Oil Price Forecasting Using Gene Expression Programming and Artificial Neural Networks

Posted: 16 Nov 2018

See all articles by Mohamed M. Mostafa

Mohamed M. Mostafa

Gulf University for Science and Technology (GUST)

Ahmed A El-Masry

Coventry University Centre for Financial and Corporate Integrity

Date Written: April 1, 2016

Abstract

This study aims to forecast oil prices using evolutionary techniques such as gene expression programming (GEP) and artificial neural network (NN) models to predict oil prices over the period from January 2, 1986 to June 12, 2012. Autoregressive integrated moving average (ARIMA) models are employed to benchmark evolutionary models. The results reveal that the GEP technique outperforms traditional statistical techniques in predicting oil prices. Further, the GEP model outperforms the NN and the ARIMA models in terms of the mean squared error, the root mean squared error and the mean absolute error. Finally, the GEP model also has the highest explanatory power as measured by the R-squared statistic. The results of this study have important implications for both theory and practice.

Keywords: Oil price prediction, Gene expression programming, Neural networks, ARIMA

JEL Classification: C45, C53, C73, F37, G17

Suggested Citation

Mostafa, Mohamed M. and El-Masry, Ahmed A, Oil Price Forecasting Using Gene Expression Programming and Artificial Neural Networks (April 1, 2016). Mostafa, M. and El-Masry, A. (2016). Oil Price Forecasting Using Gene Expression Programming and Artificial Neural Networks, Economic Modelling, Vol. 54, 2016, Available at SSRN: https://ssrn.com/abstract=3257058

Mohamed M. Mostafa

Gulf University for Science and Technology (GUST) ( email )

Ahmed A El-Masry (Contact Author)

Coventry University Centre for Financial and Corporate Integrity ( email )

William Morris Building
Gosford Street
Coventry, CV1 5FB
United Kingdom

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