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.
DOI
10.1016/j.econmod.2015.12.014
Publication Date
2016-04-01
Publication Title
Economic Modelling
Volume
54
Publisher
Elsevier B.V.
Embargo Period
2024-11-19
Keywords
Oil price prediction, Gene expression programming, Neural networks, ARIMA
First Page
40
Last Page
53
Recommended Citation
El-Masry, A., & Mostafa, M. (2016) 'Oil price forecasting using gene expression programming and artificial neural networks', Economic Modelling, 54, pp. 40-53. Elsevier B.V.: Available at: https://doi.org/10.1016/j.econmod.2015.12.014
