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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., 199