Update On the Oil Model

I thought given events in the middle east that this would be a prudent time to review our long term OIL model.   Note that the models I have developed here look for extreme moves and attempt to identify the inception of long term trends.  Therefor they are long term models ie you can be long for a long time or out of the market for a long time.   They in a sense ride the wave of the trend.   Note that the model exited the market near the prior peak in April 2008 and stayed out until April 2009 and is currently still long.   The result of this is that the model itself missed the downturn that occurred from the prior peak and re-entered the market once more as a new uptrend was being established that uptrend continues.  The idea of these models is to outperform the underlying ETF

How does the prior years return impact future performance (Part 2)

In this post I decided to continue the prior study.  In the prior post I present a study that looks at how future performance is impacted by the prior year for some of the most liquid ETFs.  I now extend these results by reviewing  the best and worst performers of 2010 and eliminated those that were less than 500million USD in size and have very low volume.  The results below indicate that when you have extreme movements either up or down there is a period of reversal.  Note that the exception is UNG which seems to have gone down since inception however both EWP and EWI have gone up 13% since 12/31/2010 and all the best performers in ETF have had a reversal.  Note that while TUR did not have an extreme movement upwards in 2010 (anything over 50% is extreme) it was still among the top performers.  It will be interesting to monitor these positions throught the first half of 2011 to see where they end up.   Note as a disclaimer these are merely observations that I am making about the ETF market and not live positions.

How does the prior years return impact future performance

In the following study I decided to analyze the walk forward performance of some highly liquid ETFs.  For this study the ETFs are divided into four buckets of performance over the prior year.   The buckets are as follows,  bucket1 (-50% to -25%),  bucket2 (-25% to 0%), bucket3 (0% to 25%),  bucket4 (25% to 50%).  The walk forward periods are 90, 120 and 252 days.   What is interesting is that the best walk-forward performance occurs when the prior year performance is in bucket1 (-50% to -25%). 

The 252 days walk-forward period performance is on average between 45% and 70% if the prior year was in bucket1.   Similarly in all cases if the average over the prior year was in bucket 4 (25% to 50%) in the next 90, 120, 252 walk forward periods the performance is negative.    The trading strategy would be load up  when the prior year is in bucket 1and short when the prior year is in bucket 4 .  Note also the the 252 day walk forward period is significantly better than both the 90 and 120 day periods.

Results For the Brazil EWZ Model

The last long EWZ trade was entered on  08/27/2010 and exited  10/05/2010 right now the system is neutral.  The system had a return of 16% for 2010 vs a gain of 7.07% for the underlyer.  So far the underlyer EWZ has had a net loss since year end 2010 while the system has remained neutral.

Emerging Markets Model Performance Through Jan 2011

The table below summarizes the results of our strategy as applied to the EEMs (Emerging Markets ETF).  We exited a long position in late November 2011 and are currently neutral.

Cumulative Performance Of QQQQ Model

In this post I want to summarize the performance of our QQQQ model from inception.  We recently exited a long position on January 14 2011 and are currently neutral.   The QQQQ model is an example of our slow trading systems which identify longer dated trends and stick with them.   The key to the model is to avoid major pitfalls and downturns.   The model can be either in or out of the market for long periods of time.  Most notably when the NASDAQ bubble burst in 2000 the model stayed out of the market while the NASDAQ lost half its value.   The model goes long in 2002 and avoids the meltdown in 2008 going long again in 2009.   The purpose of this model is identify long term trends.   It is worthwhile noting that if you invested $1000,000 in the QQQQ at the end of April 2000 you would still be down over 40% by today.  Whereas if you traded the QQQQ ETF using the model you end up 340% over the same period with much less volatility.

A Correlation Study between ETFs

The other day I was reading an article in Seeking Alpha by Erik Gholtoghian in which he established a multi-factor capm model between DRYS, SEA and USO which can be summarized as follows:

 DRYS weekly % change = 1.38*weekly% change in SEA+.56*weekly %change in USO.

SEA is the Guggenheim/Delta Global Shipping ETF which was introduced in the middle of last year, OIH is the Merrill Lynch Oil Service Holders ETF.  I decided to conduct a  study that examined the correlation of SEA with respect to commodities that are shipped by DRYS container ships.  In the first chart the correlation matrix includes data from the SEA ETF which started trading in June last year.   The strongest relationship exists between XLE and IYM for  06/14/2010 till present and from jan 3 2007-present.  There is also a strong correlation between SEA and IYM and XLE over the much shorter period.  These represent potential trading opportunities assuming that pairs trade within certain ranges and they mean revert.

The first chart below summarizes the relationship between IYM and XLE from January 2007 till the present.  Clearly there are buying opportunites when IYM trades below XLE and selling opportunties when IYM crosses above XLE.  There are a few ways to play this one could go long IYM at some point after crossing XLE from above and waiting till it once more crosses XLE from below.  A second trade is to buy XLE and Sell IYM when the spread between the two has widened and close out the position when it converges.

In the second chart below I incorporate the SEA data from 06/14/2010

till present.