We propose a new approach to imposing economic constraints on time series forecasts of the equity premium. Economic constraints are used to modify the posterior distribution of the parameters of the predictive return regression in a way that better allows the model to learn from the data. We consider two types of constraints: non-negative equity premia and bounds on the conditional Sharpe ratio, the latter of which incorporates time-varying volatility in the predictive regression framework. Empirically, we find that economic constraints systematically reduce uncertainty about model parameters, reduce the risk of selecting a poor forecasting model, and improve both statistical and economic measures of out-of-sample forecast performance.

ABSTRACT We develop a new approach to modeling dynamics in cash flows extracted from daily firm‐level dividend announcements. We decompose daily cash flow news into a persistent component, jumps, and temporary shocks. Empirically, we find that the persistent cash flow component is a highly significant predictor of future growth in dividends and consumption. Using a log‐linearized present value model, we show that news about the persistent dividend growth component predicts stock returns consistent with asset pricing constraints implied by this model. News about the daily dividend growth process also helps explain concurrent return volatility and the probability of jumps in stock returns.