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  • Microscopic Momentum in Commodity Futures

    Robert J. Bianchi, Michael E. Drew and John Hua Fan

    No. 2015-10

    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).

    ISSN 1836-8123

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    Microscopic Momentum in Commodity Futures

    Robert J. Bianchi, Michael E. Drew and John Hua Fan

    Department of Accounting, Finance and Economics, Griffith Business School

    Griffith University

    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

    JEL classification:

    G11, G13, G14

    Corresponding author. 170 Kessels Road, Nathan, Queensland 4111, Australia. Tel: +61 7 373 53948;

    Fax: +61 7 373 57760. E-mail: j.fan@griffith.edu.au (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.

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    1. Introduction

    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.

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    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

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    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).

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    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.

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    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...


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