Abstract
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. We
employ various methods of forecast combination, bootstrap aggregation (bagging), diffusion
index (principal component) and Bayesian regressions to allow for a simultaneous role of the
variables under consideration, besides individual predictive regressions. We assess both the
statistical and economic significance of the individual predictive regressions, combination
methods, bagging, principal components and Bayesian regressions. Our results show that
forecast combination methods and principal component regressions improve the predictability
of the equity premium relative to the benchmark autoregressive model of order one (AR(1)).
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, both in terms of statistical (forecasting) and economic (utility)
gains.