Optimal portfolio choice under decision-based model combinations

We propose a novel Bayesian model combination approach where the combination weights depend on the past forecasting performance of the individual models entering the combination through a utility-based objective function. We use this approach in the context of stock return predictability and optimal portfolio decisions, and investigate its forecasting performance relative to a host of existing combination schemes. We find that our method produces markedly more accurate predictions than the existing model combinations, both in terms of statistical and economic measures of out-of-sample predictability. We also investigate the role of our model combination method in the presence of model instabilities, by considering predictive regressions that feature time-varying regression coefficients and stochastic volatility. We find that the gains from using our model combination method increase significantly when we allow for instabilities in the individual models entering the combination.