Enhancing Portfolio Returns With Futures Carry Strategies

An Executive Summary for Investors and Investment Professionals

For a deeper dive into the mechanics and implications of Futures Yield (Carry) across different asset classes, refer to the foundational paper
Managed Futures Carry: A Practitioner’s Guide

Futures carry strategies can enhance portfolio returns while managing risk

While investors generally prefer to sell investments for more than they purchased them for, price appreciation is not the only source of returns. Landlords, for example, can earn rental income, seeking to generate income in excess of costs associated with owning property.

This second source of return is called the expected yield or “carry,” and can be loosely defined as the expected return of an investment assuming no change in its price. More thoroughly, carry is the economic benefit that one might expect to accrue by holding – or “carrying” – a particular investment, minus the costs associated with holding it. Empirically, carry is a key driver of returns across various asset classes, including equities, bonds, commodities, and currencies (Koijen et al. 2018; Moskowitz, Ooi, and Pedersen 2012; Baltas 2017).

Managed futures carry strategies capitalize on the tendency for higher-yielding futures markets to outperform lower-yielding markets, offering the potential to enhance portfolio returns while managing risk. Over the past three decades, hypothetical optimized futures carry strategies have demonstrated attractive risk-adjusted returns with low average correlations to core holdings of stocks and bonds. This paper provides an intuitive understanding of carry signals, their origins, and hypothetical simulations of different carry strategy implementations.

Figure 1: Source: ReSolve Asset Management. For illustrative purposes only.

Futures contracts enable investors to buy or sell a ‘spot’ asset on a future date at a predetermined price. The futures term structure describes the relationship between prices of contracts expiring at different dates. In ‘contango’ as in Figure 1, distant futures prices are higher than near-term futures, implying negative expected yield as prices converge to spot over time. Conversely, ‘backwardation’ occurs when distant futures prices are lower, implying positive expected yield. For example, gold futures currently1 exhibit contango, with April 2025 contracts at $2,420/oz2 versus the $2,325/oz spot price3, reflecting storage costs for the non-perishable commodity. By comparing futures prices along the forward term structure, we can estimate carry across assets without the need for complex calculations specific to each market.

Below we explore the potential of managed futures carry strategies to enhance portfolio returns while managing risk, providing valuable insights for investors seeking to diversify their portfolios and optimize risk-adjusted returns.

Understanding the nuances of carry is essential for effective strategy design

Understanding the nuances of carry is essential for designing effective investment strategies. While carry represents the economic benefit of holding an asset minus the associated costs (Koijen et al. 2018), it manifests uniquely in each asset class. In equities, carry is the expected dividend yield minus the cost of financing. For bonds, it is the coupon and roll yield minus financing costs. Commodities have convenience yields offset by storage, transportation, and financing costs, while currency carry is the interest rate differential between the local and foreign currency (Asness, Moskowitz, and Pedersen 2013) (Frazzini and Pedersen 2014) (Ilmanen 2011).

    Carry Components by Asset Class

    Asset Class Benefit of Holding Cost of Holding
    Equities Dividend Yield Financing Rate
    Bonds Yield Plus Roll Down Financing Rate
    Commodities Convenience Yield Financing Rate, Storage, Transportation, Insurance
    Currencies Foreign Short Rate Local Short Rate

    In theory, these measures provide guidance on future returns due to their risk-based explanations. For example, in equities, dividend yield may be compensation for underlying fundamental risks. In bonds, carry captures compensation for illiquidity risk, monetary policy risk, and inflation risk. In commodities, positive carry may be the premium earned for taking a position opposite to commodity hedgers (Baltas 2017). Finally, in currencies, interest rate spreads between countries can provide compensation for bearing inflation, funding liquidity, and consumption growth risks (Baltas 2017).

    Figure 2: Inception Dates for Markets in our Experimental Universe

    As seen in Figure 2, our analysis spanned a broad market dataset. We start with an examination of how carry strategies have historically played out within each sector in Figure 3 and Figure 4. Sectors are divided into currencies, energy commodities, metals, global government bonds, and equity indices. Carry is computed as appropriate for each sector, and each market is then weighted in proportion to its carry score and the inverse of its volatility. Portfolio weights are then scaled at the sector portfolio level to target 10 percent annualized standard deviation by taking current smoothed weights and a long-term covariance estimate.

    The Sector Ensemble combines all the sector strategies together, and rescales the portfolio weights to target 10 percent annualized standard deviation. While the Sector Ensemble has delivered strong risk-adjusted returns over the past three decades, as seen in Figure 4, it has also experienced periods of underperformance, particularly around the 2020 COVID-19 pandemic.

    Figure 3: Performance of Sector Carry Strategies.

    Data from CSI Data. Authors’ calculations. Performance is HYPOTHETICAL and GROSS of all trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Figure 4: Cumulative Excess Returns of Sector Carry Strategies.

    Data from CSI Data. Authors’ calculations. Performance is HYPOTHETICAL and assumes $100 invested with reinvestment of profit GROSS of trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    In practice, global carry strategies typically allow markets to compete against all other markets in the investment universe rather than isolating competition within sectors. Generally, portfolios that enable all markets to directly compete based on their carry and volatility metrics outperform those constructed from sector-specific strategies, where markets only vie for allocations within their respective sectors. This makes sense: while markets might earn a high score relative to other members of its sector, if the carry score of the sector is weak, the markets in the sector should earn lower weight in the portfolio.

    We examined portfolios formed from all 24 markets weighted in proportion to each market’s individual carry and volatility (inverse-volatility-weighted portfolios), and also explored the performance of portfolios formed using mean-variance optimization. Both theoretically and empirically, each method has its own strengths and weaknesses. Inverse-volatility-weighted portfolios are simple to implement and have a long history of success, but they can be sensitive to imbalanced investment universes and large deviations in correlations. Mean-variance optimization can be more robust to investment universe, but it requires a more complex implementation and can be sensitive to extreme correlations.

    Our research found that ensemble approaches combining both methods delivered the strongest results. For example, Figure 5 shows that an ensemble of inverse volatility- and optimization-weighted carry strategies produced a gross Sharpe ratio of 1.09 with a 27.5% maximum drawdown. The strong risk-adjusted performance is evident in Figure 6, which depicts the growth of the inverse volatility ensemble carry strategies over time.

    Figure 5: Performance of Time-Series (TS) Optimized Ensemble Carry Strategies.

    Data from CSI Data. Authors’ calculations. Performance is HYPOTHETICAL and GROSS of all trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Figure 6: Cumulative Excess Returns of Time-Series (TS) Optimized Ensemble Carry Strategies.

    Data from CSI Data. Authors’ calculations. Performance is HYPOTHETICAL and assumes $100 invested with reinvestment of profit GROSS of trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    While these strategies generated attractive returns, it’s important to note that their correlations to stocks and bonds oscillated between positive and negative regimes over multi-year periods, as shown in Figure 7. This suggests thoughtfully constructed carry strategies have the potential to provide meaningful diversification benefits across changing market environments.

    Figure 7: Rolling Pearson Correlations: Time-Series (TS) Optimized Ensemble Carry Strategies vs S&P 500 and U.S. 10-Year Treasury Futures.

    Data from CSI Data. Authors’ calculations. Performance is HYPOTHETICAL and GROSS of all trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Overall, our analysis indicates that combining carry signals in an ensemble approach may allow investors to harness this important driver of asset returns in a robust fashion (Moskowitz, Ooi, and Pedersen 2012) (Baltas 2017).

    Carry strategies demonstrated compelling diversification benefits, especially in crises

    Carry strategies showed compelling potential diversification benefits during the most challenging market environments, based on our historical analysis. To assess carry’s potential as a portfolio diversifier, we analyzed its hypothetical performance during the worst quarterly returns for the S&P 500 and Bloomberg Aggregate Bond indices. Figure 8 and Figure 9 show that carry strategies delivered consistent positive average hypothetical returns across all benchmark quintiles, largely independent of the severity of stock or bond losses (Koijen et al. 2018; Moskowitz, Ooi, and Pedersen 2012; Baltas 2017). However, this crisis performance and diversification potential based on historical results may not persist in the future.

    Figure 8: Annualized Excess Returns of Carry (Net), Trend Replication (Net), and S&P 500 Futures Conditioned on S&P 500 Total Return Quintile.

    Data from CSI Data, Standard & Poors. Authors’ calculations. S&P 500 futures are GROSS of roll costs. Carry and Trend performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Figure 9: Annualized Excess Returns of Carry (Net), Trend Replication (Net), and U.S. 10Y Treasury Futures Conditioned on Bloomberg Agg Total Return Quintile.

    Data from CSI Data, Bloomberg. Authors’ calculations. U.S. 10Y Treasury futures are GROSS of roll costs. Carry and Trend performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Notably, carry proved particularly resilient during major stock market crises in our historical analysis, providing valuable “crisis alpha”. As seen in Figure 10 and Figure 11, carry thrived during the Global Financial Crisis and the early 2000s tech crash, while mostly shrugging off the COVID-driven selloff (Figure 12).

    Bonds presented a more nuanced picture, with carry returns mirroring Treasury losses during the 2022 inflationary shock (Figure 13) but showing mixed results in other major drawdowns (Figure 14 and Figure 15) (Baz et al. 2015).

    For a deeper dive into the mechanics and implications of Futures Yield (Carry) across different asset classes, refer to the foundational paper
    Managed Futures Carry: A Practitioner’s Guide

    Figure 10: Cumulative Excess Growth of S&P 500 Futures and Carry Strategy (Net) from October 10, 2007 to August 16, 2012.

    Data from CSI Data and Standard & Poors. Authors’ calculations. Carry and Trend performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Figure 11: Cumulative Excess Growth of S&P 500 Futures and Carry Strategy (Net) from March 27, 2000 to October 26, 2006.

    Data from CSI Data and Standard & Poors. Authors’ calculations. Carry and Trend performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Figure 12: Cumulative Excess Growth of S&P 500 Futures and Carry Strategy (Net) from February 20, 2020 to August 10, 2020.

    Data from CSI Data and Standard & Poors. Authors’ calculations. Carry and Trend performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Figure 13: Cumulative Excess Growth of U.S. 10Y Treasury Futures and Carry strategy (Net) from August 05, 2020 to December 29, 2023.

    Data from CSI Data and Bloomberg. Authors’ calculations. Performance is HYPOTHETICAL and GROSS of all trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Figure 14: Cumulative Excess Growth of U.S. 10Y Treasury Futures and Carry strategy (Net) from December 31, 2008 to June 28, 2010.

    Data from CSI Data and Bloomberg. Authors’ calculations. Performance is HYPOTHETICAL and GROSS of all trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Figure 15: Cumulative Excess Growth of U.S. 10Y Treasury Futures and Carry strategy (Net) from October 18, 1993 to May 04, 1995.

    Data from CSI Data and Bloomberg. Authors’ calculations. Performance is HYPOTHETICAL and GROSS of all trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Thoughtful implementation is key to realizing the potential of carry strategies

    While carry strategies have clearly delivered value gross of transaction costs over an extended historical period, what matters is net performance. Aside from their potential ability to deliver more reliable results, the internal diversification of ensemble strategies also plays a roll in minimizing turnover. As Figure 16 and Figure 17 illustrate, efficiently managing transaction costs through trade netting and weight smoothing is critical to realizing the potential of these strategies (Koijen et al. 2018; Baltas 2017; Moskowitz, Ooi, and Pedersen 2012).

    Figure 16: Cumulative Growth of S&P 500 Total Return Index Stacked with Carry and Trend.

    Data from CSI Data. Authors’ calculations. GROSS performance is HYPOTHETICAL and GROSS of all trading slippage and commissions. NET performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Figure 17: Cumulative Excess Returns of Time-Series (TS) Carry Strategies: Gross vs Net.

    Data from CSI Data. Authors’ calculations. GROSS performance is HYPOTHETICAL and GROSS of all trading slippage and commissions. NET performance is HYPOTHETICAL and assumes $100 invested with reinvestment of profit NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Carry is best utilized as part of a diversified investment approach, not a standalone strategy. To further highlight carry’s diversification potential, we examined hypothetical portfolios that stacked carry on top of stock and bond holdings. In simulation, these portfolios achieved substantially higher returns with volatility and drawdown profiles similar to the underlying assets, underscoring the hypothetically complementary nature of carry in portfolio construction. As demonstrated in Figure 18, Figure 19, and Figure 20, thoughtfully stacking carry and trend strategies on top of traditional stock and bond allocations has the potential to enhance risk-adjusted performance over extended horizons.

    Figure 18: Cumulative Growth of S&P 500 Total Return Index Stacked with Carry and Trend.

    Data from CSI Data and Standard & Poors. Authors’ calculations. Trend and Carry performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Figure 19: Cumulative Growth of Bloomberg Aggregate Total Return Index Stacked with Carry and Trend.

    Data from CSI Data, Standard & Poors and Bloomberg. Authors’ calculations. Performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Figure 20: Performance of S&P 500 Total Return Index and Bloomberg Aggregate Total Return Index Stacked with Carry and Trend.

    Data from CSI Data, Standard & Poors and Bloomberg. Authors’ calculations. Performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.

    Carefully consider carry strategies for your diversified portfolio

    In conclusion, our extensive analysis demonstrates that carry has been a key driver of returns across equities, bonds, commodities, and currencies, and that futures markets provide an efficient way to access this important source of returns. By employing sophisticated portfolio construction techniques and efficiently managing transaction costs, thoughtfully designed carry strategies have the potential to enhance portfolio returns while also providing valuable diversification benefits, especially during adverse market environments.

    However, it is essential to approach carry with a prudent investment perspective. Carry strategies should be utilized as a complement within a diversified portfolio, not as a standalone investment. Additionally, investors must weigh the potential benefits against the inherent risks and uncertainties present in all financial markets. While the historical results presented in this study are certainly encouraging, they do not guarantee future performance.

    We believe investors should consider carefully incorporating carry strategies within their diversified portfolios, as they have the potential to play a valuable role in achieving long-term investment objectives and enhancing risk-adjusted returns. As always, we recommend investors consult with their financial advisors to determine the most appropriate approach for their unique circumstances.

    Disclaimers

    Hypothetical Performance Disclosure

    The performance figures presented in this document are hypothetical and do not represent actual trading results. They are based on backtested data and certain assumptions, including but not limited to, the availability of historical data, the accuracy of such data, and the absence of material changes in market conditions. Actual results may vary significantly from the hypothetical results presented. Past performance is not indicative of future results.

    Risk Disclosures

    Futures trading involves substantial risk of loss and is not suitable for all investors. Investors should carefully consider their investment objectives, risk tolerance, and financial circumstances before trading futures contracts. Specific risks associated with futures trading include, but are not limited to, market risk, liquidity risk, leverage risk, and operational risk.

    General Disclaimers

    This document is for informational and educational purposes only and does not constitute an offer to sell or a solicitation of an offer to buy any security or investment product. The information presented herein should not be construed as investment advice and is not tailored to the specific investment objectives, financial situation, or particular needs of any individual investor. Investors are strongly encouraged to seek professional advice from a qualified financial advisor before making any investment decisions.

    No Guarantee of Future Performance

    This document does not guarantee future performance or success of any investment strategy. The information presented herein is based on historical data and analysis, which may not be indicative of future market conditions or results.

    Methodology

    The methodology used to derive the hypothetical performance figures and analysis presented in this document is available upon request.

    About ReSolve Asset Management

    ReSolve Asset Management Inc. is registered in Canada as an Investment Fund Manager in Ontario, Quebec and Newfoundland and Labrador, and as a Portfolio Manager and Exempt Market Dealer in Ontario, Alberta, British Columbia and Newfoundland and Labrador and a Commodity Trading Manager in Ontario and Derivatives Portfolio Manager in Quebec and is registered in the United States with the Securities and Exchange Commission and the National Futures Association. ReSolve Asset Management SEZC (Cayman) is registered with the Cayman Islands Monetary Authority and with the National Futures Association in the United States.

    References

    Asness, Clifford S, Tobias J Moskowitz, and Lasse Heje Pedersen. 2013. “Value and Momentum Everywhere.” The Journal of Finance 68 (3): 929–85.
    Baltas, Nick. 2017. “Optimising Cross-Asset Carry.” Available at SSRN 2968677.
    Baz, Jamil, Nicolas Granger, Campbell R Harvey, Nicolas Le Roux, and Sandy Rattray. 2015. “Dissecting Investment Strategies in the Cross Section and Time Series.” Available at SSRN 2695101.
    Frazzini, Andrea, and Lasse Heje Pedersen. 2014. “Betting Against Beta.” Journal of Financial Economics 111 (1): 1–25.
    Ilmanen, Antti. 2011. “Expected Returns: An Investor’s Guide to Harvesting Market Rewards.”
    Koijen, Ralph SJ, Tobias J Moskowitz, Lasse Heje Pedersen, and Evert B Vrugt. 2018. “Carry.” Journal of Financial Economics 127 (2): 197–225.
    Moskowitz, Tobias J, Yao Hua Ooi, and Lasse Heje Pedersen. 2012. “Time Series Momentum.” Journal of Financial Economics 104 (2): 228–50.
    1. As of April 23, 2024↩︎
    2. Source: CME↩︎
    3. Source: Kitco↩︎