Managed Futures Strategies

April 23, 2012

Managed Futures Strategies

In the past, managed futures investing has been associated with complex trading strategies, high fees, high minimums for investment and lockup agreements that resemble the unregulated hedge fund industry. Despite such obstacles, the managed futures industry has grown from virtually nothing in 1980 to more than $300 billion at the end of 2011.1 In recent years, the asset class and investment approach has started to become democratized in more retail-friendly vehicles. Today investors can access the benefits of managed futures by not only investing with the traditional commodity trading advisors (CTAs),2 but also by investing in mutual funds3 and exchange-traded funds.4

In this paper, we review some history of managed futures strategies, discuss the reason why investors have included them in their portfolios, and discuss the challenges and various solutions for crafting passive, rules-based benchmarks for measuring the returns of this investment approach.

Origins Of Futures Trading
Prior to the early 1970s, futures contracts exchanged hands principally as a means for producers and consumers of agricultural commodities to protect and lock in prices for their production or their supply. The history of futures contracts for hedging purposes is believed to date back thousands of years to the ancient city of Babylon where people exchanged contracts on livestock—goats, pigs, sheep and other items—to trade one good for another and lock in a set of prices for these goods.

The growth of futures trading expanded with the introduction of interest rate and currency trading that typified “financial futures” in the early 1980s. Now the markets are accessed by speculators, hedgers and investors alike in over 100 liquid markets ranging from equity futures, financial futures and commodity futures 24 hours per day. This rapid increase in trading instruments also gave birth to the CTA, or a third-party decision-maker who is charged with making buy or sell decisions on an investor’s behalf.

The term “CTA” tends to be vague, and the term “commodity” may not always accurately reflect the nature of the underlying securities in many of these strategies. Over time, the composition of CTAs has shifted; in the 1980s, agricultural futures represented about 64 percent of market activity, metals futures accounted for 16 percent, and currency and interest rate futures totaled approximately 20 percent.5 Today financial futures such as currencies, interest rates and stock indexes dominate trading in the global futures markets. CTA composition has also reflected this evolution, as many CTA portfolios are heavily invested in noncommodity-related futures contracts.

Benefits Of Incorporating Managed Futures In A Portfolio
As managed futures have grown in popularity, it is important to understand why many seek to diversify their traditional stock and bond portfolios to include this alternative asset class, including:

  1. Potential for returns in up and down markets: The flexibility and ease in taking long and short positions allows profit both from rising as well as falling markets.
  2. Noncorrelation to traditional investments: Returns of managed futures strategies have historically been noncorrelated to traditional stock and bond market returns over long-term periods.
  3. Enhanced diversification: The noncorrelation of managed futures, combined with their potential ability to provide returns during up and down markets, help provide enhanced overall portfolio diversification.

During the volatility experienced in the markets during the financial crisis of 2008, there were few asset classes that provided adequate desired diversification and negative correlation. Managed futures were one of the select areas that did provide that diversification potential. A growing number of retail-friendly vehicles like mutual funds and ETFs are making access to this once-institutional-only product set more easily attainable.

An Institutional Product? Active Managed Futures Strategies
In recent years, the vast majority of current assets under management in the managed futures space are with active CTA managers. Active managers in the managed futures space have traditionally charged hedge-fundlike fees (base investment management fees plus performance fees) for the prospects of generating exposure to a managed futures program.

CTAs tend to be perceived as a complex and an almost scientific sector of the investment management industry, but the general objective of many of these strategies is to simply follow trends. Surveys show that upward of 70 percent of CTAs report that they make trend-following or momentum-based trading decisions.6

While managers employ a variety of proprietary processes and techniques to identify and capture price trends, the general objective does not change. Active managers justify their high fees by claiming they generate meaningful alpha,7 but academic research suggests that CTA returns comprise a significant amount of systematic exposure to trend-following strategies.

The academic paper by Professors Gorton, Rouwenhorst and Bhardwaj had a provocative title: “Fooling Some of the People All of the Time: The Inefficient Performance and Persistence of Commodity Trading Advisors.” They write:

We estimate that CTAs on average … captured most of their performance through charging fees. Yet, even before fees we find that CTAs display no alpha relative to simple futures strategies that are in the public domain. We argue that CTAs appear to persist as an asset class despite their poor performance, because they face no market discipline based on credible information. Our evidence suggests that investors’ experience of poor performance is not common knowledge.

This research suggests that exposure to simple trend-following strategies can explain most of the average outperformance of CTAs before fees.

In Search Of A Managed Futures Benchmark
If the majority of CTA performance can be attributed to systematic exposure to the market, then how does one identify that exposure?

Investors have grown accustomed to comparing investments within asset classes to a representation of the asset class, but does such a representation of CTAs exist? While there are certainly indexes that claim to represent returns of the asset class going back to 1980, such as the Barclays CTA Index8 or the CASAM/CISDM CTA indexes (equal weighted and asset weighted),9 these indexes have two main drawbacks in estimating returns from managed futures strategies:

  1. Returns may be biased upward: The returns for indexes of CTA managers tend to be biased upward as a result of the voluntary nature of self-reporting performance. A CTA with poor performance for a period of time is less likely to report unfavorable returns to these types of databases, resulting in an index that includes mostly favorable performance.
  2. Lack of a natural measuring stick: Other asset classes like equities or bonds have a natural benchmark for performance reporting. Market-capitalization-weighted benchmarks for equities or bonds mathematically represent the average return in aggregate to investors in the equity or bond markets. These are meaningful performance measures. How would one apply that to the managed futures space?

CTAs are providing exposure to various asset classes—from equities, bonds, currencies and commodities. In the end, a benchmark for managed futures must systematize the type of exposure these CTAs provide.

There is much debate about the best way to measure the performance of managed futures strategies. Given the academic research that suggests CTA performance can be explained by exposure to systematic trend-following rules, we propose that a passive, rules-based selection and weighting approach based on a meaningful measure of size of the underlying constituents provides an effective representation of managed futures strategies.

The Rise Of Systematic Trading Approaches In CTAs
While discretionary CTA managers still exist, today approximately 80 percent of the universe of managed futures trading advisors comprises strategies that rely on systematic, computerized approaches to generate market-trading decisions.5 In theory, systematic trading strategies strive to eliminate any element of luck in generating alpha. As with most investment decisions, there are strengths and weaknesses associated with systematic strategies:


  • Decisions are determined by computer models, which help maintain a consistent and disciplined investment approach by removing emotion and reliance on manager discretion
  • Allows for the historical study of price data to research, develop and test strategies that results in a repeatable process that can be quantified and studied to improve consistency
  • Portfolio construction using various markets and sectors to increase diversification
  • Investing in a passive manner diminishes the impact of some of the traditional obstacles to investing in CTAs and also lessens the burden of how to find and monitor the best CTA managers


  • Systematic trading systems cannot adapt to news or environments that are different from past environments from which the models were initially derived

Constructing A Systematic Approach To Managed Futures
In constructing a passive approach, two index design decisions must be made:

  1. What constituents to include
  2. How to weight them

While other index-based approaches exist in the market, we will focus on the Diversified Trends Indicator (DTI) in this piece for illustration. Developed by Alpha Financial Technologies LLC10 the DTI comprises 24 liquid commodity and financial futures contracts that are grouped into 17 sectors with 50 percent exposure to commodity futures and 50 percent exposure to financial futures (defined as currency and interest-rate futures only).

While equity futures certainly are some of the most actively traded futures contracts, there is a reasonable debate about whether they add value to a managed futures program if the goal is creating a noncorrelated vehicle to the traditional equities futures and bond portfolios. In our judgment, leaving equities out of the constituent list lowered correlation of a managed futures strategy to traditional equity allocations.

Figure 1 illustrates the futures composition of the DTI; note this is a long/short index based on trends. In the DTI, energy can only be long or flat, never short. When energy is flat, the allocations are spread pro rata to the remaining sectors.

The weights for the commodities subsectors are designed to reflect and approximate the relative production value and liquidity of the various commodities, one of the most natural ways of measuring their performance. Meanwhile, for currencies and interest rates, the weights reflect and approximate the various country exposures to global GDP.

DTI Component & Sector Weights

DTI Employs A Rules-Based Methodology
To determine whether a sector should be long or short, the DTI compares a sector’s close at month-end to a seven-month weighted moving average for the sector. If the sector closes above its average at month-end, it will be held long for the forthcoming month. If the sector should close at month-end below its seven-month weighted moving average, it will be short in the upcoming month (except for energy, which would be flat). Sectors are rebalanced back to their base weights (as highlighted above) on a monthly basis. The example above assumes the energy sector is long; if the energy sector is flat, the weightings would be different than those shown in Figure 1. The underlying components of multicomponent sectors are only rebalanced annually. Using a weighted moving average method places a greater weight and importance on prices that have occurred more recently.

One of the big dilemmas that creators of trend-following strategies face is determining the appropriate length of time to establish a trend. Short-term trend-following strategies aim to generate favorable returns at the risk of eroding profits by excessively trading and repeatedly investing in false trends or losing strategies. Longer-term trend-following strategies seek favorable returns by identifying and correctly trading long-term trends in the futures markets at the risk of overlooking profits from short-term volatility.

In short, the determination of the length of the moving average line involved trade-offs, but historical simulations of the strategy’s success were not dependent on just this one specific seven-month average. Other moving-average periods could also be used to form the basis of the buy/sell decision without compromising the nature of the strategy.

Characteristics Of DTI
The DTI has been calculated live in real time since 2004. Over that period, it has been negatively correlated to both U.S. equities and U.S. bonds: -0.21 and -0.17, respectively (through Dec. 31, 2011).11 The negative correlation is a key element in the diversification that managed futures strategies can bring.

There are few asset classes that can provide meaningful negative correlation. Commodities was an asset class that many assumed would provide noncorrelation, and for a large amount of time, that was true. During the financial crisis, however, one can see that the correlation of a commodity index such as the S&P GSCI Index spiked higher and started to approach 0.8 from a negative correlation in 2005.11

The DTI rolling three-year correlation also shows mostly negative correlations on a three-year basis. However, in the middle of 2011, the rolling three-year correlation was impacted by the spike in overall commodity correlations, as commodities do represent 50 percent of the DTI.11 (See Figure 2.)

Rolling 3-Year Correlation Of Various Indexes Vs. S&P 500 Index: 1/31/2003-12/31/2011

Risk/Return Statistics That Complement Traditional Portfolios
Since the index went live in 2004, the DTI has provided superior returns to the S&P 500 Index,12 with 679 basis points less annualized volatility and lower downside risk. Relative to commodity strategies, such as the S&P GSCI Index and the Dow Jones UBS Commodity Index, the DTI has performed admirably, with greater risk-adjusted returns and lower downside risk. Figure 3 also shows that the DTI’s returns since 2004 have been in line with the Newedge CTA Trend Sub-Index and the Barclay Systematic Traders Index.

A key point: The DTI was able to achieve returns within 1 percentage point per year of the Newedge CTA Trend Sub-Index with less volatility, even taking into account the fact that the Newedge CTA Trend Sub-Index involves reporting biases inherent in CTA indexes.

Annualized Risk And Return Characteristics: 1/1/2004-12/31/2011

Maximum Drawdown13
The diversification benefits and the flexibility of a long/short strategy can benefit investors in rising as well as falling markets. Investors point to low correlation and sometimes negative correlations to traditional investments, as well as favorable CTA performance in crisis events.

In recent memory, managed futures attracted attention for positive performance in the face of the financial crisis of 2008, when the S&P 500 Index was down approximately 37 percent. The ability to diversify and go both long and short has proven advantageous for the DTI, as the index has shown lower volatility than other major asset classes, save U.S. bonds.

In addition to the favorable volatility relative to the other asset classes shown, the DTI has had a maximum drawdown of 15.65 percent since 2004. This maximum drawdown was one-third of that of U.S. stocks. To put it into perspective, commodity strategies experienced a maximum drawdown of between 54 and 67 percent.

Calendar-Year Returns
Managed futures strategy funds certainly raised their profile among investors during the 2008 financial crisis. While global markets were falling off the cliff in 2008, there was a clear and discernible pattern that allowed trend followers to go short in many of the declining markets and go long those futures that reflect a flight-to-safety quality. The DTI showed returns of 8.29 percent, and the Newedge CTA Trend Sub-Index had returns of 20.88 percent. The higher returns might be explained by funds employing more leverage on their positions, while the DTI provides only one-to-one exposure to the market.

Trend-following strategies suffered in 2009 and 2011 when the equity markets were positive. A long/short managed futures strategy like the DTI can be described as one gauge of volatility in its components or strong trends in those underlying markets. When there is a lack of strong trends in the DTI’s components, the DTI’s performance is apt to suffer. This environment showed that one of the limitations of a trend-following approach could be a challenge identifying profitable trends during volatile markets. These specific volatile markets were influenced by the zero-interest-rate policy established by the Federal Reserve, as well as ongoing interventions in the currency and interest rate market by global central banks that caused wide deviations and fluctuations of price trends in commodity, currency and interest-rate markets.

Downside Table: 1/1/2004-12/31/2011

Managed futures strategies represent exposure that was once only available to the institutional investor community and characterized by high fees, lockups, leverage and opaque strategies. Only recently have managed futures strategies started to become available in liquid, transparent vehicles. Although the DTI represents exposure to one specific managed futures systematic trading strategy, there is research indicating the vast majority of CTA returns can be explained by such exposure to systematic trading approaches. The DTI serves to crystallize the exposure in a simple rules-based algorithm providing exposure to effective and representative managed futures strategies.

As the number of asset classes that provides low correlation to traditional equity and bond portfolios becomes increasingly sparse, we believe investors and advisors would benefit from understanding the pros and cons of the various managed futures offerings coming to market.


  1. Barclay Hedge Alternative Investment Databases, 2011
  2. Commodity trading advisors – investment managers charged with making buy and sell decisions usually in the commodity spot, physical and futures markets on behalf of investors. There is no guarantee that a CTA will meet his/her objective, and investors may lose money.
  3. Mutual funds – an investment vehicle in which investors pool assets together and a professional money manager makes investment decisions on the shareholders’ behalf to try to meet the fund’s objective. There is no guarantee that a mutual fund will achieve its stated objective, and investors may lose money.
  4. Exchange-traded funds – an investment vehicle in which investors buy a share of a fund that attempts to provide returns, less fees and expenses, of a stated objective (e.g., U.S. equity market, the price of gold). Investors may lose money in such investments.
  5. L’habitant, F-S, “Handbook of Hedge Funds,” The Wiley Financial Series.
  6. Bhardwaj, G., Gorton, G., Rouwenhorst, G. “Fooling Some of the People All of the Time: The Inefficient Performance and Persistence of Commodity Trading Advisors.” Yale IFC Working Paper, October 2008.
  7. Alpha is a risk-adjusted measure of the active return on an investment. It is the return in excess of the compensation for the risk borne, and thus commonly used to assess active managers’ performance.
  8. The Barclay CTA Index is an industry benchmark of representative performance of commodity trading advisors. There are currently 565 programs included in the calculation of the Barclay CTA Index for the year 2011, which is unweighted and rebalanced at the beginning of each year.
  9. The CASAM CISDM CTA/CPO indexes represent a series of both asset-weighted and equal-weighted performance indexes of commodity trading advisors and commodity pool operators in the CASAM CISDM Hedge Fund/CTA Database. Currently there are 20 indexes.
  10. Diversified Trends Indicator and DTI are registered marks of AFT and have been licensed by the fund. The fund is not sponsored, endorsed, sold or promoted by AFT.
  11. Zephyr StyleADVISOR, WisdomTree
  12. S&P 500 Index – capitalization-weighted index of 500 stocks selected by the Standard & Poor’s Index Committee designed to represent the performance of the leading industries in the U.S. economy.
  13. Maximum drawdown measures the peak-to-trough decline during a specific record period of an investment. A drawdown is usually quoted as the percentage between the peak and trough.

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