Tactical allocation in commodity futures markets: Combining momentum and term structure signals
- Ana-Maria Fuertes, Joelle Miffre, and Georgios Rallis
- A version of the paper can be found here.
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This paper examines the combined role of momentum and term structure signals for the design of profitable trading strategies in commodity futures markets. With significant annualized alphas of 10.14% and 12.66% respectively, the momentum and term structure strategies appear profitable when implemented individually. With an abnormal return of 21.02%, a novel double-sort strategy that exploits both momentum and term structure signals clearly outperforms the single-sort strategies. This double-sort strategy can additionally be utilized as a portfolio diversification tool. Interestingly, the abnormal performance of the double-sort portfolios cannot be explained by a lack of liquidity or data mining and is robust to transaction costs and to different specifications of the risk-return trade-off.
Datastream and Bloomberg from 1979 to 2007.
- First compute all commodity futures’ roll returns (using nearest-to-maturity and second-nearest-to-maturity contracts)
- Next compute all commodity futures’ momentum. The paper uses past 1-month, 3-month, or 12-month returns to compute momentum.
- Sort roll returns into 3 groups, with highest 1/3 roll returns being “high”, and lowest 1/3 roll returns being “low.”
- Then sort the commodities in the “high” group into “winners” and “losers” based on past momentum. Do the same for the “low” group.
- Go long the “high-winners” and short the “low-losers.” All portfolios are EW and are rebalanced every month
- Table 6 shows that this strategy yields around 18-23% alpha per year.
- Paper finds that rebalancing at the end of the month or on the 15th of the month does not significantly affect returns.
- Paper also sorts on momentum first, then term structure and finds similar results, which are also in Table 6.
- After accounting for trading costs in the paper, the returns are still between 18% and 22% (Table 6).
- Table 7 also highlights that this strategy is negatively correlated with the SP500, and has a small positive correlation to bond returns.