{"title":"基于支配性粗糙集方法的主权评级分析","authors":"Ayrton Benedito Gaia do Couto, L. Gomes","doi":"10.2478/fcds-2020-0001","DOIUrl":null,"url":null,"abstract":"Abstract The classifications of risk made by international rating agencies aim at guiding investors when it comes to the capacity and disposition of the evaluated countries to honor their public debt commitments. In this study, the analysis of economic variables of sovereign rating, in a context of vagueness and uncertainty, leads the inference of patterns (multi-criteria rules) by following the Dominance-based Rough Set Approach (DRSA). The discovery of patterns in data may be useful for subsidizing foreign investment decisions in countries; and this knowledge base may be used in rule-based expert systems (learning from training examples).The present study seeks to complement the analysis produced by an international credit rating agency, Standard & Poor’s (S&P), for the year 2018.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"45 1","pages":"16 - 3"},"PeriodicalIF":1.8000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Sovereign Rating Analysis through the Dominance-Based Rough Set Approach\",\"authors\":\"Ayrton Benedito Gaia do Couto, L. Gomes\",\"doi\":\"10.2478/fcds-2020-0001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The classifications of risk made by international rating agencies aim at guiding investors when it comes to the capacity and disposition of the evaluated countries to honor their public debt commitments. In this study, the analysis of economic variables of sovereign rating, in a context of vagueness and uncertainty, leads the inference of patterns (multi-criteria rules) by following the Dominance-based Rough Set Approach (DRSA). The discovery of patterns in data may be useful for subsidizing foreign investment decisions in countries; and this knowledge base may be used in rule-based expert systems (learning from training examples).The present study seeks to complement the analysis produced by an international credit rating agency, Standard & Poor’s (S&P), for the year 2018.\",\"PeriodicalId\":42909,\"journal\":{\"name\":\"Foundations of Computing and Decision Sciences\",\"volume\":\"45 1\",\"pages\":\"16 - 3\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Foundations of Computing and Decision Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/fcds-2020-0001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Foundations of Computing and Decision Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/fcds-2020-0001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Sovereign Rating Analysis through the Dominance-Based Rough Set Approach
Abstract The classifications of risk made by international rating agencies aim at guiding investors when it comes to the capacity and disposition of the evaluated countries to honor their public debt commitments. In this study, the analysis of economic variables of sovereign rating, in a context of vagueness and uncertainty, leads the inference of patterns (multi-criteria rules) by following the Dominance-based Rough Set Approach (DRSA). The discovery of patterns in data may be useful for subsidizing foreign investment decisions in countries; and this knowledge base may be used in rule-based expert systems (learning from training examples).The present study seeks to complement the analysis produced by an international credit rating agency, Standard & Poor’s (S&P), for the year 2018.