Examining the South African Economic and Social Renaissance

Group project for the WorldQuant University module, risk management.

The following paper has a look at the historical context of South Africa while attempting to identify key economic drivers. To do this a Random Forest was fit to the data in order to leverage the power of machine learning and determine the feature importance. It was found that Investment GFCF is a key driver and it was confirmed by applying a linear regression in the traditional fashion.

Introduction

South African history provides great context for a case study in risk management. Analysts have the opportunity to investigate the effects of sociopolitical and economic hardships during the apartheid regime followed by an African Renaissance that has been criticized for implementing so called neo-liberal economic policies.

The following sections are split up as follows: section 2 acts as a literature review by analyzing the historical contexts and economic drivers of South Africa's economy. Section 3 summarizes section 2 and discusses the key economic drivers. Section 4 describes the data. Section 5 reviews the methodology of linear regression and random forests in the context of modeling financial time series. Section 6 discusses our findings. Section 7 Concludes.