuer of lesser quality, and the loan is long term. Conversely, an issuer of good quality, short-term securities borrows to offer a lower coupon. The coupon may be fixed or variable. It is mostly paid on an annual basis, but bonds may pay coupons that are more regular on a half-yearly or quarterly basis, for example. The coupons will depend on the duration of the obligation and the quality of the issuer. Some obligations do not pay a coupon during the life of the loan. This is called bonds “zero-coupon”.

Interest rate

Investing in bonds has long been considered one of the safest, especially if you hold the securities until maturity. However, there are some risks.

The sovereign debt crisis of 2011–2012 reminded us that even the supposedly safest bonds, namely those of governments, could present risks. The long period of very low interest rates that we have known for several years has reduced the attractiveness of bond investments while placing additional risk on bond holders. Interest rates measure the value of a bond. They can increase or decrease, and thus invest in bonds more or less attractive when compared to the value of the coupon. If rates go up, the price of an already issued bond decreases (investors prefer to place at higher rates, so resell the bonds they hold, which pushes their price down. Thus, already issued bonds offer the same return as the market). Conversely, if rates fall, the value of the bond rises. These movements have no consequences for investors who keep their bonds until maturity, since they will be redeemed at their original value.

4.1 Correlation analysis

Before beginning the regression of the panel data, it is necessary to examine the correlations between the explanatory variables used in our econometric model. The aim is to avoid the biases of multi-collinearity. The latter can induce instability of the regression coefficients and distort the precision of the model estimate. Table 5 shows the correlation coefficients between the different variables considered in our study.

Before interpreting the results of the estimation, it is interesting to study the problem of multi-collinearity between the explanatory variables. Reading the correlation matrix reveals that the correlation coefficients between the independent variables have a minimum value of −0.4575 and a maximum of 0.4203. This leads us to note the absence of the problem of multi-collinearity between these variables insofar as no coefficient exceeds the limit value of 0.7. This shows that the estimate of the regression coefficients of our model is reliable and valid.

4.2 Regression analysis

This paper examines the effect of credit rating announcement on bond liquidity, using the panel data methodology over 2009–2017 across nine countries such as Argentina, Poland, Greece, Mexico, Peru, Hong Kong, Spain, Hungary and Turkey. Table 6 presents results derived from this analysis.

According to our results, we observe that the information value of the credit rating agency is a contentious and not determinative issue. We also find that the change in ratings has a significant effect on the liquidity of bonds. These results are in line with the findings of previous research of Weinstein (1977) , Pinches and Singleton (1978) , Kaplan and Urwitz (1979) and Wakeman (1981) .

Several empirical studies on the significance of credit notifications on bond or equity returns have found antonymous results. Some studies have studied the change in the price of corporate bonds during the period around the announcement of a rating change, and they have suggested that the stock market has no significant reaction to these notifications. Other studies have established evidence that credit notifications provide a market information value.

We have tried to explain the effect of credit notifications on the liquidity of emerging bond markets, essentially in the post-crisis period. Indeed, the tables show that credit notifications have a significant effect on the liquidity of bonds. This significant effect may be positive for Poland, Mexico, Peru, Hong Kong, Hungary and Turkey, and negative for Argentina, Greece and Spain depending on the sign of the rating awarded by the rating agencies. The negative effect is explained by the degradation of the rating attributed by the rating agency, and this is explored in the results found concerning Greece and Spain where we found that the change of notation to a negative effect and significant is at the 1% level. This negative effect can be explained by the critical situations that Greece and Spain have seen from the beginning of 2010. These negative effects can be explained by the economic problems and the financial problems that have occurred in these two countries and which led to the intervention of the European Union to stabilize the financial and economic situation of Greece. These results confirm the real situation that these two countries have seen essentially in this period may explain the informational content of the notifications granted by the rating agencies. Our results also show that rating agencies have taken into account the criticisms expressed by several market participants regarding their strategies and the indicators used in the ratings given to financial assets. From the results found, it can be said that liquidity is also an important factor explaining bond spreads, after having neutralized the impact of credit ratings, maturities and volatility. These results indicate that liquidity is indeed taken into account in the valuation of bonds. Liquidity is a concept strongly depending on information transparency. Indeed, the presence of information asymmetry between investors makes trading in the market unclear and results in a reduction in liquidity in the market ( Bagehot, 1971 ; Myers and Majluf, 1984 ). This theoretical observation may have an effect on the quality of the information disclosed by the various rating agencies, which must show a practical interest for the managers of these agencies. They can seize the opportunity to improve their information disclosure policy in order to reduce information market asymmetries, increase investor confidence and increase the number of transactions in bond securities. Indeed, our results clearly explain this positive relationship between information asymmetry and the liquidity of bonds.

Our results confirm the work of Petersen and Plenborg (2006) , which denote that the high quality of disclosed announcement decreases information asymmetries on the market, increases investor authority and appreciates the liquidity of securities. This is explained by the significant effect of the asymmetry of information on the liquidity of the bond markets. Indeed, the more relevant and reliable the quality of the information disclosed by rating agencies is, the greater the liquidity of the securities is.

Our results also show that the control variables used in our model, and which represents the main characteristics of the bonds, have a significant effect on the liquidity of the bonds and essentially on the interest rate. The latter clearly expressing the financial behaviour of investors in the market suggests that the two factors price and actuarial rate of return are linked but vice versa. Indeed, the reality of the market imposes those investors to opt for the most profitable investments. Therefore, if interest rates rise, the investor will have an interest in selling their bond in order to invest in another, and if all other things are equal, there will be a fall in bond prices. Moreover, in the event of falling in interest rates, the price of the bond increases. Indeed, the age of a bond or what can be described as its time can indicate its level of liquidity. Indeed, this characteristic is negatively related to liquidity. Thus, the older a bond is or the longer it has been issued, the less liquid it will be. This is well expressed in our results for Greece and Spain during the period of the sovereign debt.

4.3 Robusteness checks

We will carry out a robustness test to have the effect of the announcement of rating agencies on the liquidity of emerging bond markets, and we will use an alternative method of estimating the long-term relationship DOLS (Dynamic Ordinary Least Squares). This method is based on a parametric procedure proposed by Saikkonen (1991) and Stock and Watson (1993) in the case of time series. It consists in including advanced and delayed values of the explanatory variables in the co-integration relation, in order to eliminate the nuisances linked to endogeneity and the serial correlation of the residuals.

Table 7 shows that the results of the DOLS estimates confirm the results obtained by the panel data approach. The coefficients are very close and in the case of the DOLS estimates we obtain a significant coefficient for the age variable.

5. Discussion and conclusions

Since the role of credit rating agencies in the sovereign debt crisis in the financial phase (2007–2008) of the current crisis is degrading, a lot of criticism was voiced against rating agencies, investors affected by the performance of certain financial assets that had the best ratings. This work examines the information value of local sovereign credit rating announcements in emerging countries. We analyse the effect of sovereign bond ratings on the liquidity of emerging bond markets.

The results of the empirical study indicate that ratings are comparable to signals, conveying information to investors on the bond market. Indeed, our results show that the notifications given by the rating agencies send signals on the liquidity of the bonds; the rating is a means for the investors to convince the market, to attract the providers of funds, and to finance at a lower cost. Similarly, our results show that the agencies give scores on a standardized scale; the informational content of the signals issued depends on the nature of the advertisement and its intensity.

This study can give investors certain assurances of being more courageous when investing in newly rated sovereign bonds. The new and affirmative notes are informative events based on our results. The certification role of credit rating agencies will help less knowledgeable investors who lack the skills and resources to assess credit risk and make better investment decisions. Credit rating announcements reduce asymmetric information between issuers and investors. In this regard, this study attempts to bridge the gap between theory and practice. Therefore, it is significant to consider the impact of rating announcements and the factors influencing bond yields as reliable and informative instruments for investment decisions.

There are certain limitations to this research. The sample size is small because the announcement of rating changes is not as important as in the bond market. And, as for future research, we can introduce other factors such as that of COVID-19 to test the effect of credit announcements on the liquidity of the bond markets, and we can make a comparison with those of the stock and oil markets.

Descriptive statistics of the sample

Note(s): AGE: Age of bonds; AI: Asymmetric information; COUP: Coupon; CR: Credit Rating; IR: Interest Rate; VOL: Volatility; Significance level: *** 1%; ** 5%; * 10%; the numbers in parentheses indicate the coefficient, and out of parentheses indicate the p -value

Source(s): Own elaboration

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Verrecchia , R.E. ( 2001 ), “ Essays on disclosure ”, Journal of Accounting and Economics , Vol. 32 Nos 1-3 , pp.  97 - 180 .

Voorhees , R. ( 2011 ), “ Rating the raters: restoring confidence and account ability in credit rating agencies ”, Case Western Reserve Journal of International Law , Vol. 44 , p. 875 .

Wakeman , L.M. ( 1981 ), “ The real function of bond rating agencies ”, Chase Financial Quarterly , Vol. 1 No.  1 , pp.  18 - 26 .

Weinstein , M.I. ( 1977 ), “ The effect of a rating change announcement on bond price ”, Journal of Financial Economics , Vol. 5 No.  3 , pp.  329 - 350 .

Wengner , A. , Burghof , H.P. and Schneider , J. ( 2015 ), “ The impact of credit rating announcements on corporate CDS markets—are intra-industry effects observable? ”, Journal of Economics and Business , Vol. 78 , pp.  79 - 91 .

Wooldridge , J.M. ( 2002 ), “ Further results on instrumental variables estimation of average treatment effects in the correlated random coefficient model ”, Economics Letters , Vol. 79 No.  2 , pp.  185 - 191 .

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Case Study Credit Rating advisory ₹110 Crore

by Vora Corporate Finance | Jun 7, 2019 | Credit Rating Advisory , Financial Advisory , Insights | 0 comments

Ship breaking company in operation from 70s requested advisory to improve long term financial position and improve credit rating from Credit rating agency. 

Vora Corporate Finance streamlined the working capital cycle and asset base for the company to improve the capital structure and improved the credit rating by two notches. 

The Full Story

The situation.

M, N and O* was a group of three companies involved in shipbreaking activities. The operations of these companies were largely dependent upon working capital as large letter of credits have to be opened for buying ships. The total working capital limit of the group was Rs.1.1 billion. 

Due to high working capital intensity of operations and deteriorating industry scenario, the credit rating agency downgraded rating of the 3 companies in FY17. Rating downgrade could affect the group on obtaining funds at desired rate of interest. 

MNO’s management approached Vora Corporate Finance to help with the following financial parameters due to which the rating was downgraded:

• Debt Equity Ratio was over 10
• High Debtors and high debt compared to peers resulting in high working capital requirement. 
• Liquidation of Letter of Credits was slow due to historical poor practises. 

Our Approach

Vorafin focused on the following key areas of the group to improve the weak financial situation:

• Receivables Management: Review the credit terms provided to customers and improve receivables management
• Group wealth: Review the net worth of the group to see if it is adequate to meet liabilities
• Competition analysis: Review competitors’ policy of debtors’ management 
• Ratio Analysis: To identify potential areas of financials to improve strength of balance sheet. 

Action Taken

• New capital infused into the business to clear outstanding Letter of Credit balance and to improve financial position. Also certain unused family resources were identified and mobilised for business. 
• This improved the Debt Equity ratio to bring it closer to a much acceptable ratio for the banks. 
• Vorafin also suggested streamlining intercompany transactions for better receivables management and thereby improving working capital cycle. This resulted in releasing liquidity of business and reduced the working capital requirement of the company. 

The Results

MNO’s financials improved and the credit rating agency increased the credit rating by two notches for limits of Rs. 1.1 Billion (Rs. 110 Crore). 

M, N, and O’s capital structure improved substantially because of this exercise and balance sheet strength was improved.

Company’s standing with the Bankers was improved and rate of interest was made more favourable. 

Company was now in a better position to raise further funds in future in case of more favourable business opportunities.

* We take our clients’ confidentiality seriously. While the names are changed, the results are real. 

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Credit Scoring: Case Study

• July 2, 2023

Download the dataset below to solve this Data Science case study on credit scoring.

Credit scoring aims to determine the creditworthiness of individuals based on their credit profiles. By analyzing factors such as payment history, credit utilization ratio, and number of credit accounts, we can assign a credit score to each individual, providing a quantitative measure of their creditworthiness.

The given dataset includes features such as age, gender, marital status, education level, employment status, credit utilization ratio, payment history, number of credit accounts, loan amount, interest rate, loan term, type of loan, and income level.

Your task is to calculate credit scores and segment customers based on their credit scores to gain insights into different customer groups.

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Credit Rating

• Harvard Case Studies

Harvard Business Case Studies Solutions – Assignment Help

In most courses studied at Harvard Business schools, students are provided with a case study. Major HBR cases concerns on a whole industry, a whole organization or some part of organization; profitable or non-profitable organizations. Student’s role is to analyze the case and diagnose the situation, identify the problem and then give appropriate recommendations and steps to be taken.

To make a detailed case analysis, student should follow these steps:

STEP 1: Reading Up Harvard Case Study Method Guide:

Case study method guide is provided to students which determine the aspects of problem needed to be considered while analyzing a case study. It is very important to have a thorough reading and understanding of guidelines provided. However, poor guide reading will lead to misunderstanding of case and failure of analyses. It is recommended to read guidelines before and after reading the case to understand what is asked and how the questions are to be answered. Therefore, in-depth understanding f case guidelines is very important.

Harvard Case Study Solutions

porter’s five forces model

STEP 2: Reading The Credit Rating Harvard Case Study:

To have a complete understanding of the case, one should focus on case reading. It is said that case should be read two times. Initially, fast reading without taking notes and underlines should be done. Initial reading is to get a rough idea of what information is provided for the analyses. Then, a very careful reading should be done at second time reading of the case. This time, highlighting the important point and mark the necessary information provided in the case. In addition, the quantitative data in case, and its relations with other quantitative or qualitative variables should be given more importance. Also, manipulating different data and combining with other information available will give a new insight. However, all of the information provided is not reliable and relevant.

When having a fast reading, following points should be noted:

• Nature of organization
• Nature if industry in which organization operates.
• External environment that is effecting organization
• Problems being faced by management
• Identification of communication strategies.
• Any relevant strategy that can be added.
• Control and out-of-control situations.

When reading the case for second time, following points should be considered:

• Decisions needed to be made and the responsible Person to make decision.
• Objectives of the organization and key players in this case.
• The compatibility of objectives. if not, their reconciliations and necessary redefinition.
• Sources and constraints of organization from meeting its objectives.

After reading the case and guidelines thoroughly, reader should go forward and start the analyses of the case.

STEP 3: Doing The Case Analysis Of Credit Rating:

To make an appropriate case analyses, firstly, reader should mark the important problems that are happening in the organization. There may be multiple problems that can be faced by any organization. Secondly, after identifying problems in the company, identify the most concerned and important problem that needed to be focused.

Firstly, the introduction is written. After having a clear idea of what is defined in the case, we deliver it to the reader. It is better to start the introduction from any historical or social context. The challenging diagnosis for Credit Rating and the management of information is needed to be provided. However, introduction should not be longer than 6-7 lines in a paragraph. As the most important objective is to convey the most important message for to the reader.

After introduction, problem statement is defined. In the problem statement, the company’s most important problem and constraints to solve these problems should be define clearly. However, the problem should be concisely define in no more than a paragraph. After defining the problems and constraints, analysis of the case study is begin.

STEP 4: SWOT Analysis of the Credit Rating HBR Case Solution:

• Pest analysis

SWOT analysis helps the business to identify its strengths and weaknesses, as well as understanding of opportunity that can be availed and the threat that the company is facing. SWOT for Credit Rating is a powerful tool of analysis as it provide a thought to uncover and exploit the opportunities that can be used to increase and enhance company’s operations. In addition, it also identifies the weaknesses of the organization that will help to be eliminated and manage the threats that would catch the attention of the management.

This strategy helps the company to make any strategy that would differentiate the company from competitors, so that the organization can compete successfully in the industry. The strengths and weaknesses are obtained from internal organization. Whereas, the opportunities and threats are generally related from external environment of organization. Moreover, it is also called Internal-External Analysis.

In the strengths, management should identify the following points exists in the organization:

• Advantages of the organization
• Activities of the company better than competitors.
• Unique resources and low cost resources company have.
• Activities and resources market sees as the company’s strength.
• Unique selling proposition of the company.

WEAKNESSES:

• Improvement that could be done.
• Activities that can be avoided for Credit Rating.
• Activities that can be determined as your weakness in the market.
• Factors that can reduce the sales.
• Competitor’s activities that can be seen as your weakness.

OPPORTUNITIES:

• Good opportunities that can be spotted.
• Interesting trends of industry.
• Change in technology and market strategies
• Government policy changes that is related to the company’s field
• Changes in social patterns and lifestyles.
• Local events.

Following points can be identified as a threat to company:

• Company’s facing obstacles.
• Activities of competitors.
• Product and services quality standards
• Threat from changing technologies
• Financial/cash flow problems
• Weakness that threaten the business.

Following points should be considered when applying SWOT to the analysis:

• Precise and verifiable phrases should be sued.
• Prioritize the points under each head, so that management can identify which step has to be taken first.
• Apply the analyses at proposed level. Clear yourself first that on what basis you have to apply SWOT matrix.
• Make sure that points identified should carry itself with strategy formulation process.
• Use particular terms (like USP, Core Competencies Analyses etc.) to get a comprehensive picture of analyses.

STEP 5: PESTEL/ PEST Analysis of Credit Rating Case Solution:

Pest analyses is a widely used tool to analyze the Political, Economic, Socio-cultural, Technological, Environmental and legal situations which can provide great and new opportunities to the company as well as these factors can also threat the company, to be dangerous in future.

Pest analysis is very important and informative.  It is used for the purpose of identifying business opportunities and advance threat warning. Moreover, it also helps to the extent to which change is useful for the company and also guide the direction for the change. In addition, it also helps to avoid activities and actions that will be harmful for the company in future, including projects and strategies.

To analyze the business objective and its opportunities and threats, following steps should be followed:

• Brainstorm and assumption the changes that should be made to organization. Answer the necessary questions that are related to specific needs of organization
• Analyze the opportunities that would be happen due to the change.
• Analyze the threats and issues that would be caused due to change.
• Perform cost benefit analyses and take the appropriate action.

PEST FACTORS:

• Next political elections and changes that will happen in the country due to these elections
• Strong and powerful political person, his point of view on business policies and their effect on the organization.
• Strength of property rights and law rules. And its ratio with corruption and organized crimes. Changes in these situation and its effects.
• Change in Legislation and taxation effects on the company
• Trend of regulations and deregulations. Effects of change in business regulations
• Timescale of legislative change.
• Other political factors likely to change for Credit Rating.

ECONOMICAL:

• Position and current economy trend i.e. growing, stagnant or declining.
• Exchange rates fluctuations and its relation with company.
• Change in Level of customer’s disposable income and its effect.
• Fluctuation in unemployment rate and its effect on hiring of skilled employees
• Access to credit and loans. And its effects on company
• Effect of globalization on economic environment
• Considerations on other economic factors

SOCIO-CULTURAL:

• Change in population growth rate and age factors, and its impacts on organization.
• Effect on organization due to Change in attitudes and generational shifts.
• Standards of health, education and social mobility levels. Its changes and effects on company.
• Employment patterns, job market trend and attitude towards work according to different age groups.

case study solutions

• Social attitudes and social trends, change in socio culture an dits effects.
• Religious believers and life styles and its effects on organization
• Other socio culture factors and its impacts.

TECHNOLOGICAL:

• Any new technology that company is using
• Any new technology in market that could affect the work, organization or industry
• Access of competitors to the new technologies and its impact on their product development/better services.
• Research areas of government and education institutes in which the company can make any efforts
• Changes in infra-structure and its effects on work flow
• Existing technology that can facilitate the company
• Other technological factors and their impacts on company and industry

These headings and analyses would help the company to consider these factors and make a “big picture” of company’s characteristics. This will help the manager to take the decision and drawing conclusion about the forces that would create a big impact on company and its resources.

STEP 6: Porter’s Five Forces/ Strategic Analysis Of The Credit Rating Case Study:

To analyze the structure of a company and its corporate strategy, Porter’s five forces model is used. In this model, five forces have been identified which play an important part in shaping the market and industry. These forces are used to measure competition intensity and profitability of an industry and market.

porter’s five forces model

These forces refers to micro environment and the company ability to serve its customers and make a profit. These five forces includes three forces from horizontal competition and two forces from vertical competition. The five forces are discussed below:

• THREAT OF NEW ENTRANTS:
• as the industry have high profits, many new entrants will try to enter into the market. However, the new entrants will eventually cause decrease in overall industry profits. Therefore, it is necessary to block the new entrants in the industry. following factors is describing the level of threat to new entrants:
• Barriers to entry that includes copy rights and patents.
• High capital requirement
• Government restricted policies
• Switching cost
• Access to suppliers and distributions
• Customer loyalty to established brands.
• THREAT OF SUBSTITUTES:
• this describes the threat to company. If the goods and services are not up to the standard, consumers can use substitutes and alternatives that do not need any extra effort and do not make a major difference. For example, using Aquafina in substitution of tap water, Pepsi in alternative of Coca Cola. The potential factors that made customer shift to substitutes are as follows:
• Price performance of substitute
• Switching costs of buyer
• Products substitute available in the market
• Reduction of quality
• Close substitution are available
• DEGREE OF INDUSTRY RIVALRY:
• the lesser money and resources are required to enter into any industry, the higher there will be new competitors and be an effective competitor. It will also weaken the company’s position. Following are the potential factors that will influence the company’s competition:
• Competitive advantage
• Continuous innovation
• Sustainable position in competitive advantage
• Level of advertising
• Competitive strategy
• BARGAINING POWER OF BUYERS:
• it deals with the ability of customers to take down the prices. It mainly consists the importance of a customer and the level of cost if a customer will switch from one product to another. The buyer power is high if there are too many alternatives available. And the buyer power is low if there are lesser options of alternatives and switching. Following factors will influence the buying power of customers:
• Bargaining leverage
• Switching cost of a buyer
• Buyer price sensitivity
• Competitive advantage of company’s product
• BARGAINING POWER OF SUPPLIERS:
• this refers to the supplier’s ability of increasing and decreasing prices. If there are few alternatives o supplier available, this will threat the company and it would have to purchase its raw material in supplier’s terms. However, if there are many suppliers alternative, suppliers have low bargaining power and company do not have to face high switching cost. The potential factors that effects bargaining power of suppliers are the following:
• Input differentiation
• Impact of cost on differentiation
• Strength of distribution centers
• Input substitute’s availability.

STEP 7: VRIO Analysis of Credit Rating:

Vrio analysis for Credit Rating case study identified the four main attributes which helps the organization to gain a competitive advantages. The author of this theory suggests that firm must be valuable, rare, imperfectly imitable and perfectly non sustainable. Therefore there must be some resources and capabilities in an organization that can facilitate the competitive advantage to company. The four components of VRIO analysis are described below: VALUABLE: the company must have some resources or strategies that can exploit opportunities and defend the company from major threats. If the company holds some value then answer is yes. Resources are also valuable if they provide customer satisfaction and increase customer value. This value may create by increasing differentiation in existing product or decrease its price. Is these conditions are not met, company may lead to competitive disadvantage. Therefore, it is necessary to continually review the Credit Rating company’s activities and resources values. RARE: the resources of the Credit Rating company that are not used by any other company are known as rare. Rare and valuable resources grant much competitive advantages to the firm. However, when more than one few companies uses the same resources and provide competitive parity are also known as rare resources. Even, the competitive parity is not desired position, but the company should not lose its valuable resources, even they are common. COSTLY TO IMITATE: the resources are costly to imitate, if other organizations cannot imitate it. However, imitation is done in two ways. One is duplicating that is direct imitation and the other one is substituting that is indirect imitation. Any firm who has valuable and rare resources, and these resources are costly to imitate, have achieved their competitive advantage. However, resources should also be perfectly non sustainable. The reasons that resource imitation is costly are historical conditions, casual ambiguity and social complexity. ORGANIZED TO CAPTURE VALUE: resources, itself, cannot provide advantages to organization until it is organized and exploit to do so. A firm (like Credit Rating)  must organize its management systems, processes, policies and strategies to fully utilize the resource’s potential to be valuable, rare and costly to imitate.

STEP 8: Generating Alternatives For Credit Rating Case Solution:

After completing the analyses of the company, its opportunities and threats, it is important to generate a solution of the problem and the alternatives a company can apply in order to solve its problems. To generate the alternative of problem, following things must to be kept in mind:

• Realistic solution should be identified that can be operated in the company, with all its constraints and opportunities.
• as the problem and its solution cannot occur at the same time, it should be described as mutually exclusive
• it is not possible for a company to not to take any action, therefore, the alternative of doing nothing is not viable.
• Student should provide more than one decent solution. Providing two undesirable alternatives to make the other one attractive is not acceptable.

Once the alternatives have been generated, student should evaluate the options and select the appropriate and viable solution for the company.

STEP 9: Selection Of Alternatives For Credit Rating Case Solution:

It is very important to select the alternatives and then evaluate the best one as the company have limited choices and constraints. Therefore to select the best alternative, there are many factors that is needed to be kept in mind. The criteria’s on which business decisions are to be selected areas under:

• Improve profitability
• Increase sales, market shares, return on investments
• Customer satisfaction
• Brand image
• Corporate mission, vision and strategy
• Resources and capabilities

Alternatives should be measures that which alternative will perform better than other one and the valid reasons. In addition, alternatives should be related to the problem statements and issues described in the case study.

STEP 10: Evaluation Of Alternatives For Credit Rating Case Solution:

If the selected alternative is fulfilling the above criteria, the decision should be taken straightforwardly. Best alternative should be selected must be the best when evaluating it on the decision criteria. Another method used to evaluate the alternatives are the list of pros and cons of each alternative and one who has more pros than cons and can be workable under organizational constraints.

STEP 11: Recommendations For Credit Rating Case Study (Solution):

There should be only one recommendation to enhance the company’s operations and its growth or solving its problems. The decision that is being taken should be justified and viable for solving the problems.

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ESG, credit risk and ratings: part 3 - from disconnects to action areas

• 1 Executive summary
• 2 Fostering CRA-investor dialogue
• 3 From disconnects to action areas
• 4 A transparent and systematic framework
• 5 Applying theory to practice
• 6 Next steps: Connecting the dots
• 7 Regional colour from the forums
• 8 Sovereign versus corporate credit risk
• 9 CRA examples
• 10 Investor case studies
• 11 Case study: AXA Group
• 12 Case study: BlueBay Asset Management LLP
• 13 Case study: Futuregrowth Asset Management
• 14 Case study: HSBC Global Asset Management
• 15 Case study: Legal & General Investment Management
• 16 Case study: Nikko Asset Management
• 17 Case study: NN Investment Partners
• 18 Case study: Nomura Asset Management
• 19 Case study: Triodos Investment Management
• 20 Case study: Aegon Asset Management
• 21 Case study: Caisse des Depots
• 22 Case study: Colchester Global Investors
• 23 Case study: Insight Investment
• 24 Case study: PIMCO
• 25 Case study: Templeton Global Macro

Case study: Colchester Global Investors

2019-01-31T09:24:00+00:00

Action area:

• Materiality of ESG factors 

The investment approach

ESG factors have always been an integral part of Colchester Global Investors’ (Colchester) investment process for developed and emerging economies. We believe that countries with stronger governance, healthier and more educated workforces, and higher environmental standards tend to produce better economic outcomes. Typically, this leads to more stable debt and currency paths, which are associated with better risk-adjusted returns.

Colchester integrates its assessment of ESG factors into its analysis of a country’s balance sheet (see below). 

Quantitative and qualitative ESG factors within the in-house investment process

Source: Colchester Global Investors

Unsurprisingly, governance is the most important factor when considering sovereign bonds. Not only is stronger governance correlated with higher GDP per capita (see below), it also strongly influences social and environmental factors, as government policies define the framework within which social and environmental outcomes are determined. There is evidence to support the belief that better social conditions – healthcare, educational standards, labour conditions, etc. – have a positive impact on economic outcomes. The positive correlation (R2 = 0.68) between educational standards and per capita GDP is one example. Similarly, a strong link can be found between environmental performance index and a country’s economic outcome (R2 = 0.72). Academic research also supports the notion that countries with higher governance standards tend to have more effective environmental policies.

Correlation between governance factors and GDP per capita

Source: IMF, World Bank and Colchester Global Investors  

The investment process

ESG factors are integrated holistically - not formulaically - into our investment valuation framework. Countries are assigned a proprietary financial stability score (FSS) that combines an assessment of their overall balance sheet strength and ESG factors (see below). Bond and currency scores range from +4 to –4, and a country may be excluded from the investment universe if its ranking falls below –4. Colchester penalises a country’s balance sheet for weak ESG factors (for example, Russia), but does not increase the FSS for strong ESG factors. ESG and country research is undertaken by Colchester’s investment team, who also engage with stakeholders, where possible, during country research trips. Such engagement and potential impact is more limited and, by definition, more challenging in the sovereign space compared with corporations.

ESG integration process

Colchester invests on the basis of real yields and real exchange rates, adjusted for the FSS. Portfolio construction is based on a standard mean-variance optimisation framework with risk measures and investment guideline constraints. The latter include, among others, client-specific credit limits, which can range from minimum ratings of AA- to below investment grade. The position size is generally a direct function of risk-adjusted potential real returns. Should two countries have equal real yields (unadjusted for their FSS), the one with the higher FSS would be favoured. Specifically, a country with stronger ESG factors is likely to have a higher comparative FSS score that enhances its attractiveness relative to the country that has weaker ESG factors. Underpinning this assessment is the belief that the former is likely to have a better economic outcome and deliver better medium-term returns.

The investment outcomes

The Italian case study below shows how Colchester’s investment process identifies material ESG issues and how these are addressed.The structural weakness in the Italian balance sheet is long-standing. Political instability has prevented successive governments from implementing lasting structural reforms and limited the country’s growth potential. While Italy compares unfavorably to most other developed world economies on most ESG indicators, weak governance underpins much of this underperformance. Such weaknesses have facilitated tax evasion and corruption, led to inefficient resource allocation, hampered productivity growth and promoted growth in the shadow economy. An inefficient bureaucracy and a complex, slow legal system also increases transaction costs and inhibits activity.

Italy also underperforms across a number of social and environmental factors. While educational standards appear comparable with other OECD countries, Italy suffers skills mismatches, particularly with lower skilled workers, which affects wage and productivity growth. This translates into very high youth unemployment (35 percent as of 2017) and a school drop-out rate (14 percent as of 2017) which is high compared with its peers.

In summary, whilst Italy has made some reform progress recently, has low private sector debt, and is a member of the EU, it has high government debt, structural rigidities, unstable governments and weak levels of governance and social factors. Our assessment of these ESG factors weighs on Italy’s overall FSS, leaving it at the lower end of our FSS range, at -3. Given that these weaknesses have been inherent in its balance sheet for several years, Italy’s FSS has remained unchanged over the past 10 years. Notwithstanding market volatility and credit rating agency pronouncements, the key drivers of Italy’s financial stability have not materially changed over many years (see below). Italy’s potential real yield (FSS-adjusted) relative to other markets, combined with risk management, kept Colchester’s Global Bond Programme underweight the Italian bond market prior to the eurozone crisis. When valuations became more attractive, the bond programme established a gradual overweight in mid-2012, despite the unchanged balance sheet. As Italian yields fell in absolute and relative terms to Germany, Colchester subsequently reduced its exposure to Italian bonds.

10-year Italian government bond spread over Bunds, and credit ratings of the three main agencies

Source: Bloombers, Colchester Global Investors 

Key takeaways

Colchester has incorporated ESG factors within its investment process since the inception of the firm. Colchester believes ESG factors are an important consideration when assessing a country’s financial stability. Stronger, more stable balance sheets combined with positive ESG factors have been associated with better financial outcomes. Incorporating this analysis into our investment process has been beneficial to Colchester’s long-term performance track record.

Download the report

Shifting perceptions: ESG, credit risk and ratings: part 3 - from disconnects to action areas

January 2019

• HQ: Developed Markets
• Shifting perceptions: ESG, credit risk and ratings - part 3: from disconnects to action areas

Executive summary

Fostering CRA-investor dialogue

From disconnects to action areas

A transparent and systematic framework

Applying theory to practice

Next steps: connecting the dots.

Regional colour from the forums

Sovereign versus corporate credit risk

CRA examples

Investor case studies

Case study: AXA Group

Case study: BlueBay Asset Management LLP

Case study: Futuregrowth Asset Management

Case study: HSBC Global Asset Management

Case study: Legal & General Investment Management

Case study: Nikko Asset Management

Case study: NN Investment Partners

Case study: Nomura Asset Management

Case study: Triodos Investment Management

Case study: Aegon Asset Management

Case study: Caisse des Depots

Case study: Insight Investment

Case study: PIMCO

Case study: Templeton Global Macro

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