I thought this might be interesting, given our study http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1376199 Abstract: We evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1/N portfolio. Of the 14 models we evaluate across seven empirical datasets, none is consistently better than the 1/N rule in terms of Sharpe ratio, certainty-equivalent return, or turnover, which indicates that, out of sample, the gain from optimal diversification is more than offset by estimation error. Based on parameters calibrated to the US equity market, our analytical results and simulations show that the estimation window needed for the sample-based mean-variance strategy and its extensions to outperform the 1/N benchmark is around 3000 months for a portfolio with 25 assets and about 6000 months for a portfolio with 50 assets. This suggests that there are still many “miles to go” before the gains promised by optimal portfolio choice can actually be realized out of sample.
Interesting, but surely this is largely because of ERISA? I’d be interested in seeing a comparison of results pre/post '74 inception.
Isn’t the study just comparing returns from portfolios made up of the same N assets, equally distributed among the assets on the one hand and distributed as indicated by an optimization process on the other? How could ERIS possible affect the results?
wow, my bad. You’re absolutely right…the abstract has nothing at all to do with pensions. I’ve had retirement funds on the brain and read them into an article that didn’t mention them at all…nice one.
I havent read the article, but it probably just proves that optimization doesnt work because the underlying assumptions (i.e. historical correlations are constant in the future) don’t hold. The true reason why naive diversification is bad is that it lacks suitability. Or am I totally wrong here?