برتر بودن اوراق بهادار بهینه شده به تنوع ساده و آسان: واقعیت یا داستان؟

DeMiguel, Garlappi, and Uppal (2009) conducted a highly influential study where they demonstrated that none of the optimized portfolios consistently outperformed the naive diversification. This result triggered a heated debate within the academic community on whether portfolio optimization adds value. Nowadays several studies claim to defend the value of portfolio optimization. The commonality in all these studies is that various portfolio optimization methods are implemented using the datasets generously provided by Kenneth French and the performance is measured by means of the Sharpe ratio. This paper aims to provide a cautionary note regarding the use of Kenneth French datasets in portfolio optimization without controlling whether the superior performance appears due to better mean-variance efficiency or due to exposures to established factor premiums. First, we demonstrate that the low-volatility effect is present in virtually all datasets in the Kenneth French online data library. Second, using a few simple portfolio optimization models that are said to outperform the naive diversification, we show that these portfolios are tilted towards assets with lowest volatilities and, after controlling for the low-volatility effect, there is absolutely no evidence of superior performance. The main conclusion that we reach in our paper is that a convincing demonstration of the value of portfolio optimization cannot be made without showing that the superior performance cannot be attributed to profiting from some known anomalies.