Based on monthly data covering the period from 1987 to 2019, we analyse whether cross-sectional moments of stock market returns may provide information about the future position of the German business cycle. We apply in-sample forecasting regressions with and without leading indicators as control variables, pseudo-out-of-sample exercises, Probit models, and Autoregressive Distributed Lag Models. We find in-sample predictive power of the first and third cross-section moments for the future growth of industrial production, even if one controls for well-established leading indicators for the German business cycle. In addition, out-of-sample tests show that these variables reduce the relative Mean Squared Error compared to benchmark models. The results for the second moment are less promising. Also, we do not observe a long-run relation between the moment series and industrial production.