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

Ahmed Bouteska, Petr Hajek, Ben Fisher, Mohammad Abedin

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This paper aims to develop an artificial neural network[sbnd]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[sbnd]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.

Original languageEnglish
Article number101863
Number of pages15
JournalResearch in International Business and Finance
Early online date26 Dec 2022
Publication statusPublished - 1 Jan 2023

Bibliographical note

Funding Information:
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.

Publisher Copyright:
© 2022 The Authors


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