We study the performance of conditional asset pricing models and multifactor models in explaining the German cross-section of stock returns. We focus on several variables, which (according to previous research) are associated with market expectations on future market excess returns or business cycle conditions. Our results suggest that the empirical performance of the Capital Asset Pricing Model (CAPM) can be improved when allowing for time-varying parameters of the stochastic discount factor. A conditional CAPM using the term spread explains the returns on our size and book-to-market sorted portfolios about as well as the Fama-French three-factor model and performs best in terms of the Hansen-Jagannathan distance. Structural break tests do not necessarily indicate parameter instability of conditional model specifications. Another major finding of the paper is that the Fama-French model – despite its generally good cross-sectional performance – is subject to model instability. Unconditional models, however, do a better job than conditional ones at capturing time-series predictability of the test portfolio returns.