This article assesses the predictive ability of asset prices relative to other variables in forecasting inflation and real GDP growth in South Africa. A total of 42 asset and non-asset predictor variables are considered. Forecasts of inflation and real GDP growth are computed using both individual predictor autoregressive distributed lag (ARDL) models, forecast combination approaches, as well as large scale models. The large scale data models considered include Bayesian vector autoregressive models and classical and Bayesian univariate and multivariate factor augmented vector autoregressive models. The models are estimated for an in-sample of 1980:Q2 to 1999:Q4, and then one- to eight-step-ahead forecasts for inflation and real GDP growth are evaluated over the 2000:Q1 to 2010:Q2 out-of-sample period. Principle Component forecast combination models are found to produce the most accurate out-of-sample forecasts of inflation and real GDP growth relative to the other combination and more sophisticated models considered. Asset prices are found to contain particularly useful information for forecasting inflation and real GDP growth at certain horizons. Asset prices are however found to be stronger predictors of inflation, particularly in the long run.