In my last post, I broke down the individual components to look at the performance of each factor. Although by themselves, the correlation and volatility factors weren’t that attractive, as a whole combined together, its a different story.

I’ve always been a proponent of simplistic approaches in system design as adding too many nuts and bolts to ensure sophistication only brings over-fit. In my opinion, when you are designing the alpha portion of your portfolio, you should look to design multiple simplistic strategies that are different in nature (uncorrelated). Take these return streams and overlay a portfolio allocation strategy and you will find yourself with a decent alpha generator with >1 risk return. Ok back to FAA…

Keller and Putten in their FAA system combined the signals of each factor by a simple meta rank function. This ranking function took the following form:

where m, c and v represents the factor rank of momentum, correlation and volatility respectively. Each factor is then given a weight. The meta ranking function is than ranked again and filter based on absolute momentum to arrive at the assets to invest in. Note that any assets that don’t pass the absolute momentum filter will be invested in cash (VFISX). When coding the meta ranking function, I found that there are times when some assets share the same final meta rank. This caused problem for some rebalance period when the assets to hold will exceed top N. I consulted with the authors and they revealed that “with rank ties, we select more than 3 funds.” Below is a replication of the strategy; it is tested with daily data as oppose to monthly data used by the authors.

The model results are pretty decent. One aspect I may change would be the use of the cash proxy in the volatility ranking factor. By including the theoretical risk free rate that is suppose to have volatility of zero will skew the results to bias cash.

A reader commented on a little error in coding I made in the last post. Don’t sweat, it doesn’t change the performance one bit. I’ve modified the code and placed everything including the current code in to the FAA dropbox folder. Should you have any questions please leave a comment below.

Thanks for reading,

Mike

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We co-developed the model with Prof. Keller over the last two years. For those interested in the proof of the pudding, namely on how this model performs in practise since May 2012, you can find this model on collective2.com under QTAA (http://www.collective2.com/cgi-perl/c2systems.mpl?systemid=73807631) .

Very nice work! On June 5, 2012 you explained the concept of equity curve trading. Could you please show how this could be implemented in a strategy in r?

Hi Thomas,

The good thing with SIT is that it is Jam packed with sample strategies. For a implementation of equity curve trading in R, have a look at the following github page:

https://github.com/systematicinvestor/SIT/blob/master/R/bt.test.r

From lines 3051 to 3265, Michael over at systematic investor has already implemented a version of it with volatility targeting.

Hope this helps,

Mike

Hi Mike,

thanks for your help! With your hint I could do the backtests I wanted to do!

Bye,

Thomas