I employ a parsimonious model with learning, but without conditioning information, to extract time‐varying measures of market‐risk sensitivities, pricing errors and pricing uncertainty. The evolution of these quantities has interesting implications for macroeconomic dynamics. Parameters estimated for US equity portfolios display significant low‐frequency fluctuations, along patterns that change across size and book‐to‐market stocks. Time‐varying betas display superior predictive accuracy for returns against constant and rolling‐window OLS estimates. As to the relationship of betas with business‐cycle variables, value stocks’ betas move pro‐cyclically, unlike those of growth stocks. Investment growth, rather than consumption, predicts the betas of value and small‐firm portfolios.