RMI Applications: Sector Rotation Using ETFs

Note: This article is part of the ETF Trends Strategist Channel

Written by Giralda Advisors

In our prior articles in this series for the ETF Strategist Channel (see archive here), we introduced the concept of Risk-Managed Investing (RMI); outlined its alpha-adding potential, particularly in light of diminished prospects for other (i.e., non-equity) asset classes; and calibrated its tolerable cost. At this point, we suspect you may be eager to hear how RMI might be executed. In this and our next installment, we will outline two practical approaches to implementing RMI, and in the piece following these, we will conduct an overall review of RMI strategies in the marketplace.

Recall that RMI is the attempt to embed equity volatility dampening and/or downside risk mitigation directly into the equity investment itself, thereby lessening the portfolio’s reliance on other, non-equity, asset classes to do so indirectly and arguably less reliably. The first RMI approach we’ll examine is a tactical one — one that is based on using momentum to help reduce the downside risk associated with typical bear market declines in domestic large-cap equities.

Rebalancing Revisited

Let’s begin our examination by revisiting one of the bedrocks of professional investment management — portfolio rebalancing.  Rebalancing is keeping your portfolio true to its intended asset allocation, and thus can be viewed a risk management device. Over time, an unrebalanced portfolio will likely see its asset allocation shift toward riskier assets and the portfolio will tend to drift inward, off the efficient frontier. Rebalancing prevents this, and the alpha-generating “rebalancing benefit” has been well documented in the literature.

What makes the rebalancing benefit work? Mean reversion, the flip side of momentum. One way to visualize the process is to think of each asset as tethered to its long-term trend line by an elastic leash. The asset can roam freely from its trend line to a degree, but if it strays too far, the leash gets stretched too tightly and pulls the asset back in the opposite direction. Tolerance-based rebalancing is implicitly assuming that the length of this leash is a predetermined constant. Once an asset’s allocation has reached this threshold, you are assuming that momentum is near its end and mean reversion is about to take over. In the real world of asset behavior, the leash is of indeterminate — and changing — length and elasticity. How do you deal with that? By actually measuring momentum.

Measuring Momentum

The most common way to measure momentum of an asset is by calculating a moving average (MA) of the asset’s return stream.  MAs have been shown to be effective in separating information from noise and, in an investment context, they can be used to generate buy/sell signals. In its simplest form, when an asset index crosses below its own MA, this can be a signal to exit that asset. Conversely, when the index crosses back above its MA, this can be a reentry signal. The degree of stability and responsiveness of an MA signal can be varied just by changing the period over which it is measured. The longer the period, the more stable, but less responsive, the signal.  The degree of stability versus responsiveness of an MA signal need not be static and may change based on market conditions.  We have developed a formula that uses the current level of asset volatility to dynamically adjust the MA period, thereby making the signal more or less responsive automatically.


Another common signaling strategy uses two MAs — one over a shorter period and one over a longer period. Instead of comparing the index itself to the MA, you invest in the asset if the shorter-period MA is greater than the longer-period MA. This is called a moving-average-crossover (MAC) strategy A third, less common, signaling strategy is to look at the trend in (i.e., the first derivative of) the MA. When the MA is increasing, you invest in the asset; when it is decreasing, you stay out.

We have developed and tested dozens of types of momentum strategies. What we found was that no single momentum strategy was perfect, but there were several that had unique strengths under different market circumstances. For example, some proved more reliable as exit signals, while others were more dependable for reentry.  Therefore, we constructed a multi-faceted signal wherein each strategy has a role to play under the circumstances when it is most likely to succeed.