Commodity futures have become widespread vehicles among various investors and traders. Both past research and practice have shown that commodities can be used for strategic and tactical asset allocations. The strategic appeal of commodity indices comes from their equity-like return, their inflation-hedging properties, and their role for risk diversification. Keynes (1930) and Cootner (1960) put forward the idea that commodity futures prices depend on the net positions of hedgers. This can be viewed as a form of insurance — producers, and consumers of the underlying commodity transfer the risk of price fluctuations to speculators. Naturally, speculators are willing to undertake this risk in the hope of a large positive return.
The term structure of commodity futures can have two forms. Firstly, the increase in the futures price as maturity approaches is referred to as normal backwardation and secondly, the decrease in the futures price as maturity approaches is traditionally referred to as contango. Both normal backwardation and contango arise as a result of the inequality between the long and short positions of hedgers, which require the intervention of speculators to restore equilibrium. Therefore commodity futures returns are directly related to the propensity of hedgers to be net long or net short. Aforementioned leads to a simple design of an active strategy that buys mostly backwardated contracts and shorts mostly contangoed contracts – the strategy which exploits the term structure in commodities.
Last but not least, similar results can be found in the paper of Shwayder and James: “Returns to the Commodity Carry Trade”. In their speculation strategy, an investor buys commodity futures if the underlying commodity market is in backwardation and sells commodity futures if the underlying commodity market is in contango. Moreover, they have demonstrated that this strategy, if applied to a portfolio of 28 commodities, is characterized by high returns and high Sharpe ratios, which are uncorrelated with conventional risk factors.
The main idea is based on the theory of Keynes (1930) and Cootner (1960) – the commodity futures prices depend on the net positions of hedgers. Producers or consumers of the underlying commodity transfer the risk of price fluctuations to speculators, who are willing to undertake this risk in the hope of a large positive return. If the supply by short hedgers exceeds the demand by long hedgers (namely, hedgers are net short), the futures price today has to be a downward-biased estimate of the futures price at maturity.
Moreover, from the practical point of view, term-structure strategies are connected with various interesting properties for potential investors. To be more precise, term-structure strategies come with lower maximum drawdowns, higher maximum run-ups, and both lower minimum and higher maximum 12-month rolling returns than the benchmark. Additionally, the reward-to-risk and Sortino ratios of all seven profitable active term-structure strategies in the paper, exceed those of the passive strategy or the benchmark. Hence, the high average returns of the term structure strategies appear to more than compensate investors for the increase in volatility and downside risk that they bear relative to the passive benchmark. Interestingly, the returns of the long-short portfolios follow the ups and downs of the S&P GSCI but are unrelated to the S&P500, which offers a way for the diversification of equity portfolios.
Erb and Harvey in “The Tactical and Strategic Value of Commodity Futures” state that historically, the average annualized excess return of individual commodity futures has been approximately zero, and commodity futures returns have been mostly uncorrelated with one another. However, the prospective annualized excess return of a rebalanced portfolio of commodity futures can be equity-like. Specific security characteristics, such as the term structure of futures prices, and some portfolio strategies have historically been rewarded with above-average returns. An interesting view on this topic can also be found in the work of Durr and Voegeli: Structural Properties of Commodity Futures Term Structures and Their Implications for Basic Trading Strategies. Time series of commodity prices and returns were analyzed by means of static and rolling principal component analysis. The authors have found high stability of the principal components and their explanatory power over time. The first component identified as a level factor is paramount for the interpretation of term structure dynamics for most underlying. This result suggests that an investor can exploit the information contained within the term structure and revealed by principal component analysis.
Confidence in anomaly's validity
Backtest period from source paper
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Period of Rebalancing
Notes to Indicative Performance
per annum, for 1 month rebalancing period
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Simple trading strategy
This simple strategy buys each month the 20% of commodities with the highest roll-returns and shorts the 20% of commodities with the lowest roll-returns and holds the long-short positions for one month. The contracts in each quintile are equally-weighted. The investment universe is all commodity futures contracts.
Hedge for stocks during bear markets
No - Term Structure (or Carry) in commodities is not such a good hedge/diversifier as a momentum factor. Bakshi, Bakshi, and Rossi’s research paper “Understanding the Sources of Risk Underlying the Cross-Section of Commodity Returns” analyzes the exposure of various commodity factor strategies and shows that the commodity Carry factor is linked to innovations in global equity return volatility. In periods where global equity volatility increases (decreases), the carry factor delivers low (high) returns. Additionally, innovations in global equity volatility can price the commodity portfolios sorted on the Carry. At the same time, we show that innovations in global equity volatility cannot price commodity portfolios sorted on momentum. The economic intuition is that the high average returns to carry is compensation for the low payoff of the strategy when volatility increases.
Strategy's implementation in QuantConnect's framework