Oil price forecasting using gene expression programming and artificial neural networks

Mohamed Mostafa, Ahmed El-Masry

Research output: Contribution to journalArticle

48 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)40-53
Number of pages14
JournalEconomic Modelling
Volume54
Early online date17 Jan 2016
DOIs
Publication statusPublished - Apr 2016
Externally publishedYes

Keywords

  • Oil price prediction
  • Gene expression programming
  • Neural networks
  • ARIMA

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