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

Ahmed Bouteska, Petr Hajek, Ben Fisher, Mohammad Abedin Orcid Logo

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

Swansea University Author: Mohammad Abedin Orcid Logo

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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
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 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.
Keywords: Energy market, Natural gas, Crude oil, Nonlinear focused time-delayed neural network
College: Faculty of Humanities and Social Sciences
Funders: 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.
Start Page: 101863

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