Reconstruction Rating Model of Sovereign Debt by Logical Analysis of Data

TL;DR

This paper presents a logical analysis-based model for reconstructing sovereign debt ratings using Boolean functions, achieving high accuracy in matching and predicting ratings for countries based on World Bank data.

Contribution

It introduces a novel Boolean function-based approach to reconstruct and improve sovereign debt ratings, including an algorithm for error correction and rating estimation.

Findings

Achieved 98% accuracy in training ratings

Achieved 84% accuracy in test ratings

Developed decision trees for each year from 2012 to 2015

Abstract

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.