Dynamic Asset Allocation for Practitioners, Part 3: Risk-Adjusted Momentum
Risk is in the Eye of the Beholder
So far, we’ve discussed the importance of investment universe selection and price momentum in designing a robust asset allocation methodology. If you haven’t read those articles, we would strongly encourage you to do so before proceeding with this one. We lay most of the explanatory and theoretical groundwork for this article in the previous installments, and we won’t repeat them here.
In our previous article, we examined how we can use total return momentum to identify asset classes with a higher probability of delivering positive performance in the near future. As a result, by regularly rotating into the strongest performing asset classes, we demonstrated that investors can earn a substantial return premium, with more consistent results and smaller drawdowns, than more conventional portfolios. The article looked at multiple ways of measuring an asset’s trajectory, but with limited regard for the path it took.
In this article, we examine whether it pays to account for differences in the path assets take to produce their momentum. All other things equal, do investors express a short-term preference for assets that have produced their returns with less risk, where risk is measured broadly as having delivered a smoother ride? As you will see, there are a variety of ways to quantify the investor’s experience, including different measures of both return and risk. We wish to explore the question of whether investors show risk aversion in manifesting the momentum effect, and which type of risk they most seek to avoid.
As a reminder, to safeguard ourselves from the temptations of “curve fitting,” we will run every risk-adjusted momentum method through a battery of simulations that vary the number of assets in the universe, portfolio concentration, trading frequency, and trading day. In total, each method will be tested on 16,116 simulation variations. The best methodologies will have to show promise across a broad set of these tests. Readers should be cautioned not to pay attention to small differences in performance across methodologies, which are almost certainly due to randomness over the test horizon. Rather, we should seek to determine if investors pay attention to risk at all when expressing the behaviors that manifest in the momentum effect, and whether any one type of risk stands out.
Defining Our Momentum Metrics
We will present 13 different risk-adjusted momentum measures selected from among the most popular and widely-available methodologies, both parametric and non-parametric.
As a refresher, parametric analysis assumes the data follows a specific probability distribution (think bell curve). Conversely, non-parametric analysis makes no initial assumptions about the distribution of returns. The true distribution of market returns is an area of intense debate, so is helpful to present both methodologies for comparison.