Coking coal futures price index forecasting with the neural network

Xiaojie Xu () and Yun Zhang ()
Additional contact information
Xiaojie Xu: North Carolina State University
Yun Zhang: North Carolina State University

Mineral Economics, 2023, vol. 36, issue 2, No 11, 349-359

Abstract: Abstract Coking coal price forecasting is a significant issue for investors and policy makers. This study explores usefulness of the nonlinear autoregressive neural network for this forecasting problem in a dataset of daily closing prices of the coking coal futures traded in China Dalian Commodity Exchange during January 4, 2016–December 31, 2020. Through examining various model settings across the algorithm, delay, hidden neuron, and data splitting ratio, the model leading to generally accurate and stable performance is reached. Particularly, the model’s inputs are the lagged coking coal futures prices and output is the 1-day ahead price forecast. The model is based on the two-layer feedforward network with six delays and two hidden neurons, which is trained through the Levenberg-Marquardt algorithm, and leads to relative root mean square errors of 1.84%, 1.85%, and 1.84% for the training, validation, and testing phases, respectively. Usefulness of the machine learning technique for the price forecasting problem of the coking coal price is illustrated. Results here might be used on a standalone basis as technical forecasts or combined with fundamental forecasts to form perspectives of price trends and perform policy analysis.

Keywords: Coking coal; Price forecasting; Time series; Neural network; Machine learning (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s13563-022-00311-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:minecn:v:36:y:2023:i:2:d:10.1007_s13563-022-00311-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/13563

DOI: 10.1007/s13563-022-00311-9

Access Statistics for this article

Mineral Economics is currently edited by Magnus Ericsson and Patrik Söderholm

More articles in Mineral Economics from Springer, Raw Materials Group (RMG), Luleå University of Technology
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().