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Abstract

This paper reviews research on momentum in asset markets, with an emphasis on research involving momentum in commodity markets. Commodity markets are different than the markets for financial assets, such as stocks and bonds. Storage costs, inventory levels, and hedging demand by suppliers and producers influence commodity prices in ways that may not be observed in other asset classes. Research indicates that momentum profits are related to these market structure factors. The persistence of momentum in commodity markets has implications for investment products that incorporate commodities. The popularity of commodity investments among institutional investors has increased dramatically in the past decade. While some investors have allocated funds to hedge funds that incorporate momentum-based strategies, most of these investments are in indices that do not have a momentum component to the return. Research indicates that adding a momentum component to a commodity portfolio may provide strategic benefits in the form of higher risk-adjusted returns and reduced volatility.

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... This strategy has been used in many other markets. It has been shown to generate significant positive returns in most international stock markets (Rouwenhorst (1998), Chan et al. (2000)), in commodities markets (Kazemi et al. (2009)) and in currency markets (Okunev and White (2003)). For an extensive review of the research about momentum, we refer to Kazemi et al. (2009). ...
... It has been shown to generate significant positive returns in most international stock markets (Rouwenhorst (1998), Chan et al. (2000)), in commodities markets (Kazemi et al. (2009)) and in currency markets (Okunev and White (2003)). For an extensive review of the research about momentum, we refer to Kazemi et al. (2009). Currently, it seems that momentum is a behavioral feature of finance. ...
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This paper presents a novel theoretical framework to model the evolution of a dynamic portfolio (i.e., a portfolio whose weights vary over time), considering a given investment policy. The framework is based on graph theory and the quantum probability. Embedding the dynamics of a portfolio into a graph, each node of the graph representing a plausible portfolio, we provide the probabilities for a dynamic portfolio to lie on different nodes of the graph, characterizing its optimality in terms of returns. The framework embeds cross-sectional phenomena, such as the momentum effect, in stochastic processes, using portfolios instead of individual stocks. We apply our methodology to an investment policy similar to the momentum strategy of Jegadeesh and Titman (1993). We find that the strategy symmetry is a source of momentum.
... In addition, the leverage of these portfolios is fixed to 2:1 1 . This strategy is particularly interesting because it is the one used to track the momentum effect in most of the literature [Jeegadeesh and Titman (1993), Rouwenhorst (1998), Chan et al. (2000), Okunev and White (2003), Kazemi et al. (2009) and Billio et al. (2009) among others]. This LSEW strategy is also the base of pair trading [Gatev et al. (1999)]. ...
... This strategy is particularly interesting because it is the one used to track the momentum effect in most of the literature [Jeegadeesh and Titman (1993), Rouwenhorst (1998), Chan et al. (2000), Okunev and White (2003), Kazemi et al. (2009) and Billio et al. (2009) among others]. This LSEW strategy is also the base of pair trading [Gatev et al. (1999)]. ...
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Sharpe-like ratios have been traditionally used to measure the performances of portfolio managers. However, they suffer two intricate drawbacks (1) they are relative to a perr's performance and (2) the best score is generally assumed to correspond to a "good" portfolio allocation, with no guarantee on the goodness of this allocation. In this paper, we propose a new measure to quantify the goodness of an allocation and we show how to estimate this measure in the case of the strategy used to track the momentum effect, namely the Zero-Dollar Long/Short Equally Weighted (LSEW) investment strategy. Finally, we show how to use this measure to timely close the positions of an invested portfolio.
... Recent works include Shen, Szakmary and Sharma (2007) and Miffre and Rallis (2007). A pro-found overview can be found in Schneeweis, Kazemi and Spurgin (2007). ...
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This study investigates the performance of large speculators in 22 commodity markets over the last 15 years. We find that large speculators were profitable in many markets. Two possible sources of returns are analyzed for their relevance in these gains: The ability to forecast and the flow of risk premia. In contrast to earlier studies we use direct test procedures for both assessments. We find very little evidence of market timing ability. However, employing the theory of storage and using a volatility measure to proxy for convenience yield, we observe consistent risk premium earnings. Furthermore, momentum may be seen as a third source of returns to speculative activity.
... It is usual to consider that the leverage of these portfolios is 2:1 2 which is the leverage used in the following. This strategy is commonly used to track the momentum effect in most of the literature [Jegadeesh and Titman (1993), Rouwenhorst (1998), Chan et al. (2000), Okunev and White (2003), Kazemi et al. (2009) and Billio et al. (2011a) among others]. ...
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Classification JEL : C - Mathematical and Quantitative Methods/C5 - Econometric Modeling/C58 - Financial Econometrics URL des Documents de travail : http://centredeconomiesorbonne.univ-paris1.fr/bandeau-haut/documents-de-travail/
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