This paper explores the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a parameter learning approach for sequential estimation, allowing for belief revisions. Empirically, we find that mixed-frequency models improve predictability, not only because of the combination of predictors with different frequencies but also due to the preservation of the time-variation in the volatility of predictors. Mixed-frequency models produce higher volatility timing benefits, compared to temporally aggregate models. Therefore, our results highlight the importance of consistently incorporating predictors of mixed frequencies and correctly specifying the volatility dynamics in predictive regressions.