Commodity Trading Advisors and Managed Futures Explained
Book: Commodities: Markets, Performance, and Strategies
Editors: H. Kent Baker, Greg Filbeck, Jeffrey H. Harris
Publisher: Oxford University Press, 2018
ISBN: 9780190656010
What Are Managed Futures?
Managed futures is a fancy name for active trading strategies that use global derivative markets. It started out as pure commodity futures trading, but today it covers way more than just corn and oil. Financial futures, currency futures, interest rate futures – all of it falls under the managed futures umbrella now.
There are three main ways you can get into managed futures. The oldest route is public commodity funds, which have been around since 1948. Then in 2007, managed futures mutual funds showed up, partly because these strategies did really well during the 2008 financial crisis. As of early 2017, there were about 50 mutual funds, three ETFs, and one exchange-traded note in this category.
The other two options are private: commodity trading advisors (CTAs) and commodity pool operators (CPOs). A CTA is basically a professional who advises you on buying and selling futures contracts, options, and swaps. If you invest directly with a CTA, you get your own managed account. But CTAs charge hedge-fund-level fees, so this route is mostly for qualified (wealthy) investors.
A CPO works more like a fund of funds. It pools money from investors and hires CTAs to manage it. Think of it as a middleman between you and the professional futures traders.
How CTAs Actually Trade
CTAs are classified along two dimensions: their strategy and the markets they trade.
On the strategy side, you have discretionary CTAs and systematic CTAs. Discretionary traders analyze macroeconomic data and make judgment calls. They look at supply/demand imbalances, news events, and big-picture trends. Systematic CTAs are the opposite. They use quantitative models and algorithms to make trading decisions. No feelings, no gut instinct, just rules.
Most systematic CTAs are trend-followers. They use things like moving averages and exponential smoothing to measure momentum. When prices go up, they increase exposure. When prices go down, they pull back. Some CTAs do the opposite – counter-trend-following based on mean-reversion models.
On the markets side, CTAs are either fully diversified across many futures markets or specialized in one sector like currencies, agricultural commodities, metals, energy, or equities.
Here is how the money broke down in Q3 2016: total CTA assets were $342.3 billion. Diversified CTAs managed $203.6 billion. Financial/Metal CTAs had $93.1 billion. Systematic CTAs dominated with $290.4 billion, while discretionary CTAs managed just $26 billion. The industry clearly favors computer-driven trend-following.
The Performance Story
This is where it gets interesting. The authors show that $100 invested in the CISDM CTA Equal-Weighted index at the beginning of 1980 grew into a smooth, impressive line. CTAs outperformed large stocks, international stocks, real estate, and commodities over that entire period.
But here is the important part: the good performance is not because CTAs just buy commodities. The worst-performing index in the comparison was the long-only commodity index (S&P GSCI), which barely beat inflation. CTAs do well because they go long and short. They trade momentum. They are not just betting that commodity prices will rise.
Two stylized facts explain why CTAs look so good in a portfolio:
Low correlation with other assets. CTAs had a correlation of -0.072 with large stocks, -0.065 with international stocks, and essentially zero (0.006) with commodities over 1980-2016.
Positive performance during equity crashes. During the 15 worst months for the S&P 500 between 1980 and 2016, CTAs averaged a positive 4.12% return while stocks averaged -10.63%. In October 1987 (Black Monday), CTAs returned +14.88% while stocks dropped -21.58%. In October 2008 (Lehman Brothers), CTAs returned +5.79% against -16.70% for stocks.
That is a pretty compelling pitch for adding managed futures to a portfolio.
The Messy Reality: Biases and Benchmarks
The managed futures industry is mostly private, which creates problems for measuring performance. CTA databases rely on voluntary self-reporting. This means several biases creep in.
Survivorship bias is the biggest one. If you only look at CTAs that are still alive, you miss all the ones that failed. Fung and Hsieh (1997) found that about 20% of CTAs ceased operating each year, and survivorship bias inflated returns by about 3.4% annually. Brown, Goetzmann, and Park (2001) found the half-life of a CTA was just two years.
Backfill bias happens when CTAs start reporting only after they have good results. Selection bias comes from how index providers pick which CTAs to include. These are real issues that make CTA performance data look better than it actually is.
Different CTA indices exist from providers like Barclay Hedge, CISDM, and Societe Generale. Despite their different methodologies, the correlation among 11 manager-based indices averaged 0.94 between 2003 and 2014. So at least the indices tell a consistent story.
What Drives CTA Returns?
Traditional factor models (like CAPM or Fama-French) do a terrible job explaining CTA returns. Fung and Hsieh (2001) had a breakthrough: they modeled trend-following CTA returns using lookback straddle options. This approach explained 48% of the variation in CTA returns, compared to just 7.5% from traditional factors.
The key insight is that trend-following strategies behave like options. They have positive skewness – they make money in both extreme up and extreme down markets. This is why they do well during crises.
But at the individual fund level, the results are inconsistent. And once you control for time-series momentum strategies, as Hurst, Ooi, and Pedersen (2013) showed, the alphas mostly disappear. Translation: much of what CTAs earn comes from systematic risk factors, not skill.
Bhardwaj, Gorton, and Rouwenhorst (2014) went further. They found that CTAs do not provide significant alphas net of fees, even though gross performance is positive. The fees eat it all up.
Should You Care About Managed Futures?
The diversification benefits are real. CTAs added to a stock-bond portfolio consistently improved Sharpe ratios in the research. Kat (2004) found that managed futures had better diversification benefits than hedge funds, though lower expected returns. The sweet spot seemed to be splitting alternative allocations 50/50 between managed futures and hedge funds.
But the fee issue is serious. Managed futures strategies follow relatively mechanical rules. As Miffre (2016) concluded, long-short commodity investing consistently outperforms long-only approaches. If these strategies can be replicated cheaply, do you really need to pay hedge-fund-level fees? A trend toward low-cost passive managed futures products may be coming.
The bottom line from Chapter 12: managed futures and CTAs offer real diversification benefits and crisis protection. But the high fees, survivorship bias in the data, and lack of persistent alpha mean investors should approach with open eyes. The strategy works. Whether the product is worth the price is another question entirely.
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