Commodity Futures

Do alternative investments (real estate, venture capital, private equity, hedge funds, private credit, infrastructure and energy investments) offer attractive returns and diversification, buffering investors from the vagaries of the stock market? In his March 2025 paper entitled “The Demise of Alternative Investments”, Richard Ennis examines contributions of alternative investments (alts) to pension fund and endowment portfolio net performances. He first looks at costs of investing in alts. He then relates public pension fund and university endowment portfolio alt allocations to their excess returns (relative to risk-matched simple portfolios of U.S. and non-U.S. stocks and investment-grade U.S. bonds). Using results of prior research, a sample of 50 large U.S. public pension funds for the 16 fiscal years ended June 2024 and 16 years of composite performance data for large university endowments from the National Association of College and University Business Officers, he finds that:

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Are plans to use nuclear power to provide electricity for proliferating data centers driving attractive performance for uranium exchange-traded-funds (ETF)? To investigate, we consider four such ETFs, all currently available:

• VanEck Uranium and Nuclear ETF ETF (NLR) – picks stocks and depositary receipts of firms involved in uranium and nuclear energy.
• Global X Uranium ETF (URA) – picks stocks of global companies involved in the uranium industry.
• Sprott Uranium Miners ETF (URNM) – picks stocks of firms devoting at least 50% of assets to mining of uranium, holding physical uranium, owning uranium royalties or engaging in other activities that support uranium mining.
• Sprott Junior Uranium Miners ETF (URNJ) – picks stocks of small firms devoting at least 50% of assets to mining of uranium, holding physical uranium, owning uranium royalties or engaging in other activities that support uranium mining.

We use Energy Select Sector SPDR Fund (XLE) as a benchmark. We also look at some performance results for SPDR S&P 500 ETF Trust (SPY) for perspective. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly total returns for the four uranium ETFs as available and for XLE and SPY through February 2025, we find that: Keep Reading

How do returns of different asset classes recently interact with inflation as measured by monthly change in the not seasonally adjusted, all-items consumer price index (CPI) from the U.S. Bureau of Labor Statistics? To investigate, we look at lead-lag relationships between change in CPI and returns for each of the following 10 exchange-traded fund (ETF) asset class proxies:

• Equities:
  • SPDR S&P 500 (SPY)
  • iShares Russell 2000 Index (IWM)
  • iShares MSCI EAFE Index (EFA)
  • iShares MSCI Emerging Markets Index (EEM)
• Bonds:
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • iShares iBoxx $ Investment Grade Corporate Bond (LQD)
  • iShares JPMorgan Emerging Markets Bond Fund (EMB)
• Real assets:
  • Vanguard REIT ETF (VNQ)
  • SPDR Gold Shares (GLD)
  • Invesco DB Commodity Index Tracking (DBC)

Using monthly total CPI values and monthly dividend-adjusted prices for the 10 specified ETFs during December 2007 (limited by EMB) through January 2025, we find that: Keep Reading

Does degree of co-movement among commodity exchange-traded funds (ETF) predict whether momentum or reversal is imminent? In their September 2024 paper entitled “How to Improve Commodity Momentum Using Intra-Market Correlation”, Radovan Vojtko and Margaréta Pauchlyová investigate whether the relationship between short-term and long-term average pairwise return correlations indicates when to pursue momentum and when to pursue reversal among commodity ETFs. Based on prior research, they consider four ETFs: DBA (agriculture), DBB (base metals), DBE (energy) and DBP (precious metals). Their strategies consists of each month:

1. Ranking the four ETFs by 12-month past return.
2. Calculating average pairwise 20-day and 250-day daily return correlations for the four ETFs.
3. If the average short-term correlation is higher (lower) than the average long-term correlation, executing an equal-weighted momentum (reversal) strategy by buying (selling) the two top-ranked ETFs and selling (buying) the two bottom-ranked ETFs.

Using daily adjusted closes for the selected ETFs from the end of 2007 through early 2024, they find that: Keep Reading

How do returns of different asset classes recently interact with the Effective Federal Funds Rate (EFFR)? We focus on monthly changes (simple differences) in EFFR  and look at lead-lag relationships between change in EFFR and returns for each of the following 10 exchange-traded fund (ETF) asset class proxies:

• Equities:
  • SPDR S&P 500 (SPY)
  • iShares Russell 2000 Index (IWM)
  • iShares MSCI EAFE Index (EFA)
  • iShares MSCI Emerging Markets Index (EEM)
• Bonds:
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • iShares iBoxx $ Investment Grade Corporate Bond (LQD)
  • iShares JPMorgan Emerging Markets Bond Fund (EMB)
• Real assets:
  • Vanguard REIT ETF (VNQ)
  • SPDR Gold Shares (GLD)
  • Invesco DB Commodity Index Tracking (DBC)

Using end-of-month EFFR and dividend-adjusted prices for the 10 ETFs during December 2007 (limited by EMB) through August 2024, we find that: Keep Reading

Are managed futures, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider six managed futures ETFs, five live and one dead:

1. WisdomTree Managed Futures Strategy (WTMF) – seeks positive total returns in rising or falling markets that are uncorrelated with broad market equity and fixed income returns via diversified combination of commodities, currencies and interest rates futures.
2. First Trust Morningstar Managed Futures Strategy (FMF) – seeks positive returns that are uncorrelated to broad market equity and fixed income returns via a portfolio of exchange-listed futures.
3. ProShares Managed Futures Strategy (FUT) – seeks to profit in rising and falling markets by long and short positions in futures across asset classes such as commodities, currencies and fixed income such that each contributes equally to portfolio risk. (Dead as of May 2022.)
4. iM DBi Managed Futures Strategy (DBMF) – seeks long-term capital appreciation via long and short positions in futures across equities, fixed income, currencies and commodities. Fund positions approximate the current asset allocation of a pool of the largest commodity trading advisor hedge funds.
5. KraneShares Mount Lucas Managed Futures Index Strategy ETF (KMLM) – seeks to track an index comprised of 22 liquid futures contracts traded on U.S. and foreign exchanges. The index includes groups of 11 commodities, six currencies, and five global bonds, with groups weighted by relative historical volatility and individual contracts weighted equally within each group.
6. Simplify Managed Futures Strategy (CTA) – seeks long term capital appreciation by systematically investing in futures in an attempt to create an absolute return profile, that also has a low correlation to equities, and can provide support in risk-off events.

We focus on average return, standard deviation of returns, reward/risk (average return divided by standard deviation), compound annual growth rate (CAGR), maximum drawdown (MaxDD) and correlations of returns with those of SPDR S&P 500 (SPY) and iShares Barclays 20+ Year Treasury Bond (TLT), all based on monthly data, as key performance statistics. We use Eurekahedge CTA/Managed Futures Hedge Fund Index (Eurekahedge) as a benchmark. Using monthly returns for the six managed futures funds as available through August 2024, and contemporaneous monthly returns for the benchmark, SPY and TLT, we find that:

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How does the performance of the global multi-class market look when evaluated at a monthly frequency? In their August 2024 paper entitled “The Risk and Reward of Investing”, Ronald Doeswijk and Laurens Swinkels assess global investing rewards and risks via an exhaustive $150 trillion portfolio of investable global assets priced at a monthly frequency, enabling greater granularity of risk estimates than does the annual frequency used in prior research. They consider five asset classes: equities, real estate, non-government bonds, government bonds and commodities. For these classes and the multi-class market, they examine stability of Sharpe ratios and severity, frequency and duration of drawdowns. Their default base currency is the U.S. dollar, but they measure effects of choosing one of nine other currencies on global market portfolio performance. They calculate excess investment returns generally relative to government bill yields as a proxy for return on savings. Using monthly returns for all investable global assets with reinvested dividends during 1970 through 2022, they find that:

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Is copper price a reliable leading indicator of economic activity and therefore of future corporate earnings and equity prices? To investigate, we employ the monthly price index for copper base scrap from the U.S. Bureau of Labor Statistics, which spans multiple economic expansions and contractions. Using monthly levels of the copper scrap price index and the S&P 500 Index during January 1957 through May 2024, we find that: Keep Reading

Do published commodity futures factors exhibit exploitable momentum? In their December 2023 paper entitled “Factor Momentum in Commodity Futures Markets”, Yiyan Qian, Xiaoquan Liu and Ying Jiang examine factor momentum in fully collateralized nearest-rolled contracts of various commodity futures. They consider ten factors:

• Market –S&P Goldman Sachs Commodity Index. 
• Basis -slope of futures term structure.
• Momentum – cross-sectional predictability of past performance.
• Basis-momentum – slope and curvature of the term structure of futures returns.
• Hedging pressure – mismatch in hedging and speculating activity.
• Skewness – investor return distribution preferences and selective hedging.
• Open interest – existing price positions.
• Currency beta – changes in the U.S. dollar versus a basket of other currencies.
• Inflation beta – impact from unexpected inflation.
• Liquidity – liquidity risk of commodity futures trading.

They calculate return series for each factor by each month buying (selling) the equal-weighted fifth of commodity futures with the highest (lowest) predicted next-month returns. For each factor return series, they then test the ability of returns over the past 1, 3, 6, 9 or 12 months to predict next-month return. Using daily data for 36 commodity futures contracts from U.S. and UK markets (16 agriculture, 6 energy, 3 livestock and 10 metal) as available during January 1985 through May 2022, they find that: Keep Reading

Can network analysis discover useful momentum spillover across asset classes? In their August 2023 paper entitled “Network Momentum across Asset Classes”, Xingyue (Stacy) Pu, Stephen Roberts, Xiaowen Dong and Stefan Zohren employ a graph machine learning model to discover cross-class momentum connections and devise a network momentum strategy across 64 series of commodities, equities, bonds and currencies future contracts. They train the model on an expanding window of at least 10 years of history for eight momentum features, including volatility-scaled returns and normalized moving average crossover divergences (MACD) over different lookback intervals. They they then apply multiple linear regressions over different lookback intervals (seeking to avoid reversals) to devise a network momentum strategy for out-of-sample testing. Every five years, they retrain the graph model. Using daily prices of the 64 futures contract series during 1990 through 2022, such that out-of-sample testing commences in 2000, they find that:

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