Proceedings of the 2024 2th International Conference on Economic Management, Financial Innovation and Public Service (EMFIPS 2024)

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Research on Commodity Futures Pricing Efficiency: A Machine Learning Perspective

Authors
Elaine Huang1, *
1Economics, Shenzhen College of International Education, Shenzhen, Guangdong Province, 518000, China
*Corresponding author. Email: s22533.huang@stu.scie.com.cn
Corresponding Author
Elaine Huang
Available Online 7 May 2025.
DOI
10.2991/978-94-6463-706-9_7How to use a DOI?
Keywords
Commodity futures; Pricing efficiency; Machine learning; Information mining
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.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2024 2th International Conference on Economic Management, Financial Innovation and Public Service (EMFIPS 2024)
Series
Advances in Economics, Business and Management Research
Publication Date
7 May 2025
ISBN
978-94-6463-706-9
ISSN
2352-5428
DOI
10.2991/978-94-6463-706-9_7How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Elaine Huang
PY  - 2025
DA  - 2025/05/07
TI  - Research on Commodity Futures Pricing Efficiency: A Machine Learning Perspective
BT  - Proceedings of the 2024 2th International Conference on Economic Management, Financial Innovation and Public Service (EMFIPS 2024)
PB  - Atlantis Press
SP  - 64
EP  - 76
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-706-9_7
DO  - 10.2991/978-94-6463-706-9_7
ID  - Huang2025
ER  -