AN ANALYSIS OF SOVEREIGN CREDIT RATINGS USING RANDOM FOREST

The study's objective was to build a novel approach for analysing sovereign credit ratings. Quarterly data from 1999 to 2020 were analysed using a random forest model generated from decision trees. Macroeconomic indicators and sovereign credit ratings (SCR) “were used from the three major credit rating agencies, Fitch, Moody's, and Standard & Poor's. The random forest model is a machine learning methodology that analyses and forecasts data using categorisation algorithms. The random forest classifier and analyser fared admirably well when classifying and analysing sovereign credit ratings. The data imply that the most relevant variables for estimating and ranking credit ratings are household debt to disposable income, exchange rates, and inflation. The data indicate that increases in economic metrics such as Real Effective Exchange Rates, Gross Domestic Product Growth, Household Debt to Disposable Income, and Consumer Price Index Headline result in ratings shift in favour of the borrower. Authorities should maintain a low HDDI, stabilised inflation, and a stronger currency to encourage sovereign rating upgrades.s