Abstract:
In this paper, we wish to evaluate the performance of simple asset-allocation strategies such as allocating 1/N to each of the N assets available. To do this, we compare the out-of-sample performance of such simple allocation rules to about ten models of optimal asset-allocation (including both static and dynamic models) for ten data sets. We find that the simple assetallocation rule of 1/N is not very inefficient. In fact, it performs quite well out-of-sample: it typically has a higher Sharpe ratio, a higher certainty equivalent value, and a lower turnover than the policies from the optimal asset allocation. The intuition for the good performance of the 1/N policy is that the loss from naive rather than optimal diversification is smaller than the loss arising from having to optimize using moments that have been estimated with error. Simulations show that the performance of policies from optimizing models relative to the 1/N rule improves with the length of the estimation window (which reduces estimation error) and also with N (which increases the gains from optimal diversification). But, even with an estimation window of 50 years, the difference in the performance of the 1/N policy and the policies from models of optimal asset allocation is not statistically significant.
