Naive Diversification Isn't So Naive After All

Abstract

I conduct a horse-race of 15 portfolio construction techniques over 8 empirical datasets comprised of individual stocks. I also conduct a robust Monte Carlo analysis that confirms that recent extensions of mean-variance optimization due to Kirby and Ostdiek (2012) are successful in curbing estimation risk and turnover. Despite these facts, my results indicate that no strategy consistently outperforms naive diversification in terms of mean excess return, Sharpe ratio, and turnover. I introduce a statistic, the time series average of the cross-sectional mean absolute deviation of risk and return, to explain why I observe these results. Data limitations and dataset characteristics contribute the most to the performance of a candidate strategy. I also propose several extensions to active timing strategies and include new characteristics in a parametric portfolio choice framework. Naive diversification continues to prevail, suggesting practical optimization techniques are inferior to naive diversification when forming portfolios of individual stocks.