Chapter

Optimal versus Naive Diversification: How Inefficient Is the 1/<i>N</i> Portfolio Strategy?

Victor DeMiguel, Lorenzo Garlappi and Raman Uppal

in Heuristics

Published in print April 2011 | ISBN: 9780199744282
Published online May 2011 | e-ISBN: 9780199894727 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780199744282.003.0034
Optimal versus Naive Diversification: How Inefficient Is the 1/N Portfolio Strategy?

Show Summary Details

Preview

This chapter evaluates 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 fourteen models the chapter evaluates 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, the 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 Twenty-five assets and about 6000 months for a portfolio with fifty 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.

Keywords: heuristics; optimization; investment; diversification; simulations; 1/N

Chapter.  14988 words.  Illustrated.

Subjects: Cognitive Psychology

Full text: subscription required

How to subscribe Recommend to my Librarian

Buy this work at Oxford University Press »

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.