The predictability of aggregate returns on commodity futures

Lutzenberger, Fabian T.

doi:10.1016/j.rfe.2014.02.001

Cited by 8 publications

(5 citation statements)

References 47 publications

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“…In fact, the 12‐month commodity futures momentum strategy also shows that it generates insignificant returns in the low sentiment period (average return = 0.604 with t statistics = 1.12 in Table ) as the 12‐month stock momentum strategy does, which shows that the commodity futures momentum is also sensitive to the sentiment. Moreover, Lutzenberger () also reports that Baker and Wurgler's () sentiment index has a predictive power even in the commodity futures market, and thus it could be another motivation to employ the sentiment index in our analysis.…”

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confidence: 99%

How about selling commodity futures losers?

Kang

Kwon

2019

Journal of Futures Markets

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This paper explores the benefits of extending the investment universe to commodity futures, from the perspective of momentum traders. We find that the growth‐optimal portfolio includes negative (positive) weights on commodity futures losers (stock winners). Motivated by this finding, we construct a joint momentum strategy, buying stock winners and selling commodity futures losers, and show that it generates an average monthly return of up to 1.91% and provides much lower skewness (0.04) and kurtosis (1.27) than a traditional stock momentum strategy. It also greatly improves profitability, especially in unfavorable market states, and thus effectively manages tail risk.

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confidence: 99%

How about selling commodity futures losers?

Kang

Kwon

2019

Journal of Futures Markets

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“…Furthermore, we show that commodity and stock markets are somewhat integrated because asset pricing factors help explain the cross-section of commodity returns. This result provides further evidence for the literature that tries to link these markets (see e.g., Alves and Szymanowska, 2019;Boons et al, 2012;Brooks et al, 2016;Hou and Szymanowska, 2013;Lutzenberger, 2014;Salisu et al, 2019).…”

Section: Introductionsupporting

confidence: 74%

“…Some papers study financial variables that are commonly used to predict other asset returns, e.g., those of stock and bonds, to predict commodity returns as well. These financial variables include the dividend-price ratio, investment-price ratio, equity premium, liquidity factor, and investor sentiment (see e.g., Gargano and Timmermann, 2014;Lutzenberger, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Commodities as an Asset Class

Han¹

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Four years ago, I came to Groningen to pursue my PhD in finance, especially focusing on asset pricing. It has been such a wonderful experience for me! Thanks to my daily supervisor, Lammertjan Dam, I engaged in many interesting topics. During the first year, he suggested me many courses related to asset pricing. These courses helped me have a systematic understanding of asset pricing and made me interested in this research area. So, I decided to change my PhD research plan to asset pricing at the end of the first year. I discussed this with Lammertjan and we wrote a completely new research proposal during the summer holiday after the first year. In hindsight, I can say it was the right decision and I am very happy with where I am now. Lammertjan is a super nice research partner and I enjoyed a lot working with him. We were stuck on a technical issue about taking logs with negative values for the first project for more than two years. In March 2022, he suddenly had some "crazy" ideas to solve the technical issue. We discussed a lot and working with him at that time was like a roller coaster. Finally, we ended up with nice math, estimations, and results and updated the project in the thesis. I saw his passion for research and I learned from him how to solve difficult issues step by step. That was an amazing March and I would never forget it. Now, I consider him an important friend. When I encountered frustration in research and in life, he was always there and gave me support and encouragement.Therefore, the first person I would like to thank is Lammertjan Dam. I enjoyed this journey with him.I would like to thank my promoter, Bert Scholtens. I learned a lot about academic writing from him. He let me know that quality is better than speed. He offered many comments on our papers and my PhD thesis, which were very helpful to improve the quality of my writing. He helped me a lot with the thesis, being also especially critical to the introduction and conclusion sections. He returned his hand-written comments faster than I expected and he patiently explained those ii comments to me. Those hand-written comments are a precious memory to me. I will put them in my place. In addition, I would like to thank Walt Pohl. I am so happy to collaborate with him on the first project. He provided many constructive comments, making the paper more solid and more convincing.There are many people that have been supportive, helpful, or simply pleasant colleagues during my PhD period, and I am very grateful for that. There are some people that I would like to thank in particular. First of all, I would like to thank the members of the doctoral dissertation committee: Robert Lensink, Tommy Lundgren, and Wolfgang Bessler. Furthermore, I would like to thank Jan Jacobs for his books and for his discussion of my paper at the SOM PhD conference. I still remembered the dinner with him and Zhongbo Jing. Also, I would like to thank the colleagues in the asset pricing group:

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“…In addition to the CAPM beta, we furthermore consider betas with respect to the changes in the macroeconomic variables, presented in Table 2. Here, consistent with current methodology, we compute the first differences of each of the aforementioned variables to proxy for their innovations according to Lutzenberger (2014).…”

Section: Variables and Transformationsmentioning

confidence: 99%

Metals: resources or financial assets? A multivariate cross-sectional analysis

et al. 2016

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Metals are very important resources for industrial production, but recently they have attracted more and more attention from investors. While certainly industrial producers, consumers, and financial investors do have some influence on metal price development, the role of relevant price factors is not yet quite clear. Therefore, in this paper we examine the explanatory power of various fundamental factors and characteristics known from financial markets, specifically on the expected returns in a unique data sample of 30 metals. We applyto our knowledge for the first time in this contextthe widely accepted method of characteristicsorted portfolios, extended by the very recent method of two-way portfolio sorts as an alternative to classical multivariate regressions. This mostly non-parametric approach, combined with portfolio aggregation, provides very robust results. Our major finding is that the financial characteristics value and momentum have a very high predictive power for monthly returns of metal portfolios. Metal-specific fundamental factors like stocks, secondary production, apparent consumption, country concentration, mine production, or reserves perform depending on the interpretation moderately well or rather poorly, regarding some economically interpretable transformations and when using multivariate two-way sorts. Hence, from the perspective of expected returns, metals are predominantly assets, while fundamental metal-specific factors still play a non-negligible role. Thus, to a much lesser extent, metals can still be regarded as resources. Overall, the combination of financial characteristics and metal-specific fundamental factors yields the best results. With these robust results, we hope to contribute to a better understanding of metal prices and their underlying factors.

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The predictability of aggregate returns on commodity futures

Cited by 8 publications

References 47 publications

How about selling commodity futures losers?

How about selling commodity futures losers?

Commodities as an Asset Class

Metals: resources or financial assets? A multivariate cross-sectional analysis

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