Momentum Rotation Strategies and Data - Part 1

I am going to take a short break from reviewing backtest results for different variations of Iron Condors, and revisit Momentum Rotation strategies. In a past post (Historical Data and Momentum Rotation Strategies), I discussed how data choices impact the repeatability of ranking output typically used in Momentum Rotation strategies.

I'll summarize a bit of what I discussed in that last rotation post...

I have been trading monthly Momentum Rotation strategies across six accounts for several years. These strategies rank portfolios containing between 10 and 30 ETFs or mutual funds by momentum. Some of my strategies combine multiple momentum readings to derive a rank, while others go further and add filters. The key point here is that all my rotation strategies rank a basket of funds relative to each other...and this is a common approach across all rotation systems, not just mine.

Now to the crux...the data used by rotation systems has an impact on individual fund ranking scores. Most rotation strategies that are published on the internet (commercial systems, blogs, etc) use split and dividend adjusted price data to generate the ranking scores. The entire adjusted price data series changes with every dividend that is issued, which changes all past prices in the time series. When a dividend is issued for one of these funds today, all of the past adjusted prices change, which will change the past rank scores for that fund. When the past rank scores change, a fund that was in position 1, can move to position 2, which then changes the backtest results. I will show two examples of this issue in this post and subsequent posts. This situation does not occur when non-adjusted price data is used.

When you implement a Rotation System, or any system for that matter, you can use: 1) split and dividend adjusted price data...I'll refer to this as "adjusted" data in the rest of this post, 2) split adjusted only data...I'll refer to this as "actual" price data in the rest of this post, or 3) a hybrid where "actual" price data is used for ranking/signal generation, and "adjusted" price data is used to determine strategy P&L.

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We will look at the results for each of the three data approaches above for a simple rotation system. Our system will only look at the 60 day rate of change for ranking and will pick the ETF with the largest positive change in the portfolio. If all of the ETFs in the portfolio have a negative rate of change...a price today that is lower than the price 60 days ago, then the system will move to cash. The system will only rank the ETFs in the portfolio on the last trading day of the month.

The ETFs in the portfolio include:

I have included screenshots from Yahoo Finance for each of these ETFs, that show how the actual price and adjusted price can deviate over time as dividend payments are added into the adjusted price time series.

EEM - on Jan 3, 2007 the close was 115.14, but the adjusted close was 32.99