Thermal coal futures trading volume predictions through the neural network
Abstract
Purpose
Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.
Design/methodology/approach
The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.
Findings
A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.
Originality/value
The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.
Keywords
Acknowledgements
Ethical approval: Not applicable.
Competing interests: The authors did not receive support from any organization for the submitted work. The authors have no relevant financial or non-financial interests to disclose.
Funding: No funding.
Availability of data and materials: Available upon reasonable request.
Citation
Jin, B., Xu, X. and Zhang, Y. (2024), "Thermal coal futures trading volume predictions through the neural network", Journal of Modelling in Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JM2-09-2023-0207
Publisher
:Emerald Publishing Limited
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