We looked at three sets of moving average observations:
- A simple, quarterly moving average. Since there are approximately 21 trading days in each month, we defined quarterly as the 63-day moving average.
- A simple, annual moving average. In this case, we used the 252-day moving average as our definition of annual.
- Points when the quarterly moving average moved above/below the annual moving average.
For output, we looked at a variety of statistics:
- The frequency, or number of days a cross or signal occurred, on an annualized basis.
- Signal length, the average number of days a signal was in place annually (the inverse of frequency).
- The median alpha (excess return) over a simple buy-and-hold return (if one were to execute buys and sells based on these signals consistently through time). Transaction costs were not factored in.
- The batting average, which represented the number of times a signal generated a positive excess return over buying and holding.
How did the results look? Here are some initial conclusions.
- Each moving average system we examined added value (note: we did not factor in transaction costs).
- Interestingly, the batting averages were all below 50 percent. This makes sense. Moving averages are about following trends, and they’re also about risk management. Following moving averages means one is adhering to the important trading wisdom chestnut of “letting winners run and cutting losers short.”
- Signal lengths were short. This suggested moving averages really are better suited for trading than investing purposes. This is the case for CLS as our typical holding periods are measured in years not weeks.
- Performance depended on the asset class.
- Equities and fixed income are traditionally considered counter-trend markets, meaning they don’t hold trends very well. That’s been the conventional view, and our data confirmed it. Both domestic equities and fixed income produced negative excess returns when using only quarterly or annual moving averages. (Domestic equities, however, did produce a positive excess return when using the quarterly moving average crossed through the annual moving average).
- Currencies and commodities, meanwhile, are traditionally considered to be trending markets, meaning they do hold trends well. Our data bore that out too. International equities produced positive excess returns using all three moving averages. This was likely driven due to the currency effect.
- Commodities produced the strongest results of all – in all three time frames.
- Alternatives, which is a grab-bag of asset class segments and strategies, surprisingly produced strong positive excess returns using all three moving average systems.
Bottom line, and this is a bottom-line business, moving averages are valuable tools to incorporate into the trading decision-making process. We stress trading over investing given the frequency of signals (at least with the ones we examined); but in a competitive environment such as investing, any long-term edge we can identify and consistently implement, even in trading decisions, should add value over time. As we like to say at CLS, every basis point of performance matters.
*CLS’s Portfolio Manager Paula Wieck and Orion Advisor Services’ Joe Porter put in a tremendous effort on this project.
This information is prepared for general information only. Information contained herein is derived from sources we believe to be reliable, however, we do not represent that this information is complete or accurate and it should not be relied upon as such. All opinions expressed herein are subject to change without notice. The graphs and charts contained in this work are for informational purposes only. No graph or chart should be regarded as a guide to investing.