Impact of economic and socio-political risk factors on sovereign credit ratings

IF 7.4 1区管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Processing & Management Pub Date : 2024-11-05 DOI:10.1016/j.ipm.2024.103943

Abhinav Goel, Archana Singh

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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.

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经济和社会政治风险因素对主权信用评级的影响

主权信用评级（SCR）有助于国际投资者对主权国家或主权国家内实体的贷款风险进行定价，从而影响资本流入一个经济体的成本和可用性。国际信用评级机构（CRAs - 穆迪、S&P 和惠誉）在确定一个国家的 SCR 时，会同时考虑定量（经济）和定性（社会政治）因素。然而，SCR 领域的研究主要集中在定量因素上，对定性因素的重视程度较低。为了实现这些目标，本研究开发了两个数据集，涵盖 55 个国家，并汇编了穆迪和惠誉提供的 10 年（2011-2020 年）SCR 数据以及各种定量和定性因素的值。数据集包括从 32 个自变量中获得的 18,700 个数据点；其中 17 个为定量变量，15 个为定性变量。数据来自世界银行、国际货币基金组织、联合国等机构。对这两个数据集进行了相关性分析，然后应用 Extra Tree 分类器预测 SCR。全面的结果分析表明，在决定 SCR 时，定性因素（单独或作为一个群体）比定量因素更重要。结果还表明，在确定 SCR 时，存在对高收入国家的偏见，银行参数的重要性适中。此外，使用 Extra Tree 分类器对数据集 1 和数据集 2 的预测准确率分别为 97% - 98%。与现有工作的比较分析证明了本工作的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Information Processing & Management 工程技术-计算机：信息系统

CiteScore

17.00

自引率

11.60%

发文量

276

审稿时长

39 days

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. We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.