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Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network

Ahmed Bouteska, Petr Hajek, Ben Fisher, Abedin Abedin

Research in International Business and Finance, Volume: 64, Start page: 101863

Swansea University Author: Abedin Abedin

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DOI (Published version): 10.1016/j.ribaf.2022.101863

Abstract

This paper aims to develop an artificial neural network-based forecasting model employing a nonlinear focused time-delayed neural network (FTDNN) for energy commodity market forecasts. To validate the proposed model, crude oil and natural gas prices are used for the period 2007–2020, including the C...

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Published in: Research in International Business and Finance
ISSN: 0275-5319
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa64246
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spelling v2 64246 2023-08-31 Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network 4ed8c020eae0c9bec4f5d9495d86d415 Abedin Abedin Abedin Abedin true false 2023-08-31 BAF This paper aims to develop an artificial neural network-based forecasting model employing a nonlinear focused time-delayed neural network (FTDNN) for energy commodity market forecasts. To validate the proposed model, crude oil and natural gas prices are used for the period 2007–2020, including the Covid-19 period. Empirical findings show that the FTDNN model outperforms existing baselines and artificial neural network-based models in forecasting West Texas Intermediate and Brent crude oil prices and National Balancing Point and Henry Hub natural gas prices. As a result, we demonstrate the predictability of energy commodity prices during the volatile crisis period, which is attributed to the flexibility of the model parameters, implying that our study can facilitate a better understanding of the dynamics of commodity prices in the energy market. Journal Article Research in International Business and Finance 64 101863 Elsevier BV 0275-5319 Energy market, Natural gas, Crude oil, Nonlinear focused time-delayed neural network 31 1 2023 2023-01-31 10.1016/j.ribaf.2022.101863 http://dx.doi.org/10.1016/j.ribaf.2022.101863 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University This work has been supported by the European Cooperation in Science & Technology COST Action grant CA19130 - Fintech and Artificial Intelligence in Finance - Towards a transparent financial industry. 2023-09-20T10:53:04.5671283 2023-08-31T17:47:19.3713155 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Ahmed Bouteska 1 Petr Hajek 2 Ben Fisher 3 Abedin Abedin 4 64246__28582__4ce68857a650437985c7735078166c46.pdf 64246.VOR.pdf 2023-09-19T14:14:14.8270103 Output 3473831 application/pdf Version of Record true © 2022 The Authors. Published by Elsevier B.V. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network
spellingShingle Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network
Abedin Abedin
title_short Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network
title_full Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network
title_fullStr Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network
title_full_unstemmed Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network
title_sort Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Abedin Abedin
author Abedin Abedin
author2 Ahmed Bouteska
Petr Hajek
Ben Fisher
Abedin Abedin
format Journal article
container_title Research in International Business and Finance
container_volume 64
container_start_page 101863
publishDate 2023
institution Swansea University
issn 0275-5319
doi_str_mv 10.1016/j.ribaf.2022.101863
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
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hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://dx.doi.org/10.1016/j.ribaf.2022.101863
document_store_str 1
active_str 0
description This paper aims to develop an artificial neural network-based forecasting model employing a nonlinear focused time-delayed neural network (FTDNN) for energy commodity market forecasts. To validate the proposed model, crude oil and natural gas prices are used for the period 2007–2020, including the Covid-19 period. Empirical findings show that the FTDNN model outperforms existing baselines and artificial neural network-based models in forecasting West Texas Intermediate and Brent crude oil prices and National Balancing Point and Henry Hub natural gas prices. As a result, we demonstrate the predictability of energy commodity prices during the volatile crisis period, which is attributed to the flexibility of the model parameters, implying that our study can facilitate a better understanding of the dynamics of commodity prices in the energy market.
published_date 2023-01-31T10:53:01Z
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score 11.013575

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