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

@article{DeMiguel2009OptimalVN,
  title={Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?},
  author={Victor DeMiguel and Lorenzo Garlappi and Raman Uppal},
  journal={Review of Financial Studies},
  year={2009},
  volume={22},
  pages={1915-1953}
}
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… 

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References

SHOWING 1-10 OF 69 REFERENCES

Parametric Portfolio Policies: Exploiting Characteristics in the Cross Section of Equity Returns

This work proposes a novel approach to optimizing portfolios with large numbers of assets by model directly the portfolio weight in each asset as a function of the asset’s characteristics, and presents an empirical implementation for the universe of all stocks in the CRSP-Compustat dataset.

Optimal Portfolio Choice with Parameter Uncertainty

Abstract In this paper, we analytically derive the expected loss function associated with using sample means and the covariance matrix of returns to estimate the optimal portfolio. Our analytical

International Portfolio Diversification with Estimation Risk

International portfolio diversification has long been advocated as a way of enhancing average returns while reducing portfolio risk for the investor who considers diversifying into foreign

On the Sensitivity of Mean-Variance-Efficient Portfolios to Changes in Asset Means: Some Analytical and Computational Results

This paper investigates the sensitivity of mean-variance(MV)-efficient portfolios to changes in the means of individual assets. When only a budget constraint is imposed on the investment problem, the

When Will Mean-Variance Efficient Portfolios Be Well Diversified?

The authors characterize the conditions under which efficient portfolios put small weights on individual assets. These conditions bound mean returns with measures of average absolute covariability

An Empirical Bayes Approach to Efficient Portfolio Selection

  • P. FrostJ. Savarino
  • Economics, Computer Science
    Journal of Financial and Quantitative Analysis
  • 1986
This empirical Bayes method is shown to select portfolios whose performance is superior to that achieved, given the assumption of a noninformative prior or by using classical sample estimates.

Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps

Mean-variance efficient portfolios constructed using sample moments often involve taking extreme long and short positions. Hence practitioners often impose portfolio weight constraints when

Portfolio Resampling: Review and Critique

A well-understood fact of asset allocation is that the traditional portfolio optimization algorithm is too powerful for the quality of the inputs. Recently, a new concept called “resampled

Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach

In this paper, we show how an investor can incorporate uncertainty about expected returns when choosing a mean-variance optimal portfolio. In contrast to the Bayesian approach to estimation error,
...