Comparing the Forecasting Performance of Futures Oil Prices with Genetically Evolved Neural Networks

Mona Shazly () and Alice Lou
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Mona Shazly: Columbia College
Alice Lou: Columbia College

International Advances in Economic Research, 2016, vol. 22, issue 4, No 1, 376 pages

Abstract: Abstract In this paper, a hybrid system combining neural networks and genetic training is designed to forecast future oil prices. The architectural design is that of the multilayer back propagation network that is fed monthly prices for West Texas Intermediate covering the period 1986–2014. The model’s predictions are compared to those of the one, two, three, and four-month futures prices and are evaluated both on their level of accuracy as well as correctness. While accuracy measures the degree of error, correctness tests the model’s ability to predict the direction of the movement. By processing information more efficiently, and identifying patterns that may be ill-defined as a result of pronounced price volatility, this paper aims to improve the accuracy of oil price forecasts.

Keywords: Oil futures forecast; Neural networks; Genetic training (search for similar items in EconPapers)
JEL-codes: G15 (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11294-016-9599-3

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