2010 was a good year for Trend Following. The year closed on a high for both Trend Following Wizards and the Trend Following index of the simple strategies I track in the State of Trend Following report.
Now that all results are in, it is a good time to look back at the results, crunch some numbers and run some analysis. Let’s see if simple strategies managed to track the Wizards performance (hint: it looks promising)…
How did the Wizards perform as whole in 2010?
In order to get an overview for 2010, I constructed a Wizard index aggregating individual fund performances. The index monthly return was simply calculated as the average of all Wizard returns.
Looking through this lens, performance in 2010 came at +18.91%, with an annualized standard deviation of 14.42% (based on monthly standard deviation of 4.16%). Below is the following monthly breakdown:
How did the Wizards compare?
Results from Trend Following Wizards are often thought to correlate with each other. Empirically, this has appeared true throughout the year, with monthly results often being “in sync”.
In order to quantify this, I calculated a correlation matrix of Wizards tracked on the blog. Of course, correlation is far from being perfect, but with this data set, it should give us a good idea of similarity in the Trend Following Wizards performance. The matrix displays the correlation (using Pearson) between the Wizards monthly returns in 2010:
To reduce down the information, I have also calculated an alternate correlation measure between each Wizard and the Wizard index, based on monthly returns. The table below contains correlation of monthly returns over 1, 3 and 6 years:
And in visual form for the 1-year correlation on 2010:
Apart from a few exceptions, correlation numbers run fairly high across the board.
How about the State of Trend Following report?
The State of Trend Following, which aggregates the performance of simple Trend-Following based-strategies, similarly had a good year in 2010.
One the drivers behind creating this report and index is the idea that a big part of Trend Follower returns are made up of style-beta, which are easier to replicate than pure alpha (see the Betafication of Alpha: towards a Commoditization of Trend Following? for more on this). This appears to be a plausible proposition when looking at the results from the correlation calculations for the Trend Following Wizards, above.
The headline return for the report index was +54.08%, which is not directly comparable to the Wizard index return: the volatility was much higher. As volatility is a function of leverage, applying a volatility-normalization to the returns will give us a better comparison. I adjusted the report index with a standard deviation-normalization (ie leveraged down the monthly returns so that their standard deviation match those of the Wizard index), which produced a return of 21.19%.
The three curves are plotted below:
Striking resemblance, don’t you find? Monthly return correlation is 0.84, which seems to support the idea of style-beta embedded in the Wizards performance.
In the light of these results, one could come to the quick conclusion that Trend Following Wizards do not add much value compared to fairly simple Trend Following strategies. I believe this would be over-simplifying the issue.
let’s not forget that the index tracked is a theoretical model, not a real-life trading program. It does not include the impact of slippage and commissions and all other constraints associated with running a real operation. Moreover, Wizards results are net-of-fees, meaning that their gross performance is superior.
However, there is probably some truth in the concept of some style-beta associated to Trend Following, which should be encouraging to all traders/investors wanting to join the “Trend Following party”, but not able to do it through CTA offerings.
Another evolution based on this concept could be the emergence of “Trend Following tracker funds”, aiming to capture the style-beta associated with Trend Following (with tracker-like, much cheaper fees), similarly to the recently launched GTAA Trend Following ETF by Mebane Faber. It will be interesting to see how the industry looks like in 5-10 years and if changes at all.
Note on R
I would have normally used Excel to do most of the correlations calculation (before I saw the “R” light). For the correlation matrix for instance, on top the fact that my version of Excel (2003) does not support color scales (to apply a specific color a the cell based on its value to generate a heat map), this would have required a fair bit of manipulation, formulas, etc. Instead I used R, and wanted to share how quick the whole operation can take. Here is the code that imports the monthly returns data, calculates the correlation table and plots it as a heat map:
x<- read.csv("2010-returns.csv", header = TRUE, as.is = TRUE, sep = ",", dec="."); x_cor<- cor(x,,,"pearson") x_heatmpap <- heatmap.2(x_cor, Rowv=FALSE, Colv=FALSE, dendrogram="none", col=cm.colors(256), trace="none", cellnote=x_matrix, notecol="black")
Three operations = three lines… Fanbloodytastic!
Note that the function heatmap.2 (improves on standard heatmap by, amongst other things, allowing values to appear on cells of heat map) requires an additional R package: gplots. A sample returns file can be found here for you to test this out. Go on and download R (Windows, Mac, or Linux) to give it a try if you do not use it already. You will not regret it.
Update as per Josh’s comment below: to use the gplots package you need to install it and load it (as it is not standard). For this, you can either use the Packages menu “Install Packages…” option and pick your CRAN mirror and select the gplots package (if you’re using RGui). Or you can do it from the console with the “install.packages” command as per code below, which installs and then loads the package (required before running the previous code):
#install package from CRAN repository mirror install.packages("gplots") #load package library(gplots)