Keep in mind that commodity trading advisors (CTAs) are generally trend followers and they need markets to move to generate returns, in other words they are long volatility. If the markets are flat, there will be no trends. By contrast, if the markets are moving in a consistent manner, managed futures managers (or CTAs) capture value. Because CTAs can go both long and short the direction of the moves is not as important as the fact that trends exist. Thus, managed futures have tended to perform better given sustained moves in either direction, with larger volatility leading to larger returns (see “Predicting manager success,” link below).
The actual month-to-month performance data available from managed futures is found in CTA disclosure documents or through numerous CTA reporting firms. The data speaks volumes about the techniques and the markets traded by CTAs. Perhaps managed futures is the better term because many CTAs trade only financial futures. They are generally global macro fund managers trading instruments like bonds, stocks, currencies, energies, metals and commodities.
Correlation
One of the more interesting analysis you can perform across the performance data of CTAs is a correlation comparison. To lay the groundwork, we have always found famed managed John W. Henry’s definition of correlation useful. Consider:
“Just what is correlation, and how do we derive the correlation coefficient? Correlation is a statistical term giving the strength of linear relationship between two random variables. More simply defined, it is the historical tendency of one thing to move in tandem with another. The correlation coefficient can be a number from -1 to +1, with -1 being the perfectly opposite behavior of two investments (e.g., up 5% every time the other is down 5%), and +1 reflecting identical investment results (up or down the same amount each period). The further away from +1 you get (and thus closer to -1), the better a diversifier one investment is for the other. Correlation coefficient is found by taking the covariance between two variables and dividing by the square root of the product of each of the two variances (trust us on this part). No wonder the eyes of so many glaze over when discussing the topic of correlation. However, it has some very tangible uses, if they can be explained to the novice. The most simplistic description of correlation is the tendency for one investment to ‘zig’ while others are ‘zagging.’”
John W. Henry’s firm was actually explaining its view of correlation among markets, outlining its approach for properly diversifying its portfolios. However, the correlation definition works equally well for comparing and contrasting CTA performance data across different traders.
Let’s look at the correlation between two well-known CTAs with track records exceeding 20 years each. Rabar Market Research, run by former Richard Dennis turtle Paul Rabar, managing $542 million, and Chesapeake Capital, run another former turtle, Jerry Parker, managing $1.5 billion, have a performance correlation of 0.75 from Jan. 1, 2000 to May 1, 2006. They also have a performance correlation from Jan. 1, 1990 to May 1, 2006 of 0.73. What does this all mean exactly? Correlation coefficients gauge how closely an advisor’s performance resembles another advisor. Values exceeding 0.66 may be viewed as having significant positive performance correlation. And consequently, values exceeding -0.66 may be viewed as having significant negative performance correlation.
In simple terms Rabar and Chesapeake are trading very similar trend following trading methods, making and losing money at generally the same time and generally the same markets. For those with an eye toward history, that’s not surprising. Both men trained under famed traders Richard Dennis and William Eckhardt during a nurture vs. nature experiment in the1980s (see “Great minds,”).
The question was: can traders be made, or are they born? The so-called “turtles,” were a group of “students” who were taught to trade. Many have gone on to be extremely successful (Rabar and Chesapeake are two examples) in their own right.
Correlation charts have another powerful benefit. You quickly can determine which markets likely influenced CTA returns. If several CTAs profited handsomely in a
particular month, then you can look for a market with an unusually large move. For instance look at returns for Chesapeake and Rabar for March and April. What do the actual market charts look like that allowed these ‘very similar’ big performance months in March and April 2006? An educated guess can be made regarding which markets produced the profit (see “Profit centers,”).
You can see a very pronounced downtrend in U.S. bonds in March that continued through April. Crude oil, T-bonds, the Euro and the British pound all moved significantly in April giving generous returns to both Rabar and Chesapeake’s clients.
Now let’s look at a matrix of correlation of returns among a larger group of CTAs (see “Correlations”).
This analysis of correlation charts among like-trading CTAs is primarily useful as a ‘confidence’ booster. It allows investors who might be skeptical of certain trading styles (like trend following) to understand the cyclical nature of CTA performance returns. It also allows novice traders to build confidence in their own trading methods by studying the past results of their more established peers.
Moreover, studying correlation tables gives aspiring traders the confidence that if the great traders appear to be trading in a similar fashion, why shouldn’t they? The turtle experiment took a diverse and mostly novice group of individuals and taught them all to trade trend following methods. The correlation table verifies they are still using very similar trading techniques across very similar market portfolios. New traders or new investors in managed futures can see the power of doing something that works throughout a long time. The correlation tables anchor the significance and durability of CTAs employing trend following methods.
Taking It a Step Further
Once you have the CTA or a CTA portfolio assembled, the importance of
correlation is seen in yet another way. Adding a CTA piece to your current and typical stocks and bonds portfolio can further enhance return and reduce risk. Why? Because it is non correlated to traditional investments and other alternative investments. The Alternative Investment Management (AIMA) pointed out that between 1980 and 2004 the correlation between the CISDM dollar-weighted managed futures index and the Russell 3000 was –0.03; the correlation between the CISDM and the Lehman Aggregate Bond Index was 0.07; the MSCI EAFE index –0.05; and the Goldman Sachs Commodity Index –0.01.
Managed futures as part of your typical stock and bond portfolio gives you the ability to profit when stock markets decline. And that difference in return streams is where correlation comes into play yet again, albeit in a slightly different fashion than comparing CTA performance data. Most stock and bond portfolios profit when the S&P 500 is moving higher but lose money when the S&P 500 declines. Adding managed futures to a portfolio may actually reduce portfolio risk by allowing you to profit when the standard equity markets are down.
Starting in 1990, $1,000 in the S&P 500 grew to $3,709 if you just bought and held. Using the same $1,000, assume $800 is in the S&P 500 and a total of $200 goes into two CTA programs, split evenly. We will hypothetically put $100 into Chesapeake and $100 into Rabar. Adding just 20% exposure to CTAs increased total portfolio return by 31.5% and decreased risk (measured in terms of drawdown) by 24.8%.
The power of integrating correlation thinking throughout your trading attitude is not a secret financial potion.
It is, instead, a useful tool that can benefit traders and investors in subtle and significant ways.
Justin Vandergrift, president of Chadwick Investment Group, contributed to this article.Michael Covel is author of the best-selling book “Trend Following” (Pearson, 2005). He can be reached at www.michaelcovel.com and www.trendfollowing.com.
