We extend the density combination approach of Billio et al. (2013) to feature combination weights that depend on the past forecasting performance of the individual models entering the combination through a utility-based objective function. We apply our model combination scheme to forecast stock returns, both at the aggregate level and by industry, and investigate its forecasting performance relative to a host of existing combination methods. Overall, we find that our combination scheme produces markedly more accurate predictions than the existing alternatives, both in terms of statistical and economic measures of out-of-sample predictability. We also investigate the performance of our model combination scheme in the presence of model instabilities, by considering individual predictive regressions that feature time-varying regression coefficients and stochastic volatility. We find that the gains from using our combination scheme increase significantly when we allow for instabilities in the individual models entering the combination.
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