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Commodity Futures

These entries address investing and trading in commodities and commodity futures as an alternative asset class to equities.

Exploitable Commodity Futures Factor Momentum?

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:

  • MarketS&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

Multi-class Network Momentum

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:

Keep Reading

Are Managed Futures ETFs Working?

Are managed futures, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider five managed futures ETFs, four 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. This fund is 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 the asset classes such as 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. 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 five managed futures funds as available through August 2023, and contemporaneous monthly returns for the benchmark index, SPY and TLT, we find that:

Keep Reading

Recent Interactions of Asset Classes with Inflation (CPI)

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 June 2023, we find that: Keep Reading

Recent Interactions of Asset Classes with Effective Federal Funds Rate

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 monthly EFFR and monthly dividend-adjusted prices for the 10 ETFs during December 2007 (limited by EMB) through December 2022, we find that: Keep Reading

Best Safe Haven ETF?

A subscriber asked which exchange-traded fund (ETF) asset class proxies make the best safe havens for the U.S. stock market as proxied by the S&P 500 Index. To investigate, we test 15 ETFs/funds as potential safe havens:

Utilities Select Sector SPDR Fund (XLU)
iShares 20+ Year Treasury Bond (TLT)
iShares 7-10 Year Treasury Bond (IEF)
iShares 1-3 Year Treasury Bond (SHY)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
iShares Core US Aggregate Bond (AGG)
iShares TIPS Bond (TIP)
Vanguard Real Estate Index Fund (VNQ)
SPDR Gold Shares (GLD)
Invesco DB Commodity Index Tracking Fund (DBC)
United States Oil Fund, LP (USO)
iShares Silver Trust (SLV)
Invesco DB G10 Currency Harvest Fund (DBV)
SPDR Bloomberg Barclays 1-3 Month T-Bill (BIL)
Grayscale Bitcoin Trust (GBTC)

We consider three ways to find safe havens for the U.S. stock market based on daily or monthly returns:

  1. Contemporaneous return correlation with the S&P 500 Index during all market conditions at daily and monthly frequencies.
  2. Performance during S&P 500 Index bear markets as defined by the index being below its 10-month simple moving average (SMA10) at the end of the prior month.
  3. Performance during S&P 500 Index bear markets as defined by the index being -20%, -15% or -10% below its most recent peak at the end of the prior month.

Using daily and monthly dividend-adjusted closing prices for the above 15 funds since their respective inceptions, and contemporaneous daily and monthly levels of the S&P 500 Index since 10 months before the earliest inception, all through April 2022, we find that: Keep Reading

Economic Surprise Momentum

How should investors think about surprises in economic data? In their March 2022 paper entitled “Caught by Surprise: How Markets Respond to Macroeconomic News”, Guido Baltussen and Amar Soebhag devise and investigate a real-time aggregate measure of surprises in economic (not financial) variables around the world. Each measurement for each variable consists of release date/time, initial as-released value, associated consensus (median) forecast, number and standard deviation of individual forecasts and any revision to the previous as-released value across U.S., UK, the Eurozone and Japan markets from the Bloomberg Economic Calendar. They classify variables as either growth-related or inflation-related. They apply recursive principal component analysis to aggregate individual variable surprises separately into daily nowcasts of initial growth-related and inflation-related announcement surprises and associated revision surprises. They investigate the time series behaviors of these nowcasts and then examine their interactions with returns for four asset classes:

  1. Stocks via prices of front-month futures contracts rolled the day before expiration for S&P 500, FTSE 100, Nikkei 225 and Eurostoxx 50 indexes.
  2. Government bonds via prices of front-month futures contracts rolled the day before first notice on U.S., UK, Europe and Japan 10-year bonds.
  3. Credit via returns on 5-year credit default swaps for U.S. and Europe investment grade and high yield corporate bond indexes.
  4. Commodities via excess returns for the Bloomberg Commodity Index.

Specifically, they test an investment strategy that takes a position equal to the 1-day lagged value of the growth surprise nowcast or the inflation surprise nowcast on the last trading day of each month. They pool regions within an asset class by equally weighting regional markets. Using daily as-released data for 191 economic variables across global regions and the specified monthly asset class price inputs during March 1997 through December 2019, they find that: Keep Reading

Natural Gas Trades Around Major Winter Storms?

A subscriber asked whether it works to buy natural gas before big winter storms. To investigate, we look at the interactions between a sample of major winter storms in the U.S. northeast (NE) and contemporaneous prices of the futures-based United States Natural Gas Fund, LP (UNG). Using daily adjusted closes of UNG and dates of the selected major winter storms during April 2007 (UNG inception) through March 2022, we find that: Keep Reading

Overnight Effect Across Asset Classes?

Does the overnight return effect found pervasively among equity markets, as summarized in “Persistence of Overnight/Intraday Equity Market Return Patterns”, also hold for other asset classes? To investigate, we compare open-to-close (O-C) and close-to-open (C-O) average returns, standard deviations of returns and cumulative performances for the exchange-traded funds (ETF) used as asset class proxies in the Simple Asset Class ETF Momentum Strategy (SACEMS). Using daily dividend-adjusted opening and closing prices of these ETFs during mid-December 2007 (inception of the youngest ETF) through early March 2022, we find that: Keep Reading

Timing GLD Using Gold Futures Position Data

A subscriber asked whether traders should enter a position in gold, as proxied by SPDR Gold Shares (GLD), whenever Commercial gold futures traders are net long and Non-commercial gold future traders (Speculators) are net short. To investigate, we:

  • Obtain from the Commodity Futures Trading Commission weekly gold Commitments of Traders (CoT) legacy reports (futures only) as available. Terminology in the legacy reports matches that in the question posed.
  • For each week, calculate the net (long minus short) contract positions separately for Commercial traders and Speculators.
  • Identify the weeks when Commercial traders are net long and Speculators are net short. Because these two groups are largely trading counterparties, they are nearly always opposite in net positions (in other words, the specified setup is not much different from just requiring that Commercial traders be net long).
  • Examine future GLD returns for these weeks.

Using weekly CoT gold futures position data since January 1986 and matching weekly GLD prices since inception in late November 2004, both through late February 2022, we find that: Keep Reading

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