英文标题：
《Reconstruction Rating Model of Sovereign Debt by Logical Analysis of
  Data》
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作者：
Elnaz Gholipour (1), B\\\'ela Vizv\\\'ari (1) and Zolt\\\'an Lakner (2) ((1)
  Eastern Mediterranean University, (2) St. Stephen University)
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Computer Science        计算机科学
二级分类：Numerical Analysis        数值分析
分类描述：cs.NA is an alias for math.NA. Roughly includes material in ACM Subject Class G.1.
cs.na是Math.na的别名。大致包括ACM学科类G.1的材料。
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一级分类：Mathematics        数学
二级分类：Numerical Analysis        数值分析
分类描述：Numerical algorithms for problems in analysis and algebra, scientific computation
分析和代数问题的数值算法，科学计算
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一级分类：Mathematics        数学
二级分类：Optimization and Control        优化与控制
分类描述：Operations research, linear programming, control theory, systems theory, optimal control, game theory
运筹学，线性规划，控制论，系统论，最优控制，博弈论
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
  Sovereign debt ratings provided by rating agencies measure the solvency of a country, as gauged by a lender or an investor. It is an indication of the risk involved in investment, and should be determined correctly and in a well timed manner. The present study reconstructs sovereign debt ratings through logical analysis of data, which is based on the theory of Boolean functions. It organizes groups of countries according to twenty World Bank defined variables for the period 2012 till 2015. The Fitch Rating Agency, one of the three big global rating agencies, is used as a case study. An approximate algorithm was crucial in exploring the rating method, in correcting the agencys errors, and in determining the estimated rating of otherwise non rated countries. The outcome was a decision tree for each year. Each country was assigned a rating. On average, the algorithm reached almost ninety eight percentage matched ratings in the training set, and was verified by eighty four percentage in the test set. This was a considerable achievement.
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