In this paper the forecasting performance of popular leading indicators for the German business cycle is investigated. Survey based indicators (ifo business climate, ZEW index of economic sentiment) and composite leading indicators (Handelsblatt, Frankfurter Allgemeine Zeitung, Commerzbank) are considered. The analysis points to a significant relationship of the indicators to the business cycle within the sample period, as measured by the direction of causality. But, their out-of-sample forecasts do not improve the autoregressive benchmark. This result may be caused by structural breaks in the out-of-sample period. As combinations of forecasts tend to be more robust against such shifts, pooled forecasts are constructed using different methods of aggregation, including linear combinations of forecasts and common factor models. In contrast to the single indicator approach, the combined indicator forecasts are able to beat the benchmark at each forecasting horizon. Therefore, the analysis points to the usefulness of pooling information in order to get more reliable forecasts.