Research on Commodity Futures Pricing Efficiency: A Machine Learning Perspective

Elaine Huang ()
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Elaine Huang: Shenzhen College of International Education, Economics

A chapter in Proceedings of the 2024 2nd International Conference on Economic Management, Financial Innovation and Public Service (EMFIPS 2024), 2025, pp 64-76 from Springer

Abstract: Abstract The huge size of China’s commodity market plays an important role in global asset pricing. It is significant to deeply analyse whether machine learning can extract effective information from futures market data and how effective artificial intelligence algorithms are. Based on the monthly data of index contracts of 71 varieties in China’s commodity futures market, this paper uses OLS and machine learning algorithms to extract information from the price data. Also, based on trading strategies based on predictions, this paper discusses the impact of the application of machine learning (technological progress) on the pricing efficiency of the futures market. The results show that the machine learning algorithm can improve the strategy performance on the whole, but with the development of the market, the improvement effect decreases at a significant level of 5%, that is, the market effectiveness is improving.

Keywords: Commodity futures; Pricing efficiency; Machine learning; Information mining (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-706-9_7

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DOI: 10.2991/978-94-6463-706-9_7

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