Thursday, May 16, 2013

I will come back to that later

Some time ago I wrote about generating safe investment strategy by combining many low correlated investment models.

Recently I've been working on a family of models based on one common concept - short term return prediction.

Seems, it may actually work :)

I have run a number of test and so far results are encouraging:


The above chart presents strategy returns over 250 sessions between 2011-11-23 and 2013-04-12.
(some sessions are missing for lack of common data for some days)

Lines of different colors represent sub-models used. The thick purple line represents the combined model.

The correlation between some sub-models is pretty high - that may be used for reduction of the number of sub-models used.

> cor(test.arr[,,"return"])
             M1        M2        M3        M4        M5      mean
M1    1.0000000 0.3565054 0.2471695 0.3712636 0.1614239 0.4081393
M2    0.3565054 1.0000000 0.1567697 0.1935882 0.1805843 0.2852754
M3    0.2471695 0.1567697 1.0000000 0.5065645 0.4060609 0.5138838
M4    0.3712636 0.1935882 0.5065645 1.0000000 0.3663719 0.6328662
M5    0.1614239 0.1805843 0.4060609 0.3663719 1.0000000 0.5846763
mean  0.4081393 0.2852754 0.5138838 0.6328662 0.5846763 1.0000000

The returns of all sub-models were positive over the test period:

> stats
          M1      M2      M3      M4      M5      mean             
total     0.7488  0.5927  1.1036  0.5844  1.0368  1.0415
mean      0.0029  0.0023  0.0044  0.0023  0.0041  0.0041
drawdown -0.1331 -0.1181 -0.1674 -0.1461 -0.1563 -0.1804



The combined model was positive for all test periods and assets used for tests so far.

The individual returns are low, but it seems it is possible to safely leverage the strategy 2-3x. I haven't completed yet the analysis of the frequency of portfolio re-balancing yet, too.

As I've mentioned in the title of this post, I'm going to return to this strategy and probably its sibling a little bit later...




No comments: