  In the past decade many researchers have proposed new optimal portfolio
selection strategies to show that sophisticated diversification can outperform
the na\"ive 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.
