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The purpose of this paper is to empirically investigate the effect of real credit ratings change on capital structure decisions.

The study uses three models to examine the impact of credit rating on capital structure decisions within the framework of credit rating-capital structure hypotheses (broad rating, notch rating and investment or speculative grade). These hypotheses are tested by multiple linear regression models.

The results demonstrate that firms issue less net debt relative to equity post a change in the broad credit ratings level (e.g. a change from A- to BBB+). The findings also show that firms are less concerned by notch ratings change as long the firms remain the same broad credit rating level. Moreover, the paper indicates that firms issue less net debt relative to equity after an upgrade to investment grade.

The study covers the periods of 2009 to 2016; therefore, the research result may be affected by the period specific events such as the European debt crisis. Moreover, studying listed non-financial firms only in the Tadawul Stock Exchange has resulted in small sample which may not be adequate enough to reach concrete generalization. Despite the close proximity between the GCC countries, there could be jurisdictional difference due to country specific regulations, policies or financial development. Therefore, it will be interesting to conduct a cross country study on the GCC to see if the conclusions can be generalized to the region.

The paper contributes to the literature by testing previous researches on new context (Kingdom of Saudi Arabia, KSA) which lack sophisticated comparable studies to the one conducted on other regions of the world. The results highlight the importance of credit ratings for the decision makers who are required to make essential decisions in areas such as financing, structuring or operating firms and regulating markets. To the best of the authors’ knowledge, this is the first study of its kind that has been applied on the GCC region.

Given the importance of credit rating agencies’ (CRAs) assessment in affecting international financial markets, it is useful for policymakers and investors to be able to forecast it properly. Therefore, this study aims to forecast sovereign risk perception of the main agencies related to Brazilian bonds through the application of different machine learning (ML) techniques and evaluate their predictive accuracy in order to find out which one is best for this task.

Based on monthly data from January 1996 to November 2018, we perform different forecast analyses using the K-Nearest Neighbors, the Gradient Boosted Random Trees and the Multilayer Perceptron methods.

The results of this study suggest the Multilayer Perceptron technique is the most reliable one. Its predictive accuracy is relatively high if compared to the other two methods. Its forecast errors are the lowest in both the out-of-sample and in-sample forecasts’ exercises. These results hold if we consider the CRAs classification structure as linear or logarithmic. Moreover, its forecast errors are not statistically associated with periods of changes in CRAs’ opinion of any sort.

To the best of the authors’ knowledge, this study is the first to evaluate the performance of ML methods in the task of predicting sovereign credit news, including not only the sovereign ratings but also the outlook and credit watch status. In addition, the authors investigate whether the forecasts errors are statistically associated with periods of changes in sovereign risk perception.

In April and May of 2010 Moody's recalibrated its municipal bond ratings to a global scale, the system they use for other asset classes and the same scale used by Standard and Poor's (S&P). The authors investigate the impact of Moody's recalibration on true interest cost (TIC) of competitively-sold, uninsured, new bond issues with split bond ratings, by looking at a sample of bond issues before recalibration (1997–2010) and after recalibration (2010–2017).

Two different hypotheses are tested for each period to estimate whether TIC remains the same when the S&P rating is higher (H1) than Moody's rating or lower (H2) compared to bond issues for which the S&P and Moody's rating are the same. Further, two additional hypotheses are tested. H3 tests whether the impact of having a higher rating from S&P is the same as having a lower rating from S&P. H4 tests whether the impact of having a split rating is the same in the pre- and post-recalibration period.

Tests suggest that before recalibration a higher S&P rating leads to significantly lower interest costs, but a lower S&P rating does not lead to significantly higher costs. After recalibration, a higher S&P rating leads to significantly lower interest costs; however, a lower S&P rating leads to significantly higher interest costs for the bonds in the sample. The findings also suggest that the rating systems of Moody's and S&P became more similar to each other after recalibration and that the impact on interest cost of a higher S&P rating is reduced after the recalibration.

It appears that a given Moody's rating (which used higher credit standards in the period before recalibration) was more influential than the S&P rating prior to recalibration because investors “ignored” a lower S&P rating during this period. After recalibration, the lower S&P rating was no longer ignored by investors. Therefore, Moody's recalibration seems to have had the intended effect of moving the credit standards of the two rating agencies more into parity. This provides value to investors since they may now assume, unlike the situation in the pre-recalibration period, that similar ratings from the two companies provide similar information about the probability of default and loss that would occur following a default. From the standpoint of regulators, the municipal credit information is easier to understand and is more transparent for investors.

This paper aims to examine the informational value of credit rating changes for investors. The article analyses whether credit rating changes indicate the future financial performance of a firm.

The study employs pooled time-series cross-section regression technique and two-sample t-test for analysis. The paper utilizes a firm's operating profit as a proxy of its future financial performance to understand what inference can be drawn about future financial performance from a change in a firm's credit rating.

The paper finds that a firm operating profit declines in the year after a credit rating downgrade. However, no such significant relationship is evident in the case of a rating upgrade. The results are consistent across rating categories and individual years of the sample period.

The study uses non-financial corporate rating data; hence, the findings may not apply to credit rating changes in financial corporates and structured finance.

Investors and analysts can incorporate credit rating downgrade by CRAs as a key input in a firm's future financial forecast. Analysts and investment managers can also look at credit rating changes of firms in the same industry and draw a definite conclusion about which firm is likely to see a higher deterioration in performance.

The author has not come across any literature that directly investigates credit rating changes from the perspective of information content about future financial performance.

The purpose of this paper is to analyze which key financial factors are appropriate for measuring a credit rating score for family firms. In the recent literature, there exists a vast number of studies which evaluates performance differences between family and non-family firms (NFF). However an analysis with regards to a distinction between credit rating scores of family-orientated businesses compared to their counterparts in Austria has not been examined so far.

In order to bridge this research gap, an empirical model based on Moody’s credit rating methodology is used to address these issues. Therefore, the relevant data were taken from the 600 largest, both listed and non-listed, companies of Austria. The statistical measurements refer to a comparison of the means resulting from quantitative rating categories (profitability, leverage structure, liquidity development and firm size).

The results of this empirical research show that family firms achieve better values in profitability, leverage structure and liquidity development based on credit rating scores. Only firm size represents no significant differences between family and NFF.

This study will contribute to the existing literature in the academic area of family business research and offers a framework for future empirical analysis in this field. Furthermore, this paper provides important information that will help both family and NFF accomplish their financial strategies related to credit rating transitions.

The purpose of this study is to examine the impact of goodwill impairment losses on bond credit ratings.

The authors use regression analysis to examine the relationship between goodwill impairment losses and bond credit ratings.

The empirical results show a negative relationship between the amount of goodwill impairment losses and bond credit ratings, suggesting that firms with goodwill impairment losses receive lower credit ratings. The authors perform various additional tests, including subsamples in good or bad market time, changes analysis, first time goodwill impairment firms vs subsequent impairment and the two-stage least squares regression analysis to address potential endogeneity issues. The main results persist.

This paper links and contributes to two streams of literature: goodwill impairment in accounting literature and bond credit ratings in finance literature. Whether a firm’s goodwill impairment losses affect the firm’s bond credit rating remains an interesting question that has not been examined previously. To the best of the authors’ knowledge, this is the first study that directly examines the relationship between goodwill impairment losses and bond ratings at the firm level.

Since sovereign ratings provided by credit rating agencies (CRAs) are a key determinant of the interest rates a country faces in the international financial market and once sovereign ratings may have a constraining impact on the ratings assigned to domestic banks or companies, some studies have focused on identifying the determinants of sovereign credit risk assessments provided by CRAs. In particular, this study estimates the effect of fiscal credibility on sovereign risk using four different comprehensive credit rating (CCR) measures obtained from CRAs' announcements and two different fiscal credibility indicators.

We build comprehensive credit rating (CCR) measures to capture sovereign risk. These measures are calculated using sovereign ratings, the rating outlooks and credit watches issued by the three main credit rating agencies (S&P, Moody's and Fitch) for long-term foreign-currency Brazilian bonds. Based on monthly data from 2003 to 2018, we use different econometric estimation techniques in order to provide robust results.

The results indicate that fiscal credibility exerts both short- and long-run effects on sovereign risk perception, and macroeconomic fundamentals are important long-run determinants.

Since fiscal credibility reflects the government's ability to maintain budgetary balance and sustainable public debt, the government should keep its commitment to responsible fiscal policies so as not to deteriorate expectations formed by financial market experts about the fiscal scenario and, thus, to achieve better credit assessments issued by CRAs with respect to sovereign debt bonds. Sovereign credit rating assessment is a voluntary practice. It is up to the country whether they want to apply for a rating assessment or not. Thus, without a sovereign rating, one must find an alternative to measure the sovereign risk of a country. In this sense, an important practical implication that this study provides is that fiscal credibility can be used as a leading indicator of sovereign risk perceptions obtained from CRAs or even as a proxy for sovereign risk.

This paper is the first to verify how important the expectations of financial market experts in relation to the fiscal effort required to keep public debt at a sustainable level (i.e. fiscal credibility) are to sovereign risk perception of credit rating agencies. In this sense, the study is the first to address this relation, and thus it contributes to the literature that seeks to understand the determinants of sovereign ratings in emerging countries.

Credit market models and the microstructure theory of the ratings market suggest that information provided by credit rating agencies becomes more relevant in recessions when agency costs are high and less relevant in expansions when agency costs are low. The purpose of this paper is to empirically test these hypotheses with regard to equity markets.

The authors use business cycle identification algorithms to map rating events (credit rating changes and watchlist inclusions) to business cycle phases and apply the event study methodology. The results are backed by cross-sectional regressions using a variety of control variables.

The authors find that the relevance of information provided by credit rating agencies for equity prices heavily depends on the level of agency costs. Furthermore, the authors detect a “flight-to-quality” during recessions in the speculative grade segment and a weakened relevance of rating events in expansions in the investment grade segment.

This paper is the first to empirically analyse how equity investors perceive credit rating changes and watchlist inclusions over the business cycle. In the empirical analysis, the authors use a large sample of about 25,000 rating events in all Organisation for Economic Co-operation and Development markets. The presented results underline that credit ratings address the agency problem in financial markets and can thus be regarded as useful for risk management or regulation.

This study seeks to identify the main determinants of the optimal capital structure by reexamining the interpretation of the conventional set of explanatory variables used as proxies for the costs and benefits of debt in the context of the dynamic tradeoff theory.

The authors isolate the variation in leverage due to different targets from that caused by deviations by aggregating the data across a dimension identifying firms with similar targets – credit rating category.

Contrary to theoretical priors, large and profitable rated firms have lower targets. The authors show that size and profitability proxy for non-financial risk and that, for rated firms, non-financial risk is positively correlated to the optimal leverage. The benefits of a better rating outweigh the costs of foregone tax shields for firms with relatively low non-financial risk. The authors find support for that theory in institutional trading – institutional investors do not punish highly rated firms when credit downgrades occur.

This paper contributes to the capital structure literature by developing a new approach based on data aggregation. This study is the first, to the authors’ knowledge, to find a positive effect of the firm's non-financial risk on target leverage among rated firms. The authors argue that the benefit of a better credit rating is an increasing function of the rating itself. The authors also contribute to the literature on the impact of credit ratings on the capital structure choices of the firm.