For a long time I’ve been intrigued by the idea of looking at specific sectors of the economy when predicting next day SPY move. Looking at sectors makes a lot of sense to me. Sectors carry specific information of how the economy is doing and what kind of positions large investors are favoring. Defensive sectors such as utilities (XLU) or healthcare (XLV) show strength in weak markets. Whereas consumer discretionary (XLY) do better in up markets. That’s at least the conventional wisdom. So if it’s that easy why not simply code that into the setup: go long when XLY is doing well and short when XLU is showing strength?
See this picture from John Murphy / stockcharts.com: that’s been my inspiration for this work. I do believe in the fundamental ideas captured in this chart. However, the main problem is that I’m not an economist and I don’t do fundamental analysis. So I need a model that’s going to capture the idea of this picture.
This is my third attempt to include sector information in my SPY trading. In the past I looked at a couple of different ways (link). The main issue with my past approaches: sector relevancy changes during the economic cycle. So depending where we are in the cycle a different sector might lead the market one way or the other.
Let me set the baseline first: I’m going to use this to improve my mean reversion trading. My mean reversion Indicator of choice is DVO from David Varadi at CSS Analytics (an excellent indicator!). So let’s look at next day returns while DVO is below 0.5
The data used for this test is from Yahoo and adjusted for cash dividends. The trade are close to close. The statistic is showing next day returns only.
This time I want to do it smarter: smarter means adaptive and flexible. I don’t want to build any kind of bias into the algo. The idea is to have a list of nine different time series (sector ETFs) and let the algo pick the one that has the highest predictive value at a given time. The ETF’s are ranked based on their historical performance forecasting volatility adjusted next day SPY returns. The result of the calculation is adjusted with the short-term momentum of the historical performance. So a SPY trade will only be taken when the leading sector and the SPY match.
There is a ~10% improvement in avg.(%) next day returns with an increased Sharpe Ratio. The effect has been consistent over the years. I also did the reverse test, picking the worst sector. In this case the performance dropped as expected.
The leading sectors from mid November to mid December has been XLP / consumer staples. Followed by XLY / consumer discretionary to mid January and now it’s XLK / technology.
Finally the bricks fit together, avoided any kind of bias while taking an adaptive approach for selecting the relevant sector ETF. After having done this piece of research I’m going to look how to include this into my SPY based mean reversion system. At this point I’m not sure if it will be part of the entry setup or money management only. That will probably make another post.