Optimal Versus Naive Diversification: How Inefficient is the 1/NPortfolio Strategy?

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ID: 290083
2007
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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.
Reference Key
openalex_W2163969674 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Victor DeMiguel, Lorenzo Garlappi, Raman Uppal
Journal review of financial studies
Year 2007
DOI
10.1093/rfs/hhm075
URL
https://doi.org/10.1093/rfs/hhm075
Keywords Keywords not found

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