Long-Term Strategic Asset Allocation: An Out-of-Sample Evaluation

The objective of this paper is to find out whether the expected potential gains from strategic asset allocation can be realized in an out-of-sample test. Firstly, we find that long-term investors should time the market if they use our proposed shrinkage prior. This prior downplays the predictability of asset returns and leads to superior out-of-sample results compared to a standard uniform prior. Important is the use of a utility metric to evaluate prediction models. Shrinkage limits the losses in extreme negative events and this is what risk-averse investors value the most. Secondly, including the hedge component of strategic portfolios only leads to a modest performance improvement out-of-sample. Repeated myopic strategies perform almost as well as a dynamic asset allocation strategy. Monte Carlo simulations relate this finding to estimation error, i.e. the estimated repeated myopic and dynamic portfolios approximate the true unknown optimal dynamic portfolio equally well. Next, our paper shows that incorporating parameter uncertainty leads to a small performance improvement. Finally, portfolio weight restrictions improve performance for bad models and hurt the good models. Affiliations: Maastricht University, Netspar, CEPR