An Updated Review of (Sub-)Optimal Diversification Models

Abstract

In the past decade many researchers have proposed new optimal portfolio selection strategies to show that sophisticated diversification can outperform the naïve 1/N strategy in out-of-sample benchmarks. Providing an updated review of these models since DeMiguel et al. (2009b), I test sixteen strategies across six empirical datasets to see if indeed progress has been made. However, I find that none of the recently suggested strategies consistently outperforms the 1/N or minimum-variance approach in terms of Sharpe ratio, certainty-equivalent return or turnover. This suggests that simple diversification rules are not in fact inefficient, and gains promised by optimal portfolio choice remain unattainable out-of-sample due to large estimation errors in expected returns. Therefore, further research effort should be devoted to both improving estimation of expected returns, and possibly exploring diversification rules that do not require the estimation of expected returns directly, but also use other available information about the stock characteristics.

Keywords: Portfolio selection, Weight optimization, Expected Returns estimation

JEL Classification: G11