Forecasting commodity prices: empirical evidence using deep learning tools

Hachmi Ben Ameur; Sahbi Boubaker; Zied Ftiti; Wael Louhichi; Kais Tissaoui

doi:10.1007/s10479-022-05076-6

Forecasting commodity prices: empirical evidence using deep learning tools

Ann Oper Res. 2023 Jan 20:1-19. doi: 10.1007/s10479-022-05076-6. Online ahead of print.

Authors

Hachmi Ben Ameur¹, Sahbi Boubaker², Zied Ftiti³, Wael Louhichi⁴, Kais Tissaoui^{5

6}

Affiliations

¹ INSEEC Grande Ecole, Omnes Education, Paris, France.
² Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21959 Saudi Arabia.
³ OCRE-Lab, EDC Paris Business School, Paris, France.
⁴ ESSCA School of Management, Paris, France.
⁵ University of Ha'il, Applied College, Hail City, PO Box 2440 Saudi Arabia.
⁶ Faculty of Economic Sciences and Management of Tunis, University of Tunis El Manar, the International Finance Group, Tunis, Tunisia.

Abstract

Since the last two decades, financial markets have exhibited several transformations owing to recurring crises episodes that has led to the development of alternative assets. Particularly, the commodity market has attracted attention from investors and hedgers. However, the operational research stream has also developed substantially based on the growth of the artificial intelligence field, which includes machine learning and deep learning. The choice of algorithms in both machine learning and deep learning is case-sensitive. Hence, AI practitioners should first attempt solutions related to machine learning algorithms, and if such solutions are unsatisfactory, they must apply deep learning algorithms. Using this perspective, this study aims to investigate the potential of various deep learning basic algorithms for forecasting selected commodity prices. Formally, we use the Bloomberg Commodity Index (noted by the Global Aggregate Index) and its five component indices: Bloomberg Agriculture Subindex, Bloomberg Precious Metals Subindex, Bloomberg Livestock Subindex, Bloomberg Industrial Metals Subindex, and Bloomberg Energy Subindex. Based on daily data from January 2002 (the beginning wave of commodity markets' financialization) to December 2020, results show the effectiveness of the Long Short-Term Memory method as a forecasting tool and the superiority of the Bloomberg Livestock Subindex and Bloomberg Industrial Metals Subindex for assessing other commodities' indices. These findings is important in term for investors in term of risk management as well as policymakers in adjusting public policy, especially during Russian-Ukrainian war.

Keywords: Bloomberg Commodity Index; Commodity markets; Deep learning; Forecasting; Performance metrics.

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