International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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A Bibliometric Analysis of Hedging in Commodity Derivatives Using Machine Learning.

Author(s) Ms. Bhagya Shree Tiwari, Dr. Nishant Kumar, Prof. Kaushal Kishore Shukla
Country India
Abstract The adoption and institution of AI in risk management started in the late 90s, but its active application in hedging started appearing around 2000. Besides that, its ingress in commodity derivatives was spotted two decades ago. Commodity derivatives markets are designed to manage the price risk associated with fluctuations in commodity prices. With this view, the study is conducted to review the penetration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in hedging or hedging effectiveness in commodity derivatives. This research presents a bibliometric analysis (BA) of 126 documents published in the Scopus database from the year 2000 to 2025 using science mapping and network approaches. The analysis was conducted utilizing the Biblioshiny (RStudio) and VOSviewer software. The review explores the foundation and evolution of various themes, the key conceptual and intellectual structures that are being developed with the help of AI/ML in hedging mechanisms, as well as future research trajectories. After analysis, it has been concluded that the dynamic daily hedging model, distributional reinforcement learning, deep quadratic hedging in commodity derivatives, the use of AI and ML models in cross hedging, and working on the conceptual framework and mechanisms behind these models to make them more transparent and authentic in the eyes of retail investors are the major gaps to be addressed. The study examines the literature of hedging in commodity derivatives using ML and AI and will be useful to academicians, policymakers, and operational risk management professionals.
Keywords Hedging, Commodity Derivatives, Machine Learning, Artificial Intelligence Bibliometric analysis, Science mapping
Field Business Administration
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-20
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.63907
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E-ISSN 2582-2160
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