The literature on excess return prediction has considered a wide array of estimation schemes, among them unrestricted and restricted regression coefficients. We propose bootstrap aggregation (bagging) as a means of imposing parameter restrictions. In this context, bagging results in a soft threshold as opposed to the hard threshold that is implied by a simple restricted estimation. We show analytically that the resulting forecast has lower variance than the forecast that results from a simple restricted estimator. In an empirical application using the same data set as in Campbell and Thompson (2008, "Predicting the Equity Premium Out of Sample: Can Anything Beat the Historical Average?"' Review of Financial Studies 21), we show that the resulting forecasts have more predictive power than those resulting from simple parameter restrictions.