This paper examines the cross-sectional properties of stock return forecasts based on Fama–MacBeth regressions using all firms contained in the STOXX Europe 600 index during the September 1999–December 2018 period. Our estimation approach is strictly out of sample, mimicking an investor who exploits both historical and real-time information on multiple firm characteristics to predict returns. The models capture a substantial amount of the cross-sectional variation in true expected returns and generate predictive slopes close to one, i.e., the forecast dispersion mostly reflects cross-sectional variation in true expected returns. The return predictions translate into high value added for investors. For an active trading strategy, we find strong market outperformance net of transaction costs based on a variety of performance measures.