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The Dynamics of Commodity Research: A Multi-Dimensional Bibliometric Analysis

Author

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  • Ionuț Nica

    (Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 0105552 Bucharest, Romania)

  • Nora Chiriță

    (Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 0105552 Bucharest, Romania)

Abstract

This study presents a comprehensive bibliometric analysis conducted in R Studio of the scientific landscape regarding commodity markets, trading strategies, sustainable production, integration of technologies such as machine learning, and their economic impacts, covering publications from 1974 to 2023. Employing a sophisticated query in Scopus, we meticulously compiled and analyzed data, revealing an annual growth rate of 10.46% in related scientific publications, with an average citation rate of 6.60 per document. The results indicate sustained interest in commodity research over time, with a significant increase observed in scientific production, particularly since the early 2008s. International collaboration is prominent, reflecting the global nature of research in commodity markets. Key themes such as “futures markets”, “commodity prices”, and “energy commodities” emerge from the analysis of keywords and bigrams, highlighting areas of interest within the field. Additionally, thematic mapping highlights emerging and niche themes in commodity research, providing insight into evolving trends and areas of specialization. Factorial analysis of keywords reveals the underlying structures of association between key concepts, shedding light on the intricate dynamics of research in the field of commodities. This research delineates the complex interplay between commodity markets and global economic dynamics, offering invaluable insights for academics, policymakers, and market participants aiming to navigate the intricate world of commodities in the digital age.

Suggested Citation

  • Ionuț Nica & Nora Chiriță, 2024. "The Dynamics of Commodity Research: A Multi-Dimensional Bibliometric Analysis," Commodities, MDPI, vol. 3(2), pages 1-24, April.
    • Handle: RePEc:gam:jcommo:v:3:y:2024:i:2:p:9-150:d:1371812
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    References listed on IDEAS

    as
    1. Ionuț Nica & Camelia Delcea & Nora Chiriță & Ștefan Ionescu, 2024. "Quantifying Impact, Uncovering Trends: A Comprehensive Bibliometric Analysis of Shadow Banking and Financial Contagion Dynamics," IJFS, MDPI, vol. 12(1), pages 1-35, March.
    2. Vacha, Lukas & Barunik, Jozef, 2012. "Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis," Energy Economics, Elsevier, vol. 34(1), pages 241-247.
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    4. Dahl, Roy Endré & Oglend, Atle & Yahya, Muhammad, 2020. "Dynamics of volatility spillover in commodity markets: Linking crude oil to agriculture," Journal of Commodity Markets, Elsevier, vol. 20(C).
    5. Jacks, David S. & Stuermer, Martin, 2020. "What drives commodity price booms and busts?," Energy Economics, Elsevier, vol. 85(C).
    6. Nazlioglu, Saban & Erdem, Cumhur & Soytas, Ugur, 2013. "Volatility spillover between oil and agricultural commodity markets," Energy Economics, Elsevier, vol. 36(C), pages 658-665.
    7. Du, Xiaodong & Yu, Cindy L. & Hayes, Dermot J., 2011. "Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis," Energy Economics, Elsevier, vol. 33(3), pages 497-503, May.
    8. Miffre, Joelle & Rallis, Georgios, 2007. "Momentum strategies in commodity futures markets," Journal of Banking & Finance, Elsevier, vol. 31(6), pages 1863-1886, June.
    9. Kang, Sang Hoon & McIver, Ron & Yoon, Seong-Min, 2017. "Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets," Energy Economics, Elsevier, vol. 62(C), pages 19-32.
    10. Nazlioglu, Saban & Soytas, Ugur, 2012. "Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis," Energy Economics, Elsevier, vol. 34(4), pages 1098-1104.
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