Managed Futures & Trend Following – Inside the Black Box

It goes without saying that 2022 has been a difficult year across markets. Investors have had to contend with an inflationary bear market for which the traditional playbook has proven woefully inadequate. NASDAQ and high yield debt, the darlings of yesteryear, have fallen from grace with few exceptions. Treasuries, the most common hedge against stock volatility, have suffered their worst drawdown in at least the last 70 years (and it’s not close):

Times such as these provide good cause for reflection. Portfolio managers and allocators are generally charged with building diverse portfolios that balance growth and safety of capital over an intermediate to long-time horizon. Treasuries have often filled the role of diversifier and risk-off asset. However, if Treasuries are expected to be a less effective hedge against risk assets in the future, then we can anticipate portfolio construction to look very different going forward. Amongst the questions PMs should be asking is if other strategies or asset classes exist that potentially enhance diversification and deliver consistent returns?

In this note, I’ll be making the case for managed futures as one such asset class. Managed futures are not well known to most investors, but, as we shall see, have some attractive properties especially at times of high volatility. We’ll begin by examining the performance and economic logic of managed futures as a strategy. From there, we’ll investigate the implementation of managed futures in liquid mutual funds which all investors can access. We’ll see that not all funds are created equal (surprise!). Finally, we will look at portfolio construction and quantify the impact of managed futures in the context of a well-diversified portfolio.

The Trend is Your Friend

The following quote from Dr. John Lintner (of CAPM) may provide some motivation as we undertake the study of managed futures:

“Indeed, the improvements from holding efficiently selected portfolios of managed [futures] accounts or funds are so large – and the correlations between the returns on the futures portfolios and those on the stock and bond portfolios are surprisingly low (sometimes even negative) – that the return/risk trade-offs provided by augmented portfolios consisting partly of funds invested with appropriate groups of futures managers… combined with funds invested in portfolios of stocks alone (or in mixed portfolios of stocks and bonds), clearly dominate the trade-offs available from portfolios of stocks alone (or from portfolios of stocks and bonds). Moreover, they do so by very considerable margins.

The combined portfolios of stocks (or stocks and bonds) after including judicious investments in appropriately selected sub-portfolios of investments in managed futures accounts…show substantially less risk at every possible level of expected return than portfolios of stock (or stocks and bonds) alone. This is the essence of the “potential role” of managed futures accounts (or funds) as a supplement to stock and bond portfolios suggested in the title of this paper.

Finally, all the above conclusions continue to hold when returns are measured in real as well as in nominal terms, and also when returns are adjusted for the risk-free rate on Treasury bills.”

– Lintner, The Potential Role of Managed Commodity-Financial Futures Accounts (and/or Funds) in Portfolios of Stocks and Bonds (1983)

This passage provides several tantalizing clues on the possible role of managed futures in a portfolio. Namely, that such strategies improve the risk/return profile of portfolios of stocks and bonds, exhibit meaningfully low correlation to traditional assets, and improve returns on both an absolute and risk-adjusted basis. We shall evaluate each of these claims in turn.

Economic Rationale

The primary driver of returns for managed futures strategies is trend-following or momentum investing; that is, buying assets have that recently been going up and selling (i.e., shorting) assets that have recently been declining. Trend based strategies are typically applied to liquid futures contracts across a wide range of markets including equity indices, rates, commodities (energy, agricultural, and industrial), and currencies. Most investors are not exposed to commodities or FX so from the simple perspective of traded instruments managed futures have the potential to introduce new sources of risk and return.

Momentum investing has a rich academic history and is widely regarded as an essential factor for explaining the performance of stock portfolios (Carhart 1997). The evidence in support of trend-following is similarly robust. Pedersen, Ooi, and Hurst (2017) analyze 137 years of performance for a time-series momentum strategy and conclude that such strategies perform well across different macroeconomic environments and have a propensity to outperform during times of macro- stress.

The chart below depicts quarterly returns from Jan-1990 to April-2022 for the Barclay’s BTOP50 Index against returns for the MSCI World. The BTOP50 Index seeks to replicate the overall composition of the managed futures industry with regard to trading style and overall market exposure. Also included is the fitted line for a second-degree polynomial. The plot shows a distinctive “smile”; characteristic of trend-followers. This suggests that a key feature of managed futures strategies is that they tend to be “long volatility” and outperform in both extreme up and extreme down markets.

Set Up and Approach

For this study we’ll be considering the period from Jan-1990 to April-2022. The Barclays BTOP50 Index  (henceforth the BTOP50) will serve as our benchmark for managed futures strategies. Returns and summary statistics are calculated on a monthly and quarterly basis (it will be made explicit which is being used).

In Part I we will examine the empirical facts of managed futures performance and how the strategy relates to other asset classes. Part II will investigate the implementation of managed futures in publicly traded mutual funds. Futures markets are highly liquid and trade standardized instruments on exchange. Therefore, mutual funds are an ideal vehicle for implementing and marketing a managed futures strategy. The ultimate objective of Part II is to quantify how well publicly available products live up to the managed futures ideal; as we shall see, the devil is in the details. Part III will explore the use of managed futures in a portfolio.

Part I: Stylized Facts

­­The table below presents the summary statistics for the BTOP50 along with indices for other key asset classes. Statistics were calculated using quarterly total return data. Confidence intervals (95%) for skew and excess kurtosis are shown in parentheses.

The table shows that over the past 32 years managed futures have, on average, produced positive returns and have exhibited approximately half the volatility of global stocks. The 95% confidence interval for skew suggests that the BTOP50 has distinctly positive skewness: unique amongst the asset classes under consideration. Even US Treasuries and the Dollar (typically considered “safe-haven” assets during risk-off periods) do not exhibit statistically significant positive skewness. The histogram below provides some visual evidence of this effect. The confidence interval for excess-kurtosis isn’t quite conclusive at the 95% level but is still suggestive of heavy tails for the BTOP50. Moreover, the Shapiro-Wilk test decisively rejects the hypothesis of normally distributed returns. Interestingly, the Shapiro-Wilk fails to reject normality for 10-year Treasury and DXY returns thereby suggesting that these series are relatively well behaved.

The following chart maps the cumulative return of the BTOP50 and comparative assets over the sample period. Several interesting observations stand out. Over the full period, managed futures (as proxied by the BTOP50 in red) are the third best performing asset class; slightly edging out Treasuries (magenta) and falling just shy of IG Corporates (aqua). Over the 1990-2010 subperiod which, of course, featured the TMT Bubble and GFC, trend-following was the top performer largely because the strategy deftly avoided both of these large drawdown events and actually posted positive returns in ’08-’09.

However, since then—and until fairly recently—strategies built to profit from price trends have struggled. Since the GFC, markets have trended less than their historical norm; which presents a certain challenge if you’re a trend-follower! Part of the explanation for underperformance during the 2010’s period may be attributable to the deluge of money that flooded the industry precisely because the performance had been so impressive (see below); a period of mean reversion was inevitable.

Another critical aspect of managed futures that can be divined from the cumulative return plot: the low correlation the strategy appears to have with the other ‘traditional’ asset classes. The following chart details the rolling 12-month correlation of the BTOP50 with the other five asset classes, respectively. The solid black line in each plot shows the average correlation over the entire observation period.

While the correlations certainly vary over time, it can be seen clearly that trend-following has structurally low correlation with the other assets. At -.0302 the correlation with equities is statistically indistinguishable from zero. Equities are often the largest source of risk in diversified portfolios, and it is often desirable to hedge this risk with assets that perform well when stocks struggle. Over the past 20+ years, Treasuries have filled this role and, up until 2022, have generally done a good job. However, 2022 has revealed significant gaps in portfolios that rely solely on bonds for downside protection. In today’s environment of high inflation, sagging growth and high volatility trend-followers have excelled. When it comes to diversification, managed futures do exceedingly well.

When Things Get Extreme

Between the smile-plot and correlation diagram we are building the case that managed futures have a very important role to play in portfolio construction. Specifically, managed futures strategies appear to produce consistently positive returns across market regimes and perform particularly well in the tails. Let’s dig a little deeper into this latter point.

The following plot contains three panels. The first panel shows the average rolling 12-month return for the Barclays BTOP50 and MSCI World over the full sample period. The MSCI World has provided an average return of ~9.75% since 1990 while the BTOP50 has returned ~5.80%. Over a sufficiently long period of time the series with the highest expected return will provably outperform all other assets. However, as the cumulative return plot demonstrates, the path to get there can (and likely will) be punctuated by periods (possibly long ones) of significant underperformance and volatility.

Examining the second and third panels. Here I have sorted the returns into deciles based on the performance of the MSCI World. The idea is to show how managed futures performed when the MSCI World did particularly well/poorly. The third panel shows the average 12-month return for the 10^th (i.e., best) decile. The interpretation is as follows: during periods of “good” returns for the MSCI, what is the average “good” return for each index? The average top decile return for the MSCI World is ~34%; so very good. On the other hand, the average return for managed futures in the top decile is only about 7%. So, when equity markets really run you want to own stocks (this probably comes as no surprise!).

The second panel shows the average 12-month return for the 1^st (i.e., worst) decile. The interpretation is: during periods of “bad” returns for MSCI, what is the average “bad”? As can be seen, the average bottom decile return for the MSCI is approximately -24% while the average return for managed futures is positive 12%. This is the critical point: managed futures have a positive expectation in both up & down markets, but it is in down markets where their hedging benefits are felt most strongly; just when you need it most.

One final iteration on this theme, let us consider the extreme cases in addition to the simple averages elucidated above. The following chart depicts the maximum & minimum return for the top and bottom deciles of the MSCI World and the corresponding performance of the BTOP50.

Panel 2 shows the maximum return for decile 10 (i.e., 100^th percentile). Essentially, this panel displays the best 12-month return for the MSCI since 1990 and how the BTOP50 performed over the same period. We can see the MSCI returned about 55% while the BTOP put up 24%. In a ripping bull market managed futures can produce solid returns, but ultimately won’t keep pace with stocks (recall that the BTOP50 has much lower volatility so this observation is not necessarily surprising).

Let’s shift our attention to Panels 1 & 3; the “bad times” for stocks. Panel 1 is the maximum return for decile 1  (i.e., the 10^th percentile) of the MSCI. The least amount that the MSCI has lost over a 12-month period is approximately -13.5%. In contrast, when the MSCI was down 13.5% the BTOP50 was up 30%. Likewise, in Panel 3 we observe that the worst (i.e., 1^st percentile) 12-month return for the MSCI was a merciless -47%. Over this period the BTOP50 did lose money, but it was a very manageable -2.6%.

Bringing it all together we can make two important observations. 1) trend-following has a long-run positive expected return and, moreover, a positive expected return in both Bull and Bear markets (this is the lesson of the means chart). 2) managed futures have an asymmetric return profile. While generally failing to keep pace with stocks in a Bull market the strategy is still capable of producing solid returns. However, in Bear markets, managed futures strategies have significantly outperformed stocks, producing positive returns or, at minimum, offering substantially less downside.

Part II: Mutual Fund Analysis

In the previous section we examined the empirical facts about managed futures and set the initial groundwork for understanding the potential role of trend-following in a portfolio. We concluded that trend-following has a long-run positive expected return, performs well across different market regimes, and exhibits a low to zero correlation with other common assets. In this section we will investigate the implementation of a managed futures strategies in publicly traded mutual funds.

In order to achieve the structurally low correlation we observed, it is necessary for a successful managed futures program to trade across many distinct markets. If a trend-follower only trades equities, then the overall portfolio will substantially track the performance of the stock market and won’t inherit the benefits of diversification. This operational complexity makes the strategy very difficult (basically impossible) for retail and high net-worth investors to implement without professional assistance. As such, the best way to access managed futures is often through a mutual fund.

The marketing of managed futures via mutual funds is a relatively recent innovation with the first funds introduced in mid-2000’s. As such, for the analysis to follow we are limited to some degree by the availability of data. The funds I intend to investigate are as follows:

• Guggenheim Managed Futures Strategy Fund (RYMTX)
• AQR Managed Futures Strategy Fund (AQMIX)
• AlphaSimplex Managed Futures Strategy Fund (ASFYX)
• Arrow Managed Futures Strategy Fund (MFTFX)
• Virtus FORT Trend Fund (VAPIX)

To give a reasonable view of performance over a market cycle I limited my search to funds with data back to 2010…these five were the only ones to make the cut. To give a sense of how potentially underutilized this asset class is at the retail level, the total AUM for these five funds is less than ~$5B; equivalent to a single mid-cap stock. Indeed, the Guggenheim fund (a gigantic asset manager otherwise) only has $32M in AUM.

To supplement the mutual funds, I also include data for two private funds available only to accredited investors:

• Abbey Capital Managed Futures Strategy
  • Abbey launched a liquid version of this strategy in 2014 which can be found here.
• Millburn Multi-Markets

To measure the exposure of each fund I regressed the monthly returns against the indices discussed in Part 1. The results of the analysis are presented in the table below. Standard errors are corrected for heteroscedasticity and autocorrelation.

The table contains a massive amount of information, so we’ll begin our analysis by considering the exposure of each fund to the BTOP50. The majority of the funds selected for study exhibit a highly statistically significant relationship to the Barclays BTOP50 as evidenced by large t-statistics and near-zero p-values. Moreover, the coefficients are uniformly positive as we expected a priori. Arrow Managed Futures (MFTFX) has the largest gross exposure with a beta of ~1.93 while Millburn has the lowest exposure (of the significant funds) with a beta of ~.84. This is good! Each of these funds claim to be a trend-follower, so they better be significantly and positively exposed to the index. The single exception is the Virtus FORT Trend Fund (VAPIX). The coefficient for the BTOP50 for VAPIX is not significantly different from zero which suggests that the fund, which purports to be a “trend” fund, has no trend-like exposure which is…curious.

In order to assess the quality of managed futures strategies in vehicles accessible to the average investor we need to rigorously understand if the funds are staying true to the managed futures mandate. This is important because if we decide to allocate to a managed futures strategy then we have a certain expectation about how it will perform. We don’t want to inadvertently invest in a fund that has covertly moved into other asset classes or added incremental Market beta risk.

With this in mind, let us now turn our attention to the MSCI World. The coefficients on the MSCI World are statistically significant for Guggenheim (RYMTX), AQR (AQMIX), Arrow (MFTFX) and Virtus FORT (VAPIX). This is potentially problematic. The coefficient of .0734 for RYMTX is small in absolute terms so while there is evidence that this fund dabbles in equities the attendant beta risk may not be of particular concern. For AQMIX and MFTFX the betas are negative which suggests that both funds tend to be short global stocks. This is possibly not ideal as what we really want from a managed futures manager is lack or correlation (so neither positive nor negative) to equities. However, at least with AQR and Arrow we can be reasonable sure that if we were to invest, then we wouldn’t be adding unwanted stock market risk to our portfolio.

This brings us to VAPIX. The coefficient on the MSCI World for VAPIX is ~.61 and highly significant which implies the fund is meaningfully exposed to stocks. Indeed, if we consider the full regression output for VAPIX, we observe that the MSCI World is the sole significant exposure for the fund. Furthermore, the MSCI explains ~61% of the fund’s variance as evidenced by R². At this point, it would seem that VAPIX is essentially an expensive substitute for a portfolio of cheap beta and cash. This is not what we want to have in a managed futures provider.

As far as the other regressors as concerned, it looks like Guggenheim (RYMTX) and AQR (AQMIX) have short Treasury exposure. AQR and AlphaSimplex are short the Dollar and only Guggenheim is short commodities.

Zooming in on a couple of other interesting features, let us consider Abbey Capital. The regression for Abbey shows that the BTOP50 is the only significant risk factor. Moreover, the R² for Abbey is .88; the highest amongst the candidate funds. This suggests that Abbey is a good example of a “pure” trend-following manager which we’ll want to keep in mind when we move toward portfolio construction.

Perhaps the most confounding results come from Millburn. Millburn demonstrates pronounced exposure to the BTOP50 while the other regressors are statistically insignificant; this is good. However, the R² comes in at only .39. This indicates that a large proportion of Millburn’s variance comes from sources outside of our selected variables and that the particular strategy they are running is part trend following and part something else. Millburn utilizes a highly data driven methodology and works on an unusually short time horizon (22 days between moving from long to short). It could be that significant non-linearities exist as part of their approach that our model isn’t picking up.

Part III: Portfolio Construction

Let’s recap what we discussed so far. In Part I we examined the empirical nature of managed futures strategies using the Barclays BTOP50 index as a broad proxy for performance. We demonstrated that trend following strategies have a positive expected return over time and that this return is uncorrelated or weakly correlated with traditional assets like stocks, fixed income and commodities. Furthermore, we were able to show that managed futures tend to have an asymmetric return profile and generally perform well in environments of high volatility when other assets typically struggle.

In Part II, we evaluated a set of public and private funds to assess how well managed futures are implemented in investable vehicles. We discovered that the degree of implementation differs significantly amongst managers and that we must take care during the selection process in order to avoid unwanted or redundant risk exposures.

In Part III, we’ll put all of these pieces together in a portfolio to see how managed futures impact the risk/return profile and potentially benefit investors. However, one of the issues that we need to address upfront is the lack of data. As mentioned previously, data for the majority of managed futures mutual funds only extends back to around 2010. In the world of finance, a decade of returns usually isn’t enough to achieve robust results. Therefore, for this part we’ll narrow our focus and only work with the funds with the longest return histories. For our case, the funds with the longest histories are Millburn and Abbey which begin in October 2004 and January 2002, respectively. It’s unfortunate that we are unable to go back further, but 18 years of data should provide us with a reasonable perspective from which to draw conclusions.

For this part, I constructed four portfolios:

• Simple Benchmark
  • 50% MSCI World
  • 20% Treasuries
  • 20% Corporate Bond
  • 5% DXY
  • 5% Commodities
• Barclays BTOP50 Benchmark
  • 40% MSCI World
  • 20% Barclays BTOP50
  • 16% Treasuries
  • 16% Corporate Bonds
  • 4% DXY
  • 4% Commodities
• Abbey Portfolio
  • 40% MSCI World
  • 20% Abbey Capital Managed Futures Strategy
  • 16% Treasuries
  • 16% Corporate Bonds
  • 4% DXY
  • 4% Commodities
• Millburn Portfolio
  • 40% MSCI World
  • 20% Millburn Multi-Markets Strategy
  • 16% Treasuries
  • 16% Corporate Bonds
  • 4% DXY
  • 4% Commodities

The Simple Benchmark represents a fairly typical allocation of diverse assets to serve as a good baseline. To build the managed futures alternatives I added 20% of the BTOP50, Abbey Capital, and Millburn, respectively, by taking pro-rata from the other asset classes. The BTOP50 Benchmark serves as a good “industry” benchmark portfolio from which to compare the Abbey and Millburn portfolios.

The cumulative return plot and summary statistics table below detail the results.

The cumulative return plot shows significant overlap between the Benchmark, Abbey and Millburn portfolios. The differences amongst the portfolios can be more clearly discerned from the table. We observe that on a cumulative and annualized return basis Millburn slightly edges out the Simple Benchmark and Abbey portfolios; however, practically speaking, all three are materially the same. The Barclays portfolio is the laggard falling meaningfully shy of the other three alternatives.

Turning our attention to risk adjusted statistics the variation amongst the portfolios begins to reveal itself. The Simple Benchmark appears to be significantly more volatile than the other three with a standard deviation of 8.45% v. 7.23%, 7.43% and 7.02% for Abbey, Millburn and Barclays, respectively. From the perspective of the Sharpe Ratio, Abbey appears to be the most efficient of our options followed closely by Millburn and well ahead of the Simple Benchmark. From an absolute and risk-adjusted perspective, the Abbey portfolio is looking quite attractive.

What about blow ups? We noted in Part I that managed futures strategies appear to have skewed and heavy tailed return distributions which may lead to downside risks not well captured by volatility and Sharpe alone. The Sortino Ratio, a variation of Sharpe that focuses on the volatility of negative returns, is highest for the Abbey Portfolio. This suggests that Abbey has the lowest downside risk of the candidate portfolios and, crucially, ahead of the Simple Bench. Finally, we consider the impact managed futures strategies have on drawdown. All three managed futures alternatives have experienced smaller drawdowns than the Simple Bench. In the case of Abbey, the drawdown is almost 10% lower!

Taken together, the portfolio risk analysis paints a very clear and compelling picture: trend-following introduces a source of independent and uncorrelated returns. The inclusion of managed futures in a portfolio does not appear to compromise total return over time as evidenced by cumulative and annualized returns. Moreover, incorporating trend following into your asset allocation leads to lower portfolio volatility, smaller drawdowns and a general improvement in the risk-adjusted statistics. This is what we had hoped to find!

Concluding Remarks

This post has been a deep dive into managed futures and trend-following strategies. We learned that managed futures have several properties that make them attractive investments: namely, uncorrelated returns and crash-protection. We demonstrated that the implementation of the strategy can differ greatly by manager and that we must be selective when considering how to incorporate managed futures into a portfolio. Finally, we discovered that such strategies add meaningfully to traditional portfolios through lower risk and enhanced efficiency without compromising long run returns.

Hopefully, you have found this article useful for your own investing. As always, thanks for reading!

-Aric Lux.