This paper investigates whether the accuracy of outlook hog price forecasts can be improved using composite forecasts in an out-of-sample context. Price forecasts from four wellrecognized outlook programs are combined with futures-based forecasts, ARIMA, and unrestricted Vector Autoregressive (VAR) models. Quarterly data are available from 1975.I through 2007.IV, which allow for a relatively long out-of-sample evaluation period after permitting model specification and appropriate composite-weight training periods. Results show that futures and numerous composite procedures outperform outlook forecasts. At intermediate horizons, OLS composite procedures perform rather well. The superiority of futures and composite forecasts decreases at longer horizons except for an equal-weighted approach. Importantly, with just few exceptions, nothing outperforms the equal-weight approach significantly in any program or horizon. Overall, findings favor the usage of equal-weighted composites, a result that is consistent with previous empirical findings and recent theoretical papers.