scholarly journals DETERMINANTS OF SOVEREIGN CREDIT RATINGS: AN APPLICATION OF THE NAÏVE BAYES CLASSIFIER

2020 ◽  
Vol 8 (4) ◽  
pp. 279-299
Author(s):  
Oliver Takawira ◽  
◽  
John W. Muteba Mwamba ◽  
Keyword(s):  
Cross Validation ◽  
Credit Rating ◽  
Naive Bayes ◽  
Credit Ratings ◽  
Naïve Bayes ◽  
Test Set ◽  
Macroeconomic Variables ◽  
Rating Agencies ◽  
Sovereign Credit ◽  
Sovereign Credit Rating

This is an analysis of South Africa’s (SA) sovereign credit rating (SCR) using Naïve Bayes, a Machine learning (ML) technique. Quarterly data from 1999 to 2018 of macroeconomic variables and categorical SCRs were analyzed and classified to predict and compare variables used in assigning SCRs. A sovereign credit rating (SCR) is a measurement of a sovereign government’s ability to meet its financial debt obligations. The differences by Credit Rating Agencies (CRA) on rating grades on similar firms and sovereigns have raised questions on which elements truly determine credit ratings. Sovereign ratings were split into two (2) categories that is less stable and more stable. Through data cross-validation for supervised learning, the study compared variables used in assessing sovereign rating by the major rating agencies namely Fitch, Moody’s and Standard and Poor’s. Cross-validation splits the dataset into train set and test set. The research applied cross-validation to reduce the effects of overfitting on the Naïve Bayes Classification model. Naïve Bayes Classification is a Machine-learning algorithm that utilizes the Bayes theorem in classification of objects by following a probabilistic approach. All variables in the data were split in the ratio of 80:20 for the train set and test set respectively. Naïve Bayes managed to classify the given variables using the two SCR categories that is more stable and less stable. Variables classified under more stable indicates that ratings are high or favorable and those for less stable show unfavorable or low ratings. The findings show that CRAs use different macroeconomic variables to assess and assign sovereign ratings. Household debt to disposable income, exchange rates and inflation were the most important variables for estimating and classifying ratings.

scholarly journals Determination of Sovereign Credit Rating Model for European Countries

2021 ◽  
Vol 5 (3) ◽  
Author(s):  
Isik Akin
Keyword(s):  
Financial Crisis ◽  
Credit Rating ◽  
Global Financial Crisis ◽  
Credit Ratings ◽  
Developed Countries ◽  
Credit Rating Agencies ◽  
Rating Agencies ◽  
Sovereign Credit ◽  
Rating Model ◽  
Sovereign Credit Rating

Credit rating agencies play a key role in financial markets, as they help to reduce asymmetric information among market participants via credit ratings. The credit ratings determined by the credit rating agencies reflect the opinion of whether a country can fulfil the liability or its credit reliability at a particular time. Therefore, credit ratings are a very valuable tool, especially for investors. In addition, the issue that credit rating agencies are generally criticised is that they are unsuccessful in times of financial crisis. Credit rating methodologies of credit rating agencies have been subject to intense criticism, especially after the 2007/08 Global Financial Crisis. Some of the criticised issues are that credit rating agencies’ methodologies are not transparent; they are unable to make ratings on time, and they make incorrect ratings. In order to create a more reliable credit rating methodology, the credit rating industry and the ratings determined by rating agencies need to be critically examined and further investigated in this area. For this reason, in this study credit rating model has been developed for countries. Supervisory and regulatory variables, political indicators and macroeconomic factors were used as independent variables for the sovereign credit rating model. As a result of the study, the new sovereign credit rating calculates exactly the same credit rating with Fitch Rating Agency for developed countries, but there are 1 or 2 points differences for developing countries. In order to better understand the reason for these differences, credit rating agencies need to make their methodologies more transparent and disclose them to the public.

scholarly journals Sistem Informasi Posyandu Ibu Hamil dengan Penerapan Klasifikasi Resiko Kehamilan Menggunakan Metode Naïve Bayes

2018 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Qomariyatul Hasanah ◽  
Anang Andrianto ◽  
Muhammad Arief Hidayat
Keyword(s):  
Cross Validation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Set ◽  
Test Set ◽  
Fold Cross Validation

Sistem informasi posyandu ibu hamil dapat mengelola data kesehatan ibu hamil yang berkaitan dengan faktor resiko kehamilan. Faktor resiko kehamilan berdasarkan ketentuan Kartu Skor Poedji Rochyati (KSPR) digunakan bidan untuk menentukan resiko kehamilan dengan memberikan skor pada masing-masing parameter. KSPR memiliki kelemahan tidak dapat memberikan skor pada parameter yang belum pasti sehingga jika belum diketahui dengan pasti maka dianggap tidak terjadi. Konsep membaca pola data yang diadopsi dari teknik datamining menggunakan metode klasifikasi naive bayes dapat menjadi alternatif untuk kelemahan KSPR tersebut yaitu dengan mengklasifikasikan resiko kehamilan. Metode naïve bayes menghitung probabilitas parameter tertentu berdasarkan data pada periode sebelumnya yang telah ditentukan sebagai data training, berdasarkan hasil perhitungan tersebut dapat diketahui resiko kehamilan secara tepat sesuai parameter yang telah diketahui. Metode naïve bayes dipilih karena memiliki tingkat akurasi yang cukup tinggi daripada metode klasifikasi lainnya. Sistem informasi ini dibangun berbasis website agar dapat diakses secara mudah oleh beberapa posyandu yang berbeda tempat. Sistem dibangun mengadopsi dari model Waterfall. Sistem informasi posyandu ibu hamil dirancang dan dibangun dengan tiga (3) hak akses yaitu admin, bidan dan kader dengan masing-masing fitur yang dapat memudahkan penggunanya. Hasil dari penelitian ini adalah sistem informasi posyandu ibu hamil dengan penerapan klasifikasi resiko kehamilan menggunakan metode naïve bayes, dengan tingkat akurasi ketika menggunakan 17 atribut didapatkan 53.913%, 19 atribut didapatkan 54.348%, , 21 atribut didapatkan 54.783%, dan 22 atribut didapatkan 56.957%. Tingkat akurasi klasifikasi diperoleh menggunakan metode pengujian menggunakan Ten-Fold Cross Validation dimana training set dibagi menjadi 10 kelompok, jika kelompok 1 dijadikan test set maka kelompok 2 hingga 10 menjadi training set. Kata Kunci: Posyandu, Resiko Kehamilan, Waterfall, Datamining, Klasifikasi, Naïve bayes

A comparative study of economic growth as a key determinant of sovereign credit ratings in Africa

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Misheck Mutize ◽  
McBride Peter Nkhalamba
Keyword(s):  
Economic Growth ◽  
Credit Rating ◽  
Credit Ratings ◽  
Credit Rating Agencies ◽  
African Union ◽  
African Countries ◽  
Content Type ◽  
Rating Agencies ◽  
Sovereign Credit ◽  
Sovereign Credit Ratings

PurposeThis study is a comparative analysis of the magnitude of economic growth as a key determinant of long-term foreign currency sovereign credit ratings in 30 countries in Africa, Europe, Asia and Latin America from 2010 to 2018.Design/methodology/approachThe analysis applies the fixed effects (FE) and random effects (RE) panel least squares (PLS) models.FindingsThe authors find that the magnitude economic coefficients are marginally small for African countries compared to other developing countries in Asia, Europe and Latin America. Results of the probit and logit binary estimation models show positive coefficients for economic growth sub-factors for non-African countries (developing and developed) compared to negative coefficients for African countries.Practical implicationsThese findings mean that, an increase in economic growth in Africa does not significantly increase the likelihood that sovereign credit ratings will be upgraded. This implies that there is lack of uniformity in the application of the economic growth determinant despite the claims of a consistent framework by rating agencies. Thus, macroeconomic factors are relatively less important in determining country's risk profile in Africa than in other developing and developed countries.Originality/valueFirst, studies that investigate the accuracy of sovereign credit rating indicators and risk factors in Africa are rare. This study is a key literature at the time when the majority of African countries are exploring the window of sovereign bonds as an alternative funding model to the traditional concessionary borrowings from multilateral institutions. On the other hand, the persistent poor rating is driving the cost of sovereign bonds to unreasonably high levels, invariably threatening their hopes of diversifying funding options. Second, there is criticism that the rating assessments of the credit rating agencies are biased in favour of developed countries and there is a gap in literature on studies that explore the whether the credit rating agencies are biased against African countries. This paper thus explores the rationale behind the African Union Decision Assembly/AU/Dec.631 (XXVIII) adopted by the 28th Ordinary Session of the African Union held in Addis Ababa, Ethiopia in January 2017 (African Union, 2017), directing its specialized governance agency, the African Peer Review Mechanism (APRM), to provide support to its Member States in the field of international credit rating agencies. The Assembly of African Heads of State and Government highlight that African countries are facing the challenges of credit downgrades despite an average positive economic growth. Lastly, the paper makes contribution to the argument that the majority of African countries are unfairly rated by international credit rating agencies, raising a discussion of the possibility of establishing a Pan-African credit rating institution.

The geographical and income differences in the determinants of African sovereign credit ratings

2020 ◽  
Vol 11 (4) ◽  
pp. 609-624
Author(s):  
Ilse Botha ◽  
Marinda Pretorius
Keyword(s):  
Credit Rating ◽  
Credit Ratings ◽  
African Countries ◽  
Content Type ◽  
Rating Agencies ◽  
Sovereign Credit ◽  
Additional Information ◽  
Income Groups ◽  
Sovereign Credit Ratings ◽  
Developmental Indicators

PurposeThe importance of obtaining a sovereign credit rating from an agency is still underrated in Africa. Literature on the determinants of sovereign credit ratings in Africa is scarce. The purpose of this research is to determine what the determinants are for sovereign credit ratings in Africa and whether these determinants differ between regions and income groups.Design/methodology/approachA sample of 19 African countries' determinants of sovereign credit ratings are compared between 2007 and 2014 using a panel-ordered probit approach.FindingsThe findings indicated that the determinants of sovereign credit ratings differ between African regions and income groups. The developmental indicators were the most significant determinants across all income groups and regions. The results affirm that the identified determinants in the literature are not as applicable to African sovereigns, and that developmental variables and different income groups and regions are important determinants to consider for sovereign credit ratings in Africa.Originality/valueThe results affirm that the identified determinants in the literature are not as applicable to African sovereigns, and that developmental variables and different income groups and regions are important determinants to consider for sovereign credit ratings in Africa. Rating agencies follow the same rating assignment process for developed and developing countries, which means investors will have to supplement the allocated credit rating with additional information. Africa can attract more investment if African countries obtain formal, accurate sovereign credit ratings, which take the characteristics of the continent into consideration.

Sovereign credit rating and contagion effects on financial markets of Asian region

2020 ◽  
Vol 2020 (6) ◽  
pp. 48-69
Author(s):  
Natalia Pivnitskaya ◽  
Tamara Teplova
Keyword(s):  
Financial Markets ◽  
Credit Rating ◽  
Contagion Effect ◽  
Rating Agencies ◽  
Sovereign Credit ◽  
Asian Region ◽  
Contagion Effects ◽  
Rating Changes ◽  
The Impact ◽  
Sovereign Credit Rating

This article studies the contagion effects on the emerging financial markets of the Asian region. The contagion effect is manifested in the change of interconnection degree of financial markets after the shock in one of the countries of the region. In the paper, we consider the information on potential or actual change in sovereign credit rating as a shock leading to a contagion effect. Our sample includes evidence from 7 Asian countries covering the period from 2000 to 2018. We use the DCC-GARCH model which allows us to take into account the peculiarities of financial data behavior. We intend to show the effect of inconsistencies in ratings assigned by various agencies on strengthening or weakening the processes of contagion on Asia’s stock markets. We also study the impact of historical inconsistencies between credit rating outlooks and actual rating changes on the level of «trust» to credit outlooks in the future. In assessing the impact of discrepancies we assume that the market remembers recent events better than more distant in time. We were able to confirm the impact of inconsistencies in the ratings given by different rating agencies for China, Hong Kong, and India. In addition, we found that the presence of inconsistencies between the outlooks and actual rating updates in the past tend to weaken the trust regarding positive outlooks rather than negative ones.

scholarly journals Sovereign Credit Ratings and Asian Financial Markets

2021 ◽  
Vol 24 (1) ◽  
pp. 165-181
Author(s):  
Khansa Pervaiz ◽  
Zuzana Virglerová ◽  
Muhammad Asif Khan ◽  
Usman Akbar ◽  
József Popp
Keyword(s):  
Financial Markets ◽  
Financial Market ◽  
Credit Rating ◽  
Credit Ratings ◽  
Market Development ◽  
Sovereign Credit ◽  
Sovereign Credit Ratings ◽  
Financial Market Development ◽  
Sovereign Credit Rating

Each region/country seeks to become more efficient to gain the confidence of potential investors. Most of the Asian economies are categorized as emerging markets, where the role of financial markets has even become more intensified to provide financial services to increasing economic and financial activities. Asian financial market has momentously suffered during the Asian, and global financial crisis. The mass destruction was mainly caused due to the mounting uncertainty, which spillover throughout the region, where investors lost their confidence. Considering the pivotal economic role of financial markets, and implications evolve due to sovereign credit rating announcements, this study aims to model the role of sovereign credit rating announcements by Standard and Poor’s, and Moody’s on financial market development of the Asian region. For 24 Asian countries/regions, we perform a regression analysis on sovereign credit rating changes based on financial market development index and its factors. The findings of Driscoll Kraay’s robust estimator reveals that improvement in sovereign credit rating score enhances the financial market development in the region. Moreover, we applied several robustness checks, such as alternative estimators, alternative measures, and three sub-dimensions of financial market development. According to the findings from these robustness checks, the positive impact of sovereign credit ratings on financial market development in the region is robust. Unlike prior literature (which is confined to the event study approach), this study utilizes the historical grades to establish the relationship under the standard error clustering approach. Due to the diversity of investors’ speculations, we propose a micro-level extension of the present model to overcome a difference in country policy.

scholarly journals KEY DETERMINANTS OF LITHUANIA’S SOVEREIGN CREDIT RATING

Ekonomika ◽  
2011 ◽  
Vol 90 (1) ◽  
pp. 73-84
Author(s):  
Aušra Pačebutaitė
Keyword(s):  
Credit Rating ◽  
Credit Ratings ◽  
Foreign Debt ◽  
Economic Downturn ◽  
Eastern European ◽  
Sovereign Credit ◽  
Sovereign Credit Ratings ◽  
The Face ◽  
External Borrowing ◽  
Sovereign Credit Rating

The topic concerning the determinants affecting sovereign credit ratings of a country became extremely relevant after the recent economic turbulence which brought relentless downgrades, especially for Central and Eastern European (CEE) countries in their sovereign credit ratings. In the face of economic downturn around the world, causing the reduced availability of global capital flows and the appetite for risk, it becomes essential for the countries to secure the high market grade ratings in order to be able to issue foreign debt to ensure the solvency of the country’s finances and to pursue a sound economic growth.The aim of the study was to elucidate the key determinants of the Lithuanian sovereign rating during the financial turbulence of 2008 and to explain their importance and dynamics through external borrowing costs of the country.

scholarly journals Sovereign Credit Rating Mismatches

2018 ◽  
pp. 49-70
Author(s):  
António Afonso ◽  
André Albuquerque
Keyword(s):  
Credit Rating ◽  
Credit Ratings ◽  
External Debt ◽  
Gdp Per Capita ◽  
Sovereign Credit ◽  
Sovereign Credit Ratings ◽  
Relevant Variables ◽  
Per Capita ◽  
Investment Grade ◽  
Sovereign Credit Rating

We study the factors behind ratings mismatches in sovereign credit ratings from different agencies, for the period 1980‑‑2015. Using random effects ordered and simple probit approaches, we find that structural balances and the existence of a default in the last ten years were the least significant variables. In addition, the level of net debt, budget balances, GDP per capita and the existence of a default in the last five years were found to be the most relevant variables for rating mismatches across agencies. For speculative‑‑grade ratings, a default in the last two or five years decreases the rating difference between S&P and Fitch. For the positive rating difference between S&P and Moody’s, and for investment‑‑grade ratings, an increase in external debt leads to a smaller rating gap between the two agencies.

scholarly journals Stock Market Reactions To Sovereign Credit Rating Changes: Evidence From Four European Countries

2014 ◽  
Vol 30 (3) ◽  
pp. 953 ◽  
Author(s):  
Ibrahim Fatnassi ◽  
Zied Ftiti ◽  
Habib Hasnaoui
Keyword(s):  
Sovereign Debt ◽  
Credit Rating ◽  
Debt Crisis ◽  
Regression Equations ◽  
Market Reactions ◽  
Rating Agencies ◽  
Sovereign Credit ◽  
Rating Changes ◽  
Stock Market Reactions ◽  
Sovereign Credit Rating

We analyze the reactions of the returns of four European stock markets to sovereign credit rating changes by Fitch, Moodys, and Standard and Poors (S&P) during the period from June 2008 to June 2012 using panel regression equations. We find that (i) upgrades and downgrades affect both own country returns and other countries returns, (ii) market reactions to foreign downgrades are stronger during the sovereign debt crisis period, and (iii) negative news from rating agencies are more informative than positive news.

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