Abstract:
A reverse event study approach was used to investigate how the South African sovereign bond yield curve reacts to headline news. The change in daily yields, calculated as the difference between the natural log of zero-coupon yields on consecutive business days, were used in the analysis. Dates of abnormal daily yield changes were identified using GARCH models. News items for the sample period were classified into categories using supervised machine learning. A regression model was fitted to determine the link between the abnormal yield changes and the news categories. The results indicated that, for abnormal increases in yield (negative news), political news had an impact on all nodes. For abnormal decreases in yield (positive news), economic news had the greatest impact on the 10-year and political news on the 15- and 20-year nodes of the yield curve.
