Journal of Information Technology & Software Engineering
Open Access

Sovereign credit ratings analysis using the Logistic Regression Model

10^th International Conference on Data Science and Machine Learning Applications

August 08, 2022 | Webinar

Oliver Takawira

Department of Finance and Investment Management (DFIM) ellore Institute of Technology

Scientific Tracks Abstracts: J Inform Tech Softw Eng

Abstract :

This study is an empirical investigation of South Africa’s (SA) sovereign credit ratings (SCR) using Logistic regression to predict and forecast SCRs. Quarterly data from 1999 to 2020 of macroeconomic variables and SCRs were analyzed and classified to predict future ratings and compare variables used in assigning SCRs. The study found that Credit Rating Agencies (CRAs) use different macroeconomic variables and unique models to assess and assign sovereign ratings pointing out that Household debt to disposable income was the most influential variable on sovereign ratings. Household debt to disposable income, exchange rates and inflation were the most important variables for estimating ratings.

Biography :

Oliver Takawira Live, work and study in South Africa. Experienced with Lecturer: Department of Finance and Investment Management (DFIM), College of Business and Economics (CBE) - University of Johannesburg, South Africa