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A performance chaser, knife catcher, and seed investor walk into a bar…

March 4, 2013

 

Ok, so this isn’t the start to a trite “inside baseball” financial joke. Don’t get us wrong – we love a laugh – but bad investing habits are no laughing matter. We’ve found that some investors become their own worst enemy, as reliance on familiar investing tropes mutates into a tunnel vision that handicaps their portfolio. No, this is no joke. This has to do with different types of investors and how they become interested in different managed futures programs.

What kind of investor are you?  Do you chase returns? Look for bargains?  Do you buy the hype of a brand new manager?  Or stick with the “brand names” of the industry? Are you a sucker for low correlations?

It’s not that these metrics are unimportant. The problem comes when one metric is given all the power, and that’s the kind of investing we discourage. However, as the saying goes, talk is cheap, so we decided to put our money where our mouth was, and put some numbers to these different types of investors to show just how well – or poorly - they do relative to the average CTA.

Test Methodology

To test this, we identified several investor types: the Performance Chaser, the Knife Catcher, the Seed Investor, and the Correlation Nut; and then assigned a theoretical  allocation methodology to each type (allocating to the top 15 programs by last year’s returns, the programs with the lowest correlations to stocks, and so on… we’ll get there in a minute).  

We ran our tests on our scrubbed database, and only considered data after the managers had started reporting to the BarclayHedge database. If your track record went back to 2002, but you didn’t start reporting until 2009, we didn’t include any of the data before ’09 (combatting instant history bias). We did, however, include programs no longer managing client funds (combatting survivorship bias).

And because we’re not looking to sugar coat anything, we looked at performance between 2008 and 2012 – including one of the worst performance stretches in the history of the asset class.

As an important note, these tests are meant to serve as educational examples. There are an infinite number of possible portfolio combinations that could occur in each of these investing frameworks, each with their own performance profile. The exercise is merely meant to illustrate what the possibilities might look like, to help better inform your perspective as you approach the managed futures allocation process.

So, without further ado…

The Performance Chaser

With how frequently we warn against the evils of chasing performance, we sometimes get to feeling as though we’re living in an echo chamber. The truth is, if we had a dollar for every time an investor asked us about who’s doing well right now, we could probably invest in Winton directly.   

It makes some sense, intuitively speaking, to find an investment that has done well recently in hopes that it will keep doing well, but as the NFA is fond of reminding you, past performance is not necessarily indicative of future results. Beyond the disclaimers, our experience has shown us that managed futures program performance tends to be cyclical, creating a lot of risk for investors seeking out the high fliers.

We wrote a newsletter on the subject of performance chasing  in individual allocations a couple of weeks ago, but what happens to a portfolio when sky rocketing returns are your sole criterion?

To find out, we looked at the top 15 managed futures programs by their annual return for each of the past five years. We found the top 15 programs by their 2007 return,  then calculated their return for the following year to determine what our returns might have been had we allocated on the back of a stellar year. We then repeated the process for each year after, rotating in new programs as they became the year’s top performers and calculating aggregate performance.  
Here’s what we found:

Performance Chasing on Average in Managed Futures
Disclaimer: 
Past performance is not necessarily indicative of future results. Hypothetical performance comes with its own limitations – please see full disclaimers below for details. The CTA Index Average is the average of the BarclayHedge CTA Index, Newedge CTA Index, and Dow Jones Credit Suisse Managed Futures Sub-Index. Managed futures indices provide a glimpse of the asset class, and do not include the performance of all CTAs in the managed futures universe.

Ok, honestly? Even we were a little surprised to see this performance chasing strategy outperforming, on average, CTA indices composed of the biggest names in the industry. We had half-expected the return to be negative.  Sure, the max drawdown and average annual volatility were both significantly higher for the performance chaser than the CTA Index averages, but that was somewhat of a given, as high return programs usually come with higher risk.  When looking at measures such as the Sharpe Ratio and Mar Ratio (return/max drawdown), the performance chaser wasn’t all that much worse than the CTA Index average.

What was going on here? Well, for starters, this isn’t really performance chasing. Performance chasing tends to have an emotional drive behind it, with impulsive excitement over big returns guiding allocation timing. Our version of performance chasing was systematic in nature, providing a consistency you’d be less likely to see in most flesh and blood investors. Plus, there’s a bit of a smoothing effect in that data. First we averaged 15 programs for each year in question. Then we averaged those years together. Double the average, double the smoothing.

Excuses, excuses… yeah, we know. Truth be told, we just sort of stared at the data for a bit when the numbers came in, not quite believing our eyes. Then we started digging a little further. What if we looked instead at the range of possible annual returns?  As it turns out, there were dark tides at work beneath the performance chaser’s deceptively rosy results.

Performance Chasing in Managed Futures

Disclaimer: 
Past performance is not necessarily indicative of future results. Hypothetical performance comes with its own limitations – please see full disclaimers below for details.

The best case scenario looks pretty good, but when you look at the median and see half of all annual returns were below 2.81%,  and half of all drawdowns worse than -18%, the danger of chasing performance becomes pretty clear.

The Knife Catcher

What about the flip side of the equation? If chasing performance is no good, why not do the exact opposite and invest in those programs which have done the worst in a sort of “Dogs of the Dow” type approach?

We looked at this back in 2010 in our newsletter “Dogs of the Dow – Managed Futures Style”, and found that the bottom 10% of our expanded watchlist of 75 programs actually continued to underperform instead of bouncing back as is expected in the Dow theory. What about this time?

You can see below that we found similar results this time around when looking at over 300 programs. Mirroring our test of the performance chaser approach, we rotated the 15 worst performing managers from the year prior into the knife catcher portfolio annually.

Knife Catching in Managed Futures

Disclaimer:
Past performance is not necessarily indicative of future results. Hypothetical performance comes with its own limitations – please see full disclaimers below for details.

There was the very successful best case scenario where the knife was successfully caught – the kind of tale that fuels a knife catching investor. However, for the most part, the laggards remained laggards, with more than half continuing to post negative returns in the following year. In one scenario, a program followed up a banner losing year with losses of -90%!

Does that mean you should never allocate to a losing program? Of course not. As we said before, our experience indicates that managed futures performance tends to be cyclical in nature, which can make buying into a drawdown a sage allocation choice. Frankly, it just depends on the program in question. Past performance is not necessarily indicative of future results (yep, and we’ll say it again), and some of those programs may never bounce back. Using stark losses as a blanket contrarian indicator though? Probably not a great bet.

The Seed Investor

Perhaps it is our love of self made millionaires/billionaires, the allure of stories like Peter Thiel getting into Facebook on the ground level, or our obsession with trendsetting in general, but - whatever it is - there is just something about backing the little guy and hoping to make it big.

In the managed futures space, we see this drive manifested in the investor who wants that hot, new program. They want to discover the young Babe Ruth of CTAs. They know there are risks involved in allocating to infant strategies or inexperienced traders, but it’s a risk they’re willing to take. 

We’re going to pause for a moment here, because we can hear the objections coming a mile away.

But you have young and/or small programs on your focus list!

You’re right – we do. But those programs all have at least three years of performance to their name, have passed our quantitative and qualitative due diligence, and give us access to their trading so we can monitor account activity for strategy drift on behalf of our clients. We’re firm believers in the idea that an emerging manager can be an asset to a portfolio (particularly with larger CTAs delivering smaller and smaller returns – see here), and do recommend them. What we won’t recommend is an untried and untested program for the sake of newness… and we’ve seen investors do that.

To simulate the performance such a true seed investor approach might yield, we looked at the 15 smallest programs which were new to the managed futures universe in the past year by identifying programs which had started submitting their results to the Barclay Hedge in the previous 12 months, and then calculating the next year’s performance for the 15 with the smallest AUM levels.

Drum roll, please…

Seed Investors in Managed Futures

Disclaimer:
Past performance is not necessarily indicative of future results. Hypothetical performance comes with its own limitations – please see full disclaimers below for details.

Well, now we have better median stats – with the highest return and lowest max drawdown medians when compared to our other investor tropes so far. But that worst return is still nasty… and perhaps part of the reason many big pensions and endowments insist on at least $50 million in assets under management and 5 years of performance history before they’ll consider allocating to a program.

The Correlation Nut

In our last test case, we find that a little knowledge can be a dangerous thing.

One of the benefits often touted for managed future and alternative investments on the whole is that they are not correlated to traditional investments like stocks and bonds.  As a refresher, correlation is a statistical measure that describes how interrelated two sets of data are – if your portfolio is highly correlated, then you are likely to see everything go up at once (which may be nice) and everything go down at once (which can be devastating). A blend of non-correlated investments can be used to try to hedge against those devastating events - though, of course, there are no guarantees.

Some investors take this non-correlation logic a step further, and attempt to build their managed futures portfolio around these correlation levels. It’s not a terrible idea; correlation is one of the metrics we look at when we make our own recommendations. However, we’ll emphasize again- it’s just one metric.  

Are we sensing a trend yet?

To simulate a “correlation nut” portfolio, we found the 15 programs with the lowest combined correlation to both the S&P 500 and the Barclay CTA Index, using the sum of the absolute value of both correlations to arrive at the programs most non-correlated with anything else. Those programs were tracked the following year, and then we re-ran the correlation test, selected 15 new programs, and so on.

Wash, Rinse, Repeat.

Correlation Optimization in Managed Futures

Disclaimer:
Past performance is not necessarily indicative of future results. Hypothetical performance comes with its own limitations – please see full disclaimers below for details.

With a correlation heavy approach, there are certainly demonstrable benefits. The risk profile of the strategy is far more palatable than some of the others we’ve looked at, with the median max drawdown of the portfolio at just -4.61%.  The worst case scenario brought less than half the damage of the other strategies did. These correlation nuts may be on to something… but the median return is still lower than the average of CTA Indices.

Rise of the Algo

There is a moral to this story, boys and girls. Focusing on one factor alone to guide your managed futures allocations is wrought with danger. What’s the alternative?

We could have just taken the easy way out here, and told you that it’s the wisdom and guidance of an Attain Capital portfolio specialist, but that would be pretty boring. Instead, we decided to test out the culmination of that wisdom and experience: our flag ranking system.

We’ve known for years that the single metric approach to managed futures allocation wasn’t going to work no matter how you sliced it. The problem is that a multi-variable approach is necessarily complex, and with thousands of registered CTAs on the books, downright daunting. For years, we worked to create an algorithm that would hold all of these important data points in context, and provide a more balanced hierarchy for program evaluation. Last year, we set it live.

How does the algorithm work? Well, for a program to even be considered, it had to have a track record of at least 36 months, be a registered CTA with the NFA, offer managed accounts, and be a viable business concern.  We then measured the program across eleven different metrics related to return, risk, correlation levels, ease of access (minimum account size), and length of track record. Within these categories, risk was given more emphasis. This stood in contrast to our previous rankings, where calculations over 25 metrics wound up tilting statistics to those with a higher ROR, possibly biasing things towards those programs with higher all-time compound rates of return.  

We also time-weighted the statistics, evaluating each metric across 1, 3, 5, and 10 year time periods in addition to the full length of the program's performance. This focus on performance and risk control across time frames insures that great returns far back in a program’s track record don’t skew their ranking, and, likewise, that newer programs who haven’t "lived through the tough times" don’t dominate the rankings (as they earn scores of 0 for any period they weren’t active – i.e. the 10yr period for a program with just 6 years of track record).

Our new methodology was only released at the end of 2011, so we only have one full year of actual results since the algorithm was set live. At the same time, there was no reason we couldn’t run the algorithm on past performance to determine how the rankings would have played out during those time periods, particularly since all the other tests we ran did just that.

And besides…we really wanted to see how our fancy ranking algorithm stacked up against the simplistic allocation models outlined above.

So we ran the ranking algorithm on the scrubbed database of 2007 – finding the Top 15 programs by our "flag" ranking -  and then calculated the 2008 performance for each of those programs. We repeated the process for each subsequent year. Here were the results:

Attain Capital Ranked Program Performance

Disclaimer:
Past performance is not necessarily indicative of future results. Hypothetical performance comes with its own limitations – please see full disclaimers below for details.

Not too shabby.  One of the best median returns in our test, a reasonable median max drawdown, and the smallest “worst return” of the bunch.  We’ll stop short of taking a bow – the returns sure could be higher, and we’re definitely sacrificing some home runs, with this best return smaller than all the others. But when looking at risk adjusted ratios, the ranking algorithm allocation method does yield the best MAR ratio (return/drawdown) of all the simplistic approaches:

MAR Ratio by Allocation Strategy in Managed Futures

Disclaimer:
Past performance is not necessarily indicative of future results. Hypothetical performance comes with its own limitations – please see full disclaimers below for details.

And when changing the view to consider the worst annual return of each test set instead of a ratio on the median values,  we really see the benefit of this multi-metric approach – with the worst return across 15 programs during the 5 year period just -17% (versus an average of -71% for the other 4 cases). 
Worst Allocation Strategy Returns in Managed Futures 
Disclaimer: 
Past performance is not necessarily indicative of future results. Hypothetical performance comes with its own limitations – please see full disclaimers below for details.
 

Hope for the Best, Plan for the Worst

In a perfect world, we’d be able to identify an optimal allocation strategy, find some way to patent it, and retire to Tahiti. But it’s not a perfect world, and even the best performing of allocation strategies fails when viewed as an absolute. Cookie cutter allocation prescriptions ignore the more important universal truth: each investor is unique. Each investor has different goals, means, and experience. Most importantly, each investor has a different appetite for risk.  

That’s why not even our flag rankings should be seen as a panacea for all that ails your managed futures portfolio (though the doctors are in, if you’d like to give us a call). It may not just be one metric, but it is just one weighting of those metrics. Your internal ranking algorithm might punish a program more severely for volatility, or be more tolerant of steep drawdowns if the average duration is short.

And this is the part where we tell you that there is one specific point in time at which we recommend looking at one metric by itself, and that is when considering the down side. Technically speaking, any futures based investment has a worst case scenario of losing more than you deposited in your account. But when we talk about worst case scenarios, we’re talking about the worst tested result in a portfolio combination. Indeed, we often recommend clients assume a correlation of 1.00 to stress test portfolios, envisioning a world where the worst max drawdown will happen for each program in their portfolio simultaneously.  Sure, the chances of that happening are slim, but they are greater than zero.

There’s a trite relationship meme that’s been making the round which proclaims, “If you can’t handle me at my worst, then you sure as hell don’t deserve me at my best.” If managed futures program track records could speak, this is what they would say to investors. Whichever variables you consider when constructing your portfolio, before you pull the trigger, take a moment to consider what happens when it’s having a bad hair day. Take a look a long, hard look at how bad it could be in a sort of six sigma test of robustness.

This isn’t meant to be a scare tactic; it’s meant to be preparation. We’ve found managed futures performance to be largely cyclical, benefitting investors who allocate for periods of three years or longer. Losses are a part of those cycles, but the fact of the matter is that unless you’re able to handle managed futures at its worst, you’ll never see it at its best.

 

Conclusion

There’s a reason managed futures is best suited for sophisticated investors. It’s complex. There is no magical allocation equation that will solve all your problems, but that doesn’t mean the investing process needs to be a grueling one. You don’t need to do it alone.

In all seriousness, it can really help to have an ally in the managed futures space. There are a lot of options out there, and even more data that comes along with them. The complicated nature of the asset class can make defaulting to these common investor mindsets tempting, but stay strong. Your perfect portfolio is out there. Don’t settle for less.

DISCLAIMER:

 

 

IMPORTANT RISK DISCLOSURE


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IMPORTANT RISK DISCLOSURE
Futures based investments are often complex and can carry the risk of substantial losses. They are intended for sophisticated investors and are not suitable for everyone. The ability to withstand losses and to adhere to a particular trading program in spite of trading losses are material points which can adversely affect investor returns.

Past performance is not necessarily indicative of future results. The performance data for the various Commodity Trading Advisor ("CTA") and Managed Forex programs listed above are compiled from various sources, including Barclay Hedge, Attain Capital Management, LLC's ("Attain") own estimates of performance based on account managed by advisors on its books, and reports directly from the advisors. These performance figures should not be relied on independent of the individual advisor's disclosure document, which has important information regarding the method of calculation used, whether or not the performance includes proprietary results, and other important footnotes on the advisor's track record.

The dollar based performance data for the various trading systems listed above represent the actual profits and losses achieved on a single contract basis in client accounts, and are inclusive of a $50 per round turn commission ($30 per e-mini contracts). Except where noted, the gains/losses are for closed out trades. The actual percentage gains/losses experienced by investors will vary depending on many factors, including, but not limited to: starting account balances, market behavior, the duration and extent of investor's participation (whether or not all signals are taken) in the specified system and money management techniques. Because of this, actual percentage gains/losses experienced by investors may be materially different than the percentage gains/losses as presented on this website.

Please read carefully the CFTC required disclaimer regarding hypothetical results below.

HYPOTHETICAL PERFORMANCE RESULTS HAVE MANY INHERENT LIMITATIONS, SOME OF WHICH ARE DESCRIBED BELOW. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE SHOWN; IN FACT, THERE ARE FREQUENTLY SHARP DIFFERENCES BETWEEN HYPOTHETICAL PERFORMANCE RESULTS AND THE ACTUAL RESULTS SUBSEQUENTLY ACHIEVED BY ANY PARTICULAR TRADING PROGRAM. ONE OF THE LIMITATIONS OF HYPOTHETICAL PERFORMANCE RESULTS IS THAT THEY ARE GENERALLY PREPARED WITH THE BENEFIT OF HINDSIGHT. IN ADDITION, HYPOTHETICAL TRADING DOES NOT INVOLVE FINANCIAL RISK, AND NO HYPOTHETICAL TRADING RECORD CAN COMPLETELY ACCOUNT FOR THE IMPACT OF FINANCIAL RISK OF ACTUAL TRADING. FOR EXAMPLE, THE ABILITY TO WITHSTAND LOSSES OR TO ADHERE TO A PARTICULAR TRADING PROGRAM IN SPITE OF TRADING LOSSES ARE MATERIAL POINTS WHICH CAN ALSO ADVERSELY AFFECT ACTUAL TRADING RESULTS. THERE ARE NUMEROUS OTHER FACTORS RELATED TO THE MARKETS IN GENERAL OR TO THE IMPLEMENTATION OF ANY SPECIFIC TRADING PROGRAM WHICH CANNOT BE FULLY ACCOUNTED FOR IN THE PREPARATION OF HYPOTHETICAL PERFORMANCE RESULTS AND ALL WHICH CAN ADVERSELY AFFECT TRADING RESULTS.