Commodity Futures

Do spot price trends drive commodity futures momentum strategies? In their August 2016 paper entitled “Momentum and Mean-Reversion in Commodity Spot and Futures Markets”, Denis Chaves and Vivek Viswanathan investigate the reasons for the success of cross-sectional (relative) momentum strategies and failure of cross-sectional mean reversion strategies in the commodity futures markets. They specify commodity valuation as the ratio of current price to average price ratio over the past 120 months (P/A). They specify commodity price trend as cumulative return over measurement intervals ranging from the last month to the last 66 months. Using two independent sets of 25 (with liquid futures) and 21 (without liquid futures) commodity spot price series as available since 1946 and one set of 27 commodity futures price series as available since 1965, all through 2014, they find that: Keep Reading

Does technical analysis work for crude oil futures trading? In their August 2016 paper entitled “Performance of Technical Trading Rules: Evidence from the Crude Oil Market”, Ioannis Psaradellis, Jason Laws, Athanasios Pantelous and Georgios Sermpinis investigate the profitability of a wide range technical trading rules applied to West Texas Intermediate (WTI) light sweet crude oil futures and the United States Oil (USO) fund, which holds front-month futures contracts. They consider 7,846 trading rules grouped into five families (filter rules, moving averages, support and resistance rules, channel breakouts and on-balance volume averages) used on daily prices. They assume these rules earn the risk-free rate when neutral. They measure performance during four subperiods (April 2007-May 2009; June 2009-March 2011; April 2011-July 2013; August 2013-December 2015), reflecting bullish or bearish and contango or backwardation subperiods. They focus on average return, Sharpe ratio and Calmar ratio as key performance statistics. They begin with in-sample tests and progressively account for trading frictions (one-way 0.033% for futures and 0.05% for USO), data snooping bias (via two methods) and out-of-sample rule identification. Using daily prices for the specified assets during April 2006 through December 2015, they find that: Keep Reading

Does time series (intrinsic or absolute) momentum work across asset classes prior to the Great Moderation (secular decline in interest rates)? In their August 2016 paper entitled “Trend Following: Equity and Bond Crisis Alpha”, Carl Hamill, Sandy Rattray and Otto Van Hemert test several time series momentum portfolios as applied to groups of bonds, commodities, currencies and equity indexes as far back as 1960. They consider 10 developed country equity indexes, 11 developed country government bond series, 25 agricultural/energy/metal futures series and nine U.S. dollar currency exchange rate series. They calculate return momentum for each asset as the weighted sum of its past monthly returns (up to 11 months) divided by the normalized standard deviation of those monthly returns. They then divide each signal again by volatility and apply a gearing factor to specify a 10% annual volatility target for each holding. Within each of equity index, bond and currency groups, they weight components equally. Within commodities, they weight agriculture, energy and metal sectors equally after weighting individual commodities equally within each sector. They report strategy performance based on excess return, roughly equal to real (inflation-adjusted) return. They commence strategy performance analyses in 1960 to include an extreme bond bear market. Using monthly price series that dovetail futures/forwards from inception with preceding spot (cash) data as available starting as early as January 1950 and as late as April 1990, all through 2015, they find that: Keep Reading

How should investors approach commodity futures trading? In her August 2016 paper entitled “An Introduction to U.S. Commodity Futures Markets: a Historical Perspective Along with Commodity Trading Principles”, Hilary Till summarizes success factors for designing and managing a commodity futures portfolio, including: identifying potential trades; constructing trades; constructing a portfolio of trades; risk management; payoff expectations; level of leverage; and, interaction of the commodity futures portfolio with holdings of other asset classes. Based on her experience and empirical examples, she concludes that: Keep Reading

How should investors think about investing in crude oil? In their June 2016 paper entitled “Understanding Oil Investing”, Ludwig Chincarini, John Love and Robert Nguyen examine oil investing, with emphasis on differences in behaviors between non-investable spot oil and investable crude oil futures. They consider several approaches to futures, all fully collateralized by cash (one-month U.S. Treasury bills):

1. Simple systematic rolling – Select a contract series (nearest, 2nd, 3rd… from expiration) and systematically roll from one contract in the series to the next at a specified time before expiration (0, 1, 3, 5, 10, 13…  days).
2. Binary signaled rolling (Strategy 1) – Acquire/roll to the next contract in a series only when the series is in backwardation (has positive roll yield) and otherwise hold cash.
3. Highest roll yield (Strategy 2) – Acquire/roll to the one of the nearest, 2nd or 3rd contract with the highest backwardation (or lowest contango) on a specified roll date.

They also examine the behaviors of crude oil exchange-traded funds (ETF). Using daily spot West Texas Intermediate (WTI) crude oil price and WTI crude oil futures prices, volumes and open interest during 1983 through 2015 (focusing on 1994-2015 and 2005-2015), and crude oil ETF prices from inceptions through early 2016, they find that: Keep Reading

Should investors believe that the financial industry can offer low-cost, liquid funds that reliably mimic Commodity Trading Advisor (CTA) hedge funds? In their June 2016 paper entitled “Just a One Trick Pony? An Analysis of CTA Risk and Return”, Jason Foran, Mark Hutchinson, David McCarthy and John O’Brien identify and examine performances of CTA-like hedge funds across eight distinct categories defined via iterative correlation clustering. Their goal is to determine whether category performance is amenable to modeling (cloning) via liquid exposures to four futures risk factor premiums:

1. Value – long (short) high-value (low-value) futures, with “value” based on book-to-market ratios for stock index futures and 5-year change in yields/spot prices/purchasing power for government bonds/commodities/currency forwards.
2. Carry – long (short) futures with high (low) roll returns.
3. Time series momentum – long (short) futures with positive (negative) 12-month past returns.
4. Options-based trend following – from Fung and Hsieh, correlated with trends shorter than time series momentum.

They estimate these premiums from monthly returns of rolling nearest contracts for: 12 global equity index futures series; eight global 10-year government bond synthetic futures series; 22 commodity futures series; and, nine global currency forward series versus the U.S. dollar. They employ a hedge fund screening process that suppresses backfill bias (lucky starts). Using monthly net returns and assets under management (AUM) for specific (not fund-of-funds) and distinct CTA funds with at least 12 months of returns denominated in U.S. dollars and monthly data required to estimate futures risk factor premiums as available during January 1987 through July 2015, they find that: Keep Reading

Is there an objective way to benchmark the performance of trend-following Managed Futures hedge funds? In their March 2016 paper entitled “Adaptive Time Series Momentum – Benchmark for Trend-Following Funds”, Peter Erdos and Gert Elaut test a futures timing system that increases (decreases) allocations when trends are emerging (fading) per 251 equally weighted, volatility-scaled, daily rebalanced time series momentum (TSMOM) strategies. Strategy lookback intervals range from 10 to 260 trading days. Volatility scaling involves dividing momentum returns by an exponentially weighted daily moving average estimator of volatility over a 60-day rolling window. They account for trading frictions (bid-ask spread plus broker/market fees by asset class, estimated separately for old and new subperiods), exchange rates, one-day signal-to-trade execution delay and estimated management/performance fees. They apply the TSMOM system as a mechanical benchmark for trend-following Managed Futures hedge funds. They examine also a momentum “speed factor” that buys longer-term and sells shorter-term TSMOM strategies. Using daily prices for 98 futures contract series and monthly net-of-fee returns for 379 live and dead trend-following Managed Futures hedge funds during January 1994 through September 2015, they find that: Keep Reading

Do commodity price changes predict currency exchange rate fluctuations for commodity-exporting countries? In their March 2016 paper entitled “When the Walk is Not Random: Commodity Prices and Exchange Rates”, Emanuel Kohlscheen, Fernando Avalos  and Andreas Schrimpf analyze relationships between commodity prices and exporter exchange rates. They first construct daily commodity export price indexes tailored to 11 commodity-exporting countries (Australia, Brazil, Canada, Chile, Colombia, Malaysia, Mexico, Norway, Peru, Russia, South Africa), encompassing 83 commodities (26 metal, 36 agricultural, 11 livestock, 10 energy). They then relate index levels to daily currency exchange rates by country. Using daily UN Comtrade statistics, commodity prices and currency exchange rates in U.S. dollars and Japanese yen as available during January 2004 (Malaysia starts in August 2005, and Russia starts in February 2009) through February 2015, they find that: Keep Reading

What kinds of commodity futures portfolio allocation strategies work? In her December 2015 paper entitled “Long-Short Commodity Investing: A Review of the Literature”, Joelle Miffre summarizes recent academic studies that analyze the performance of long-short commodity futures strategies. She focuses on strategies exploiting roll yields, inventory levels, hedging pressure or momentum. She also surveys alternative strategies based on risk, value, liquidity, sensitivity to inflation or skewness, plus some combination strategies. She relies mostly on Sharpe ratio to compare strategies. Based on results from about 50 studies, she concludes that: Keep Reading

How do the behaviors of time-series (absolute) and cross-sectional (relative) carry, momentum and value strategies differ? In the November 2015 version of their paper entitled “Dissecting Investment Strategies in the Cross Section and Time Series”, Jamil Baz, Nicolas Granger, Campbell Harvey, Nicolas Le Roux and Sandy Rattray explore time-series and cross-sectional carry, momentum and value strategies as applied to multiple asset classes. They adapt to each asset class the following general definitions:

• Carry – buy (sell) futures on assets for which the forward price is lower (higher) than the spot price.
• Momentum – buy (sell) assets that have outperformed (underperformed) over the past 6-12 months.
• Value – buy (sell) assets for which market price is lower (higher) than estimated fundamental price.

For cross-sectional portfolios, they rank assets within each class-strategy and form portfolios that are long (short) the equally weighted six assets with the highest (lowest) expected returns, rebalanced daily except for currency carry and value trades. For time-series portfolios, they take an equal long (short) position in each asset within a class-strategy according to whether its expected return is positive (negative). When combining strategies within an asset class, they use equal weighting. When combining across asset classes, they scale each class-strategy portfolio to a 15% annualized volatility target. Using daily contract closing bid-ask midpoints for 26 equity futures, 14 interest rate swaps, 31 currency exchange rates and 16 commodity futures during January 1990 through April 2015, they find that: Keep Reading