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.