Analysis of Deviations Crude Oil Price Forecasting Based on Fundamentals between Exchange Rates and Stock Indices Using Artificial Neural Network
Keywords:
Artificial Neural Network (ANN), Regression Analysis, Crude Oil, Exchange Feature, Stock FeaturesAbstract
This research presents the analysis of deviations from crude oil price fundamental factors using an Artificial Neural Network based on the exchange rates of the major currencies: USD/JPY, USD/GBP, USD/EUR and USD/CHF and major world indices: S&P 500, Dow Jones, DAX and Nasdaq 100. The results of the model tested with the training dataset in WTI and Brent crude oil price forecast have MAE values of 0.365, 0.548 and MAPE values of 1.145%, 1.236%. When tested with the test dataset (Unseen), MAE values are 1.512, 1.489, with MAPE of 4.860%, 3.524%, respectively. This indicates that the prediction model can be applied in practice.
References
Zheng, B., Zhang, Y.W. Qu, F., Geng, Y. and Yu, H. (2022). Do rare earths drive volatility spillover in crude oil, renewable energy, and high-technology markets? — A wavelet-based BEKK-GARCH-X approach, Energy, vol. 251, 123951.
Kamyk, J., Kot-Niewiadomska, A. and Galos, K. (2021). The criticality of crude oil for energy security: A case of Poland, Energy, vol. 220, 119707.
Duan, H., Liu, Y. and Wang, G. (2022). A novel dynamic time-delay grey model of energy prices and its application in crude oil price forecasting, Energy, vol. 251, 123968.
Niu, Z., Liu, Y., Gao, W. and Zhang, H. (2021). The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China, Resources Policy, vol. 73, 102173.
Shang, J. and Hamori, S. (2021). Do crude oil prices and the sentiment index influence foreign exchange rates differently in oil-importing and oil-exporting countries? A dynamic connectedness analysis, Resource Policy, vol. 74, 102400.
Lang, K. and Auer, B.R. (2020). The economic and financial properties of crude oil: A review, The North American Journal of Economics and Finance, vol.52, April 2020, Article 100914.
Altawell, N., Milne, J., Seowou, P. et al. (2020). Rural Electrification, 1st edition, ISBN: 978-0-12-822403-8, Academic Press, Massachusetts.
Xiao, D. and Wang, J. (2020). Dynamic complexity and causality of crude oil and major stock markets, Energy, vol.193, February 2020, Article 116791.
Austvik, O.G. (1992). Limits to oil pricing: Scenario planning as a device to understand oil price developments, Energy Policy, vol.20(11), November 1992, 1097.
Guo, Y., Li, J., Li, Y. et al. (2021). The roles of political risk and crude oil in stock market based on quantile cointegration approach: A comparative study in China and US, Energy Economics, vol.97, May 2021, Article 105198.
Kumar, S., Choudhary, S., Singh, G. et al. (2021). Crude oil, gold, natural gas, exchange rate and Indian stock market: Evidence from the asymmetric nonlinear ARDL model, Resource Policy, vol.73, October 2021, Article 102194.
Shah, A.A., Paul, M., Bhanja, N. et al. (2021). Dynamics of connectedness across crude oil, precious metals and exchange rate: Evidence from time and frequency domains, Resource Policy, vol.73, October 2021, Article 102154.
Yang, Y., Guo, J., Sun, S. et al. (2021). Forecasting crude oil price with a new hybrid approach and multi-source data, Engineering Applications of Artificial Intelligence, vol.101, May 2021, Article 104217.
Olayeni, O.R., Tiwari, A.K. and Wohar, M.E. (2020). Global economic activity, crude oil price and production, stock market behaviour and the Nigeria-US exchange rate, Energy Economics, vol.92, October 2020, Article 104938.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Journal of Engineering and Innovative Research
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
