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A Comparison Of Var And Neural Networks With Genetic Algorithm In Forecasting Price Of Oil

In: Applications of Artificial Intelligence in Finance and Economics

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  • Sam Mirmirani
  • Hsi Cheng Li

Abstract

This study applies VAR and ANN techniques to make ex-post forecast of U.S. oil price movements. The VAR-based forecast uses three endogenous variables: lagged oil price, lagged oil supply and lagged energy consumption. However, the VAR model suggests that the impacts of oil supply and energy consumption has limited impacts on oil price movement. The forecast of the genetic algorithm-based ANN model is made by using oil supply, energy consumption, and money supply (M1). Root mean squared error and mean absolute error have been used as the evaluation criteria. Our analysis suggests that the BPN-GA model noticeably outperforms the VAR model.

Suggested Citation

  • Sam Mirmirani & Hsi Cheng Li, 2004. "A Comparison Of Var And Neural Networks With Genetic Algorithm In Forecasting Price Of Oil," Advances in Econometrics, in: Applications of Artificial Intelligence in Finance and Economics, pages 203-223, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-9053(04)19008-7
    DOI: 10.1016/S0731-9053(04)19008-7
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    Cited by:

    1. Li, Xuemei & Liu, Xiaoxing, 2023. "Functional classification and dynamic prediction of cumulative intraday returns in crude oil futures," Energy, Elsevier, vol. 284(C).
    2. Turgut Yokuş, 2024. "Early Warning Systems for World Energy Crises," Sustainability, MDPI, vol. 16(6), pages 1-18, March.

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