How to Replicate Trend Following Managed Futures
An Executive Summary for Investors and Investment Professionals
Introduction
Trend-following managed futures strategies offer a compelling opportunity for investors to diversify their portfolios beyond traditional stocks and bonds. By capitalizing on persistent trends across a wide range of liquid futures markets – from commodities to currencies to equity and bond indices—these strategies have historically delivered attractive returns with low correlations to conventional assets.
The flexibility to take both long and short positions allows trend followers to potentially profit in varied market conditions. This has resulted in a track record of competitive performance in both rising and falling markets, with significantly reduced volatility and drawdowns compared to stocks or bonds alone.
The aim of this executive summary is to introduce the concept of trend replication strategies, which seek to mimic the attractive characteristics of managed futures at lower cost. By identifying the key drivers of performance across major managed futures indices and the underlying strategies employed by trend-following funds, these replication approaches offer the potential to democratize access to the diversification benefits of managed futures – previously the exclusive domain of sophisticated institutional investors.
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How To Replicate Trend-Following Managed Futures
Trend-Following Managed Futures
Trend-following managed futures strategies have become a cornerstone of modern hedge fund investing, renowned for their ability to enhance portfolio diversification and generate returns uncorrelated with traditional assets. These strategies trace their roots back to the 1970s, when the establishment of liquid futures markets created new opportunities to profit from market momentum. Building on these early successes, the sophistication and complexity of trend-following approaches have evolved over the decades, leading to the development of flagship funds with long track records of attractive risk-adjusted returns.
At their core, trend-following strategies seek to identify and capitalize on persistent market trends across a wide range of futures markets, including commodities, currencies, stock indices, and bond indices. By analyzing historical price data, these systematic approaches detect patterns indicating the continuation of trends. Traders then take long positions in rising markets and short positions in falling markets, aiming to ride these trends until there are clear signs of a reversal.
Figure 1. Cumulative monthly total returns for MSCI All-Cap World Index, Barclays Aggregate Bond Index, and Barclay BTOP 50 Managed Futures Index, August 1st, 1990 – January 31st, 2023.
Source: Bloomberg. Analysis by ReSolve. ACWI and AGG returns use ACWI and AGG ETF total returns from inception. Prior to inception on March 28, 2008, ACWI ETF returns are extended with MSCI ACWI USD Total Return Index (Net) with data from Bloomberg. Prior to inception on September 26, 2003, AGG ETF returns are extended with Bloomberg Aggregate Bond Index USD Total Returns with data from Bloomberg. BTOP 50 returns are sourced from Barclay Hedge. Past performance does not guarantee future results.
This flexibility to take both long and short positions across multiple markets is a key driver of the diversification benefits that trend-following strategies offer. By profiting from both rising and falling price moves, these approaches have the potential to generate gains in diverse market environments. Coupled with the historically low correlations to stocks and bonds, this has allowed managed futures to significantly reduce overall portfolio volatility and provide a valuable hedge during equity market drawdowns.
Table 1. Monthly total return performance statistics for MSCI All-Cap World Index, Barclays Aggregate Bond Index, and Barclay BTOP 50 Managed Futures Index, August 1st, 1990 – January 31st, 2023.
| MSCI ACWI | BARCLAYS AGG | BARCLAY BTOP 50 | |
| Start Date | Aug 01, 1990 | Aug 01, 1990 | Aug 01, 1990 |
| Annualized Return | 6.08% | 5.01% | 5.56% |
| Sharpe Ratio | 0.34 | 0.78 | 0.47 |
| Annualized Volatility | 16.10% | 4.10% | 8.60% |
| Max Drawdown | -55.30% | -17.00% | -16.10% |
Source: Bloomberg. Analysis by ReSolve. ACWI and AGG returns use ACWI and AGG ETF total returns from inception. Prior to inception on March 28, 2008 ACWI ETF returns are extended with MSCI ACWI USD Total Return Index (Net) with data from Bloomberg. Prior to inception on September 26, 2003 AGG ETF returns are extended with Bloomberg Aggregate Bond Index USD Total Returns with data from Bloomberg. BTOP 50 returns are sourced from Barclay Hedge. Past performance does not guarantee future results.
As illustrated in Table 1, managed futures have delivered competitive returns to stocks and bonds with significantly lower volatility and drawdowns over the long term. By capitalizing on these compelling diversification benefits, investors have the potential to build more balanced and resilient portfolios capable of weathering various market cycles.
Table 2. Monthly return Pearson correlations for MSCI All-Cap World Index, Barclays Aggregate Bond Index, and Barclay BTOP 50 Managed Futures Index, August 1st, 1990 – January 31st, 2023.
Source: Bloomberg. Analysis by ReSolve. ACWI and AGG returns use ACWI and AGG ETF total returns from inception. Prior to inception on March 28, 2008 ACWI ETF returns are extended with MSCI ACWI USD Total Return Index (Net) with data from Bloomberg. Prior to inception on September 26, 2003 AGG ETF returns are extended with Bloomberg Aggregate Bond Index USD Total Returns with data from Bloomberg. BTOP 50 returns are sourced from Barclay Hedge. Past performance does not guarantee future results.
Replication Strategies: Democratizing Access to Managed Futures
Replication strategies offer a compelling solution for investors seeking to capture the diversification benefits of trend-following managed futures at a lower cost. By mimicking the performance of major managed futures indices, these strategies aim to provide similar return characteristics without the high fees and complexity associated with investing in individual funds.
At their core, replication strategies are designed to identify and emulate the key drivers of managed futures returns. This involves analyzing the market exposures, trading signals, and portfolio construction techniques employed by leading trend-following funds. By distilling these elements into a systematic investment process, replication strategies seek to deliver a return stream that closely tracks the performance of the target benchmark.
There are two primary approaches to developing managed futures replication strategies: top-down and bottom-up methodologies. The top-down approach focuses on inferring the average market exposures and holdings of the underlying funds by analyzing the recent returns of the target index. This typically involves using statistical techniques, such as regression analysis, to identify the key markets driving index performance. By constructing a portfolio that closely matches these exposures, investors can aim to replicate the returns of the benchmark.
In contrast, the bottom-up approach delves deeper into the specific trading strategies and signals used by individual trend-following funds. This involves deconstructing the components of these strategies, such as time-series momentum and moving average crossovers, to reverse-engineer the decision-making processes of fund managers. By combining these building blocks into a cohesive investment strategy, bottom-up replication seeks to emulate the performance of the target index at a more granular level.
Figure 3. Futures universe used for analysis. Small universe of markets highlighted in red.
Source: ReSolve. For illustrative purposes only.
Ultimately, the goal of both top-down and bottom-up replication strategies is to achieve a high degree of correlation with the performance of the benchmark index. By doing so, these approaches offer investors a more accessible and cost-effective way to gain exposure to the diversification benefits of managed futures, without the need to invest in multiple individual funds.
In the following sections, we will explore how a combination of top-down and bottom-up techniques can be used to construct a robust managed futures replication strategy. By examining the results of this replication approach, we will demonstrate the potential for these strategies to deliver attractive returns and diversification benefits, while significantly reducing costs compared to traditional managed futures investments.
Top-Down Replication Methodology
The top-down replication methodology aims to recreate the average holdings of managers in a trend-following managed futures index by determining the mix of long and short market exposures that best match the benchmark’s recent returns. This process involves running regressions of the index returns against a set of potential explanatory variables, such as returns on various futures contracts.
One of the key tools employed in top-down replication is the (Elastic Net) regression model1. This model helps to identify the most significant market exposures while avoiding overfitting.
Figure 4. Elastic Net regression seeks a portfolio that minimizes the tracking error with the SG Trend Index at each point in time.
Source: Newfound Research. For illustrative purposes only.
Accurate replication requires high-quality data on both the target index and the explanatory variables (market returns). For this purpose, daily returns of the Trend Index were sourced from Société Générale Prime Services, and futures market data were obtained from CSI Data and Refinitiv.
As a first step, each market is scaled to target the same level of volatility using its past 40 days of returns. Then the Elastic Net regression model is used to determine the best tracking portfolio of assets that best mimics the returns of the index. The Elastic Net regression model is fitted using rolling returns over various lookback periods (e.g., 20, 25, 30, 35, and 40 days), ensuring that the model dynamically adjusts to changing market conditions. The portfolio is rebalanced daily to maintain alignment with the inferred asset weights.
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How To Replicate Trend-Following Managed Futures
Figure 5. Cumulative equity of Top-Down Trend Replication strategy GROSS excess returns, January 31st, 1990 – January 31st, 2023. SIMULATED RESULTS.
Source: Data from Refinitiv. Analysis by ReSolve. Simulation returns are total returns including roll yield, in excess of the funding rate and gross of slippage, commissions, and fees. RESULTS ARE BASED ON SIMULATED OR HYPOTHETICAL PERFORMANCE. You should not take SIMULATED OR HYPOTHETICAL performance as an indication of actual or future performance. Please see below for Important Information regarding SIMULATED OR HYPOTHETICAL performance.
The top-down approach has demonstrated a high degree of accuracy in replicating the index’s returns, achieving a strong correlation with the target. However, there are periods when the fit may be less precise, highlighting the importance of combining this method with other approaches to enhance overall performance.
By leveraging historical data and sophisticated regression models, the top-down replication methodology offers a robust framework for emulating the returns of trend-following managed futures indices. This approach provides investors with a cost-effective and efficient means of capturing the diversification benefits of these strategies, democratizing access to a valuable source of portfolio resilience.
Bottom-Up Replication Methodology
The bottom-up replication approach aims to mirror the performance of trend-following managed futures indices by directly emulating the underlying trading strategies of the constituent funds. In contrast to the top-down method, which infers market exposures from relatively short-term index returns, bottom-up replication seeks to identify and replicate the specific trend-following techniques used by individual managers.
Identifying Trend-Following Strategies
At the core of the bottom-up approach is a comprehensive analysis of the trading strategies employed by managed futures funds. These strategies often involve various trend identification techniques, such as time-series momentum, moving averages, and breakout systems. To capture the essence of these approaches, a representative set of thirteen trend-following strategies was selected, with lookback periods spanning from a few weeks to over a year. By incorporating multiple time horizons, the replication portfolio aims to provide a robust representation of the diverse strategies used within the managed futures space.
Constructing the Replication Portfolio
To determine the optimal weights for each trend-following strategy in the replication portfolio, a Ridge regression model was employed. This model is well-suited for handling a large number of overlapping strategies, and ensures that all relevant predictors are included. The model was fitted using daily returns data from the SG Trend Index and individual futures markets, obtained from Refinitiv.
To validate the robustness and stability of the bottom-up replication model, k-fold cross-validation was performed. This technique involves repeatedly fitting the model on a subset of the data and testing it on the remaining portion, providing an assessment of out-of-sample performance. The resulting strategy returns were then combined to simulate the replication portfolio’s performance over the full history of the SG Trend Index. Figure 6 demonstrates that the in-sample model fits generalize well to out of sample periods.
Figure 6. Cumulative equity full-sample and cross-validated Bottom-Up Trend Replication strategy GROSS excess returns, January 4th, 1990 – January 31st, 2023. SIMULATED RESULTS.
Evaluating the Bottom-Up Replication Strategy
The bottom-up replication approach has demonstrated a high degree of accuracy in mirroring the performance of the SG Trend Index, achieving a correlation exceeding 0.8. This strong result suggests that the selected trend-following strategies effectively capture the core drivers of the index’s returns. Moreover, the consistency of the cross-validated model’s performance indicates that the replication strategy is robust and reliable over time.
Figure 7. Total trend system weights in the bottom-up trend replication strategy sorted by trend lookbacks in days, January 4th, 2000 – January 31st, 2023.
Source: Data from Refinitiv. Analysis by ReSolve.
Performance Analysis and Results
The ultimate test of futures trend replication strategies lies in their ability to accurately mirror the performance of the target benchmark. By comparing the returns of the top-down and bottom-up replication methodologies with the SG Trend Index – a leading gauge of the 10 largest trend-following managed futures funds – we can assess the effectiveness of these innovative approaches.
Figure 8. Cumulative equity of Net Replication strategy and SG Trend Index, January 31st, 2000 – January 31st, 2023.
Source: Data from Refinitiv. Analysis by ReSolve. Simulation returns include roll yield, in excess of the funding rate and net of estimated trade slippage and commissions. Results are hypothetical and do not indicate future performance.
As illustrated in Figure 8, the Net Replication strategy – which combines both top-down and bottom-up methodologies – closely tracks the SG Trend Index over the period from January 31st, 2000 to January 31st, 2023. This tight fit demonstrates the replication strategy’s ability to effectively capture the key drivers of the benchmark’s performance.
Diving deeper into the performance statistics in Table 3 reveals additional strengths of the replication approach. The Net Replication strategy not only matches the SG Trend Index’s annualized return and volatility but also delivers a higher Sharpe ratio of 0.72 compared to 0.42 for the benchmark, largely due to lower fees.
Table 3: Performance statistics for Net Replication strategy and SG Trend Index, January 31st, 2000 – January 31st, 2023.
| NET REPLICATION | SG TREND INDEX | |
| Start Date | Feb 01, 2000 | Feb 01, 2000 |
| Annualized Return | 9.13% | 4.65% |
| Annualized Volatility | 13.40% | 12.90% |
| Sharpe Ratio | 0.72 | 0.42 |
| Sortino Ratio | 1.00 | 0.57 |
| Max Drawdown | -21.20% | -24.80% |
| Correlation to SG TREND | 0.86 | 1.00 |
Source: Data from Refinitiv. Analysis by ReSolve. Simulation returns include roll yield, in excess of the funding rate and net of estimated trade slippage and commissions. Results are hypothetical and do not indicate future performance.
Perhaps most importantly, the Net Replication strategy achieves a high 0.86 correlation with the SG Trend Index, confirming that it successfully mirrors the benchmark’s return profile. By leveraging both top-down and bottom-up techniques, the replication approach adapts to evolving market conditions and captures the benefits of diversified futures exposures.
The implications for investors are clear: futures trend replication strategies offer a compelling way to access the attractive characteristics of managed futures with potentially lower costs and complexity compared to investing in the underlying funds directly. By delivering consistent returns that closely match the benchmark, these innovative strategies democratize access to a valuable source of portfolio diversification – empowering a broader range of investors to enhance their portfolios’ resilience and efficiency.
Conclusion
Trend-following managed futures strategies offer compelling diversification benefits and the potential to enhance risk-adjusted returns in traditional investment portfolios. However, accessing these benefits in a cost-effective manner can be challenging due to high fees and the complexity of investing in multiple funds. This executive summary introduces an innovative solution: futures trend replication strategies that combine top-down and bottom-up methodologies to mirror the performance of major managed futures indices.
The top-down approach leverages historical return data and sophisticated regression techniques to identify the key market exposures driving index returns. By constructing portfolios that match these exposures, investors can aim to track the performance of the target benchmark. The bottom-up approach delves deeper, reverse-engineering the actual trading strategies and signals used by trend-following funds. Aggregating these building blocks allows bottom-up replication to emulate index returns at a granular level.
Integrating these two methodologies into a robust replication strategy offers the best of both worlds, capturing broad market exposures while accounting for tactical trading decisions. Empirical evidence demonstrates that this combined approach achieves a high degree of correlation with the benchmark index, delivering superior risk-adjusted returns and stability over time.
For investment professionals and their clients, futures trend replication presents a powerful tool to access the diversification benefits of managed futures without the associated complexity and high costs. By smoothing out the significant performance variations between individual funds, replication strategies can provide consistent, category-like returns – a valuable proposition for investors seeking reliable portfolio diversification.
Capitalizing on the proven characteristics of trend-following managed futures, this replication approach offers a compelling way to enhance overall investment outcomes. With the potential for improved portfolio efficiency, reduced drawdowns, and attractive returns, futures trend replication strategies are poised to democratize access to this important alternative asset class.
In an investment landscape characterized by elevated volatility and low expected returns from traditional assets, the ability to effectively diversify portfolios is more critical than ever. By harnessing the power of futures trend replication, investors can position themselves to navigate an uncertain market environment with greater resilience and confidence in the pursuit of long-term financial goals.
[1] Elastic Net regression combines the benefits of Ridge and LASSO regression techniques to find an optimal portfolio fit. The Elastic Net regression is particularly effective in selecting a sparse model that includes only the most relevant markets at any given moment.
Disclaimers
Confidential and proprietary information. The contents hereof may not be reproduced or disseminated without the express written permission of ReSolve Asset Management SEZC (Cayman). (“ReSolve Global” or “ReSolve”). These materials do not purport to be exhaustive and although the particulars contained herein were obtained from sources ReSolve Global believes are reliable, ReSolve Global does not guarantee their accuracy or completeness.
The material is intended for informational purposes only and does not constitute an offer or solicitation of an offer or any advice or recommendation to purchase any securities or other financial instruments and may not be construed as such. The information and views/opinions expressed herein are solely those of the author. All comments, opinions and views expressed are of a general nature based on information available as of the date of publication and should not be considered as advice to purchase or to sell any securities or other financial instruments.
This information is not intended to, and does not relate specifically to any investment strategy or product that ReSolve offers. It is being provided merely to provide a framework to assist in the implementation of an investor’s own analysis and an investor’s own view on the topic discussed herein. The replication strategy and themes discussed herein may be unsuitable for investors depending on their specific investment objectives and financial situation.
Important information regarding SIMULATED OR HYPOTHETICAL performance and results. SIMULATED OR HYPOTHETICAL RESULTS HAVE CERTAIN INHERENT LIMITATIONS. UNLIKE THE RESULTS SHOWN IN AN ACTUAL PERFORMANCE RECORD, THESE RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, BECAUSE THESE TRADES HAVE NOT ACTUALLY BEEN EXECUTED, THESE RESULTS MAY HAVE UNDER OR OVER-COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF LIQUIDITY. SIMULATED OR HYPOTHETICAL TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. PAST PERFORMANCE (WHETHER ACTUAL OR HYPOTHETICAL) IS NOT INDICATIVE OF FUTURE PERFORMANCE.
It is expected that the simulated or hypothetical performance will periodically change as a function of both refinements to our simulation methodology and the underlying market data. There is no representation that any fund or account will perform as the hypothetical or other performance charts indicate.
Equity line results show the growth of $100 assuming the purchase and sales of securities were executed at their daily closing price. Profits are reinvested and the simulation reflects assumptions regarding commissions, transaction costs of buying and selling securities, management fees and performance fees as described in the relevant sections. Any strategy carries with it a level of risk that is unavoidable. No investment process can guarantee or achieve consistent profitability all the time and will necessarily encounter periods of extended losses and drawdowns.
ReSolve Asset Management SEZC (Cayman) is registered with the Commodity Futures Trading Commission as a commodity trading advisor and commodity pool operator. This registration is administered through the National Futures Association (“NFA”). Certain of ReSolve Global’s employees are registered with the NFA as Principals and/or Associated Persons of ReSolve Global if necessary or appropriate to perform their responsibilities. ReSolve Global has claimed an exemption under CFTC Rule 4.7 which exempts Resolve Global from certain part 4 requirements with respect to offerings to qualified eligible persons in the U.S. ReSolve Global is also a registered person with the Cayman Islands Monetary Authority.