One of the most mind-blowing implications of portfolio theory is that a well conceived portfolio has the potential to be much better, in terms of risk adjusted performance, than what we might expect from the sum of the individual portfolio holdings.
This is more obvious in some fields than others. For example, can a person intuit the qualities of water from an understanding of the properties of hydrogen and oxygen (without a deep understanding of quantum mechanics)? Can you effectively comprehend the experience of carrot cake from an understanding of the ingredients?
The famous World Wildlife Found logo is an example of a Gestalt because the brain identifies that the conglomeration of irregular black shapes in the image is actually a panda bear. It is not the shapes themselves, but the orientation of the shapes and how they fit together that communicates the salient information contained in the image.
Most investors pay much more attention to the process of identifying the individual characteristics of the assets they want to own than they commit to the process of identifying how well the assets might fit together in a portfolio. But what if the individual characteristics of the assets are less important than the way they work together?
How is it that optimization alone can deliver better risk adjusted performance without any fundamental information about the relative prospects for portfolio constituents? Part of the answer is that optimization tends to indirectly tilt portfolios toward factors that are well known for adding excess returns over time.
The following table quantifies the annualized difference between the return to the factor exposure of the alternative index relative to the market-cap index. You can see that the optimized portfolio derives meaningful alpha from a small-cap bias relative to the market-cap index. This is unsurprising. What is more surprising is that the optimizations tend to tilt portfolios toward the Fama French Value factor, and away from the momentum factor.
Clearly there is an opportunity to combine fundamental stock-picking factors with robust portfolio optimization to deliver better results than either method alone – another Gestalt!
The following table is taken from an S&P Capital IQ presentation published in December 2010. The authors imposed factor tilts on a minimum variance portfolio derived from constituents of the S&P 1500, with the results in Table 4. Note improved Return/Risk ratios from a combination of FF Value and Earnings Quality tilt portfolios with minimum variance optimization.
