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Microscopic Momentum in Commodity Futures
Robert J. Bianchi, Michael E. Drew and John Hua Fan
Series Editors: Dr Suman Neupane and Professor Eduardo Roca
Copyright 2015 by the author(s). No part of this paper may be reproduced in any form, or stored in a retrieval system, without prior permission of the author(s).
Microscopic Momentum in Commodity Futures
Robert J. Bianchi, Michael E. Drew and John Hua Fan
Department of Accounting, Finance and Economics, Griffith Business School
This version: January 2015
Conventional momentum strategies rely on 12 months of past returns for portfolio
formation. Novy-Marx (2012) shows that the intermediate return momentum strategy
formed using only twelve to seven months of returns prior to portfolio formation
significantly outperforms the recent return momentum formed using six to two month
returns prior. This paper proposes a more granular strategy termed microscopic
momentum, which further decomposes the intermediate and recent return momentum
into single-month momentum components. The novel decomposition reveals that a
microscopic momentum strategy generates persistent economic profits even after
controlling for sector-specific or month-of-year commodity seasonality effects.
Moreover, we show that the intermediate return momentum in the commodity futures
must be considered largely illusory, and all 12 months of past returns play important
roles in determining the conventional momentum profits.
Commodity Futures, Conventional Momentum, Echo, Microscopic Momentum
G11, G13, G14
Corresponding author. 170 Kessels Road, Nathan, Queensland 4111, Australia. Tel: +61 7 373 53948;
Fax: +61 7 373 57760. E-mail: firstname.lastname@example.org (J. Fan)
The authors wish to thank Graham Bornholt, Ana-Maria Fuertes, Petko Kalev, Andrew Kaleel, Mathew
Kaleel, Suman Neupane, Eduardo Roca, Neda Todorova, Bruce Vanstone, Adam Walk, Peter Stoltz,
and Terry Walter for helpful comments. We also thank seminar participants at Griffith University,
reviewers at the Auckland Finance Meeting 2013 for their inputs and Lynette Hardman for editorial
assistance. We thank Standard and Poors Sydney for clarification on index construction. We also
acknowledge Griffith Business School's valuable trading room facilities which greatly assisted in
sourcing data for this research. Financial assistance from H3 Global Advisors Pty Ltd and Griffith
University is gratefully acknowledged. All remaining errors are our own.
Momentum, or short term return continuation, is one of the most persistent asset
pricing anomalies in the literature to date. Momentum strategies exploit the return
continuation by buying the best and short-selling the worst performing stocks.
Momentum has been found to exhibit statistically and economically significant profits
in global equity markets (Jegadeesh and Titman, 1993; Chan et. al., 1996;
Rouwenhorst, 1998; Griffin et. al., 2003). An extensive body of literature has
attempted to explain the source of the momentum phenomena, however, a general
consensus on the causes of momentum remains inconclusive.
The conventional momentum strategy of Jegadeesh and Titman (1993, thereafter JT),
relies on the entire 12 months of past returns for portfolio construction. Recently,
Novy-Marx (2012, thereafter RNM) reveals that, intermediate returns (12 to 7 months
prior to portfolio formation) are a more superior future performance indicator
compared to recent returns (6 to 2 months prior to formation).1 As a result, RNM
argues that momentum is not a tendency of return continuation, but instead behaves
more like an echo effect.
In this study, we propose a third type of momentum strategy termed Microscopic
Momentum, which further decomposes the recent (6 to 2 months) and intermediate (12
to 7 months) momentum of Novy-Marx (2012) into 12 single-month individual
momentum components. As a consequence of the decomposition, we are able to take a
glimpse at momentum profits under a month-by-month, microscopic scale. For the first
time, this novel approach not only reveals a striking new discovery of a momentum
based anomaly, but also allows us to pinpoint whether specific months in the past play
a more significant role in determining conventional and echo momentum profits, hence
it offers fresh insights into our understanding of momentum in commodity futures.
The commodity futures market is of interest for several reasons. First, commodities as
an investment offer the benefits of inflation-hedging and portfolio diversification
(Bodie and Rosansky, 1980; Erb and Harvey, 2006; Gorton and Rouwenhorst, 2006).
As a result, more attention from the literature is warranted due to the emerging
1 Intermediate return is the return from the past 12 to seven months, denoted as the 12,7 strategy and the
recent return represents returns six to two month prior, denoted as the 6,2 strategy.
importance of commodity futures in tactical and strategic asset allocation. Second,
lower trading costs relative to the equity markets provides an additional advantage in
the execution of momentum based investment strategies in commodity futures. As
shown in Locke and Venkatesh (1997), the average cost per trade in futures markets is
0.033% as opposed to 2.3% in the stock market (Korajczyk and Sadka, 2004).
Furthermore, the number of tradeable commodities is only a fraction of the stock
market, which makes momentum strategies much less trading intensive in commodity
futures. Third, the momentum literature focuses extensively on the stock market,
therefore, it presents a major limitation in our understanding of the mechanics of
momentum (Moskowitz et. al., 2012; Asness et. al., 2013).
The proposed granular analysis of microscopic momentum makes four major
contributions to the commodity futures literature. First, in the commodity futures
markets, the 11,10 microscopic momentum strategy, constructed using the 11 to 10-
month return prior to formation, produces an annualised average return of 14.74% with
strong statistical significance. The superiority of the 11,10 strategy is not driven by
sector-specific nor month-of-year commodity seasonality effects and is robust across
sub-periods and out-of-sample analysis. Second, when the RNM echo momentum is
regressed against its microscopic components, RNM intermediate momentum can be
completely subsumed by the 11,10 microscopic momentum. Thus, the superior
performance of intermediate momentum claimed by RNM may be an illusion created
by the 11,10 microscopic momentum. This implies that for tactical asset allocation
decisions, CTAs and commodity fund managers must not consider intermediate
momentum as a viable substitute for conventional momentum strategies. Instead, the
11,10 microscopic strategy, which offers similar profits in magnitude but unique
dynamics of returns to conventional strategies, may be a feasible alternative.
Third, around 77% of the variation of returns in the JT conventional momentum
strategy can be explained by its microscopic decomposition. However, since no
dominance is found on any individual month, all past months are found to be important
in determining the conventional commodity momentum profits. Fourth, echo and
microscopic momentum is partially related to the U.S. cross-sectional equity
momentum and the returns from broad commodity futures, but is not related to stocks,
bonds, foreign currency risks and macroeconomic conditions. Consistent with Asness
et. al., (2013), this finding implies that there may indeed be a common component in
momentum across asset classes.
The remainder of the paper is organised as follows. Section 2 discusses the related
literature on commodity futures momentum. Section 3 describes the data employed in
this study and details various methodologies used to form commodity momentum
portfolios. Section 4 reports the results of conventional, echo and microscopic
momentum strategies followed by robustness checks. Section 5 discusses the insights
from microscopic momentum and Section 6 reports the factor loadings of all
momentum strategies. The paper provides concluding remarks in Section 7.
2. Related Literature
Studies that focus on the explanation of momentum can be generally grouped into two
main strands, rational and behavioural. Rational explanations attempt to relate
momentum with different risk premia under an efficient market framework. For
example, macroeconomic risk (Griffin et. al., 2003; Antoniou et. al., 2007),
conditional risk (Lewellen and Nagel, 2006; Li et. al., 2008), market states (Cooper et.
al., 2004), liquidity risk (Sadka, 2006), credit risk (Avramov et. al., 2007) and trading
volume (Lee and Swaminathan, 2000). On the other hand, behavioural theorists often
attribute momentum to investors behavioural and physiological biases. For example,
conservatism (Barberis et. al., 1998), overconfidence (Daniel et. al., 1998; Hong and
Stein, 1999; Hvidkjaer, 2006), individualism (Chui et. al., 2010) and heterogeneity of
beliefs (Verardo, 2009).
In addition to the stock market, momentum is also present in alternative asset classes
such as commodities, currencies, real estates and bond markets.2 In commodity futures,
Miffre and Rallis (2007) show that momentum strategies generate statistically
significant profits and conclude that the profitability does not reflect a compensation
for bearing risks (both static and time-varying) but is related to the commodity futures
term structure information. Shen et. al., (2007) present supporting evidence and show
that commodities momentum is more consistent with overreaction hypothesis rather
2 Momentum studies in other markets include Commodities: Miffre and Rallis (2007), Shen et. al.,
(2007); Currencies: Okunev and White (2003), Menkhoff et. al., (2012); Real estate: Chui et. al., (2003),
Derwall et. al., (2009); Fixed-income: Gebhardt et. al., (2005), Asness et. al., (2013).
than a compensation for risk. Consistent with Miffre and Rallis (2007) and Shen et. al.,
(2007), we find strong and robust evidence of conventional momentum in commodity
futures. Momentum profits are positive across all ranking and holding periods,
however, weakens quickly with decreasing statistical significance when the holding
period is lengthened. Moreover, momentum profits in commodity futures appear to be
dominated by the long positions (the momentum winner portfolio) since returns on
short positions (the momentum loser portfolio) are extremely small and insignificant.
Using CRSP data, RNM shows that momentum profits are primarily driven by firms
performance 12 to 7 months prior to portfolio formation. However, by examining 37
stock markets including the United States, Goyal and Wahal (2013) conclude that
cross-sectional echo momentum is not a robust phenomenon outside of the U.S. market.
Also in sharp contrast to RNM, Yao (2012) decomposes intermediate momentum into
January and non-January components and argues that the superior performance of
intermediate term (echo) momentum is an artifact of the strong January seasonality in
the cross-section of U.S. stock returns. While the key focus is on the U.S. equities
market, RNM briefly showed that the findings of echo momentum also hold in
commodity futures. In light of the controversies surrounding RNM, this study provides
new evidence relating to echo momentum in commodity futures, whereby the
intermediate return strategy outperforms the recent return momentum strategy.
Nevertheless, it is important to take note that the outperformance is insubstantial and
the recent momentum strategy does not produce statistically significant profits.
3. Data and Commodity Portfolio Formation
We obtain daily closes of excess return commodity futures price series with embedded
roll yields from 31 December 1969 to 31 December 2011. 3 The monthly futures
returns are estimated using end-of-month prices. To avoid momentum strategies being
dominated by any single commodity, the sample period commences from January
1977. Since only five commodities were tradable at the inception date (December
1969), the selected commencement date (January 1977) ensures that at least three
different commodities can be included in each winner and loser momentum portfolio.
As our sample ends in 2011, we are able to fully capture the period of the 2008 GFC.
3 To be consistent with the literature in the equities market, the term excess return in the commodity
futures market represents total return in excess of the fully collateralised return.
The total number of commodities available ranges from 9 futures markets at the
beginning of the sample to a peak of 27 markets from March 2007 onwards. All
commodity futures price series are sourced from Bloomberg and Datastream
The commodity futures price series included in our sample replicate the actual
movements of the designated futures markets from industrial metals, precious metals,
energy and agricultural sectors. The raw futures contracts used to construct the price
series originate from the following commodity exchanges. Industrial metals sector:
aluminium, copper, lead, nickel, tin and zinc are traded on the London Metal Exchange
(LME). Precious metal sectors: gold and silver are traded on the New York
Commodities Exchange (COMEX). Platinum is traded on the New York Mercantile
Exchange (NYMEX). Agricultural sector: coffee, sugar no.11, cocoa and cotton no. 2
are traded on the Intercontinental Exchange (ICE US). Chicago wheat, corn, soybean
and soybean oil are traded on the Chicago Board of Trade (CBOT). Kansas wheat is
traded on the Kansas City Board of Trade (KCBT). Livestock sector: lean hogs, live
cattle and feeder cattle are traded on the Chicago Mercantile Exchange (CME). Energy
sector: brent crude oil and gas oil are traded on the ICE UK. Crude oil, heating oil,
RBOB gas and natural gas are traded on NYMEX. 5
These commodity futures price series, published and maintained by Standard and
Poors, are constructed in a similar way as the Standard and Poors Goldman Sachs
Commodity Index (S&P GSCI).6 The S&P GSCI is often criticised for possessing
excessive weights in the energy sector due to its relative importance in average world
production levels. As S&P GSCI individual commodity futures indices are not
production-weighted, the criticisms of index domination cannot be attributed to
individual commodity indices.
4 Bloomberg and Datastream are widely used...