This study contributes to understanding the main determinants of sovereign ratings for developing countries making use of information from Standard & Poor's, Moody's, and Fitch. Based on a sample of 40 countries for the period 1994 to 2013 and panel data approach, we extended previous works in the literature by including new economic aspects, as well as, new institutional and governance variables (e.g. inflation targeting, financial openness, democracy, corruption, etc.). The findings denote that, besides the traditional macroeconomic variables, adoption of inflation targeting, financial openness, democracy, law and order, and less corruption are important to improve the sovereign ratings.

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PurposeCredit rating agencies (CRAs) are perceived as highly influential in the financial system since their announcements can affect several players in the financial markets, from big private financial and non-financial companies and their financial markets experts to sovereign states. In this sense, this study investigates whether sovereign credit news issued by CRAs (measured by comprehensive credit rating (CCR) variables) affect the uncertainties about the exchange rate in the future (captured by the disagreement about exchange rate expectations). The study is relevant once there is evidence indicating that CRAs' assessments are responsible for affecting international capital flows and, thus, sovereign rating changes can affect the expectations formation process regarding the exchange rate. In addition, there is evidence indicating that the disagreement about exchange rate expectations affects the disagreement about inflation expectations, which brings consequences to policymakers.Design/methodology/approachThe dependent variables are the disagreement in expectations about the Brazilian exchange rate for different forecast horizons, 12, 24 and 36 months ahead and the first principal component of theses series. On the other hand, the CCR variables are built upon the long-term foreign-currency Brazilian bonds ratings, outlooks and credit watches provided by the main CRAs. Estimates are obtained using ordinary least squares (OLS) and generalized method of moments (GMM); a dynamic analysis is performed using vector-autoregressive (VAR) through impulse-response functions.FindingsNegative (positive) sovereign credit news, given by a rating downgrade (upgrade) and/or a negative (positive) outlook/watch status, increase (decrease) the disagreement about exchange rate expectations. This result holds for all disagreement and CCR variables.Practical implicationsThe study brings practical implications to both private agents (mainly financial market experts) and policymakers. An important practical implication of the study concerns the ability of CRAs to affect the expectations formation process of financial market experts regarding the future behavior of the exchange rate. When a CRA issues a signal of improvement in a country's sovereign rating, this signal reflects the perception of improvement in macroeconomic fundamentals and reduction of uncertainties about the country's ability to honor its financial obligations, which therefore, facilitates the expectations formation process, causing a reduction in the disagreement about the exchange rate expectations. With respect to the consequences for policymakers, they will have more difficulty in guiding expectations in a country with a worse sovereign risk rating, where agents have difficulties in forming expectations and the disagreement in expectations is greater.Originality/valueThe study is the first to analyze the impact of CRAs' announcements on the disagreement about exchange rate expectations. Moreover, it connects the literature that investigates the effects of sovereign credit news on the economy with the literature that examines the main determinants of disagreement in expectations about macroeconomic variables.

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.