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HeuristicsThe Foundations of Adaptive Behavior$
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Gerd Gigerenzer, Ralph Hertwig, and Thorsten Pachur

Print publication date: 2011

Print ISBN-13: 9780199744282

Published to Oxford Scholarship Online: May 2011

DOI: 10.1093/acprof:oso/9780199744282.001.0001

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

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

Chapter:
(p.644) Chapter 34 Optimal versus Naive Diversification: How Inefficient Is the 1/N Portfolio Strategy?
Source:
Heuristics
Author(s):

Victor DeMiguel

Lorenzo Garlappi

Raman Uppal

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199744282.003.0034

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

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