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Thermal coal futures trading volume predictions through the neural network

Bingzi Jin (Advanced Micro Devices (China) Co., Ltd., Shanghai, China)
Xiaojie Xu (North Carolina State University at Raleigh, Raleigh, North Carolina, USA)
Yun Zhang (North Carolina State University at Raleigh, Raleigh, North Carolina, USA)

Journal of Modelling in Management

ISSN: 1746-5664

Article publication date: 17 September 2024

51

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

Copyright © 2024, Emerald Publishing Limited

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