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 combina- tion 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 perfor- mance 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 incremental role of our model combination method in the presence of model instabilities, by considering predictive regressions that feature time-varying regression coefficients and volatil- ity. 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.