{"title":"Impact of economic and socio-political risk factors on sovereign credit ratings","authors":"Abhinav Goel, Archana Singh","doi":"10.1016/j.ipm.2024.103943","DOIUrl":null,"url":null,"abstract":"<div><div>Sovereign Credit Ratings (SCRs) help international investors price the risk of lending to sovereigns or entities domiciled within that sovereign, thereby impacting cost and availability of capital flows into an economy. The international credit rating agencies (CRAs - Moody's, S&P and Fitch) consider both quantitative (economic) and qualitative (socio-political) factors while determining the SCR of a country. However, research in the field of SCR has focussed largely on quantitative factors giving lesser importance to qualitative factors. The present work analyses the linkage of banking sector risks and SCR, the bias in rating process towards high-income nations, and the impact of both quantitative and qualitative factors to provide a more holistic picture of the determinants of SCR.</div><div>To attain these objectives, the present work develops two datasets covering 55 countries and compiles the data for 10 years (2011–2020) in terms of SCR obtained from Moody's and Fitch, and the values for various quantitative and qualitative factors. The dataset comprises of 18,700 data points obtained from 32 independent variables; 17 are quantitative and 15 qualitative. Some qualitative factors are also introduced which were not used earlier in SCR literature The data has been collated from World Bank, International Monetary Fund, United Nations etc. Correlation analysis has been performed on these two datasets followed by the application of Extra Tree Classifier for predicting SCR. Thorough result analysis indicates that qualitative factors, individually and as a group, are more important in determining SCR than quantitative factors. The results also indicate the presence of bias towards high-income nations and moderate importance of banking parameters in determination of SCR. Further, the use of Extra Tree Classifier gives a prediction accuracy of 97 % - 98 % for dataset 1 and dataset 2, respectively. Comparative analysis with existing work proves the efficacy of the present work.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing & Management","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306457324003029","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Sovereign Credit Ratings (SCRs) help international investors price the risk of lending to sovereigns or entities domiciled within that sovereign, thereby impacting cost and availability of capital flows into an economy. The international credit rating agencies (CRAs - Moody's, S&P and Fitch) consider both quantitative (economic) and qualitative (socio-political) factors while determining the SCR of a country. However, research in the field of SCR has focussed largely on quantitative factors giving lesser importance to qualitative factors. The present work analyses the linkage of banking sector risks and SCR, the bias in rating process towards high-income nations, and the impact of both quantitative and qualitative factors to provide a more holistic picture of the determinants of SCR.
To attain these objectives, the present work develops two datasets covering 55 countries and compiles the data for 10 years (2011–2020) in terms of SCR obtained from Moody's and Fitch, and the values for various quantitative and qualitative factors. The dataset comprises of 18,700 data points obtained from 32 independent variables; 17 are quantitative and 15 qualitative. Some qualitative factors are also introduced which were not used earlier in SCR literature The data has been collated from World Bank, International Monetary Fund, United Nations etc. Correlation analysis has been performed on these two datasets followed by the application of Extra Tree Classifier for predicting SCR. Thorough result analysis indicates that qualitative factors, individually and as a group, are more important in determining SCR than quantitative factors. The results also indicate the presence of bias towards high-income nations and moderate importance of banking parameters in determination of SCR. Further, the use of Extra Tree Classifier gives a prediction accuracy of 97 % - 98 % for dataset 1 and dataset 2, respectively. Comparative analysis with existing work proves the efficacy of the present work.
期刊介绍:
Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing.
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