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Financial Markets and Portfolio Management

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15.04.2021

Have trend-following signals in commodity futures markets become less reliable in recent years?

verfasst von: Benjamin R. Auer

Erschienen in: Financial Markets and Portfolio Management | Ausgabe 4/2021

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Abstract

Various trend-following trading rules have been shown to be valuable for predicting market directions and thus the formulation of investment strategies. However, recent equity market research has provided striking evidence that the predictive power of such rules appears to diminish over time due to increased investor attention and lowered arbitrage barriers. Given that trend-following rules are also very successful and have been widely used in futures markets, we analyze whether a similar effect can be observed for commodity futures contracts. Using a trend regression approach based on time-varying success ratios, we detect significantly higher predictive accuracy for cross-sectional than for time-series strategies. In addition, with the exception of a few commodities, we find no significant trending behavior in trading rule reliability. These results, which are robust in a variety of settings, indicate strong momentum stability in futures markets and justify the application of this class of trading rules in commodity futures investing.

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Moskowitz et al. (2012) alternatively predict time-series continuation and reversal via regressions of (a) scaled returns on past scaled returns and (b) scaled returns on past return signs.

Time-series momentum is a timing strategy, whereas cross-sectional momentum is a selection strategy.

Furthermore, futures contracts (and futures-based momentum portfolios) are excellent diversifiers and inflation hedges, especially when they are actively managed (see Gorton and Rouwenhorst 2006; Erb and Harvey 2006; Miffre and Rallis 2007; Miffre and Fernandez-Perez 2015).

Transaction costs in futures markets range from 0.0004 to 0.033% (see Fleming et al. 1996; Locke and Venkatesch 1997), which is much less than the conservative 0.5% estimate of Jegadeesh and Titman (1993) or the more realistic 2.3% estimate of Lesmond et al. (2004) for the equity market.

For an excellent review of additional techniques to strengthen traditional momentum signals, see Miffre (2016). Also note that Fuertes et al. (2015) introduce a very intuitive method of forming bivariate and trivariate portfolios (via combined ranking scores) which might set an important standard for future research.

Because the properties of the corresponding returns have been extensively documented (see, for example, Auer 2015; Zhang et al. 2018), we omit descriptive statistics illustrating non-normality and serial correlation. However, they are available upon request.

In contrast, Dow Jones UBS commodity indices have a slightly different rollover, from the sixth to the tenth business day (see Bianchi et al. 2015a).

Miffre and Rallis (2007) show that profitability declines with rising holding period length. They even document negative (zero) average returns over horizons of 18–24 months (beyond 24 months).

Similar to Szakmary et al. (2010), we do not form one momentum portfolio based on all commodities but base our analysis on individual securities. Our cross-sectional ranking simply serves the purpose of generating winner/loser signals which may be compared with the actual winner/loser position in the holding period.

The value of w is different from typical stock market settings because this way the strategies generate neutral signals in about one-third of the time and are thus comparable to the cross-sectional strategy outlined above.

Note that Narayan et al. (2015) calculate moving averages based on returns instead of prices.

In a related area of research, Narayan et al. (2013) find that commodity futures are better predictors for spot markets when daily data are used instead of monthly data.

Strobel and Auer (2018) emphasize that alternatively conducting trend regressions based on strategy returns leads to very similar trend conclusions because success ratios and strategy returns are naturally linked.

The observable distinct results for Brent and WTI crude oil are not surprising because their time-series characteristics are quite different (see Tian and Lai 2019).

We would not expect levels very close to one because past returns are unlikely to be able to forecast unforeseeable outside events related to climate phenomena (affecting, for example, wheat prices) or crisis-related investor behavior (influencing, for example, a safe haven asset like gold).

For a detailed empirical analysis of the reasons why momentum strategies behave differently in stock and futures markets, see Chevallier et al. (2013).

Detailed results are available from the authors upon request.

Adding more futures to the winner or loser sides enhances risk diversification at the cost of lowering the dispersion of returns between the best and worst performing futures and thus the profitability of the strategy.

Adaptive averages have also received attention. They seek to identify and adopt changing market conditions via an efficiency ratio derived from the notion of fractal efficiency and a method close to rescaled range analysis (see Ellis and Parbery 2005). However, the corresponding new trading rules leave the classic VMA framework.

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Titel: Have trend-following signals in commodity futures markets become less reliable in recent years?
verfasst von: Benjamin R. Auer
Publikationsdatum: 15.04.2021
Verlag: Springer US
Erschienen in: Financial Markets and Portfolio Management / Ausgabe 4/2021
Print ISSN: 1934-4554
Elektronische ISSN: 2373-8529
DOI: https://doi.org/10.1007/s11408-021-00385-5

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