Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors

This paper uses a predictive regression framework to examine the out-of-sample predictability of South Africa’s equity premium, using a host of financial and macroeconomic variables. Past studies tend to suggest that the predictors on their own fail to deliver consistent out-of-sample forecast gains relative to the historical average (random walk model). We therefore employ various methods of forecast combination, bootstrap aggregation (bagging), principal component and Bayesian regressions to allow for a simultaneous role of the variables under consideration. Our results show that forecast combination methods and principal component regressions improve the predictability of the equity premium relative to the benchmark random walk model. However, the Bayesian predictive regressions are found to be the standout performers with the models outperforming the individual regressions, forecast combination methods, bagging and principal component regressions.