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New algorithm could be used to predict oil price rises

Staff writer ▼ | March 9, 2016
Future fluctuations in oil prices could be forecast using a combination of previous statistics and complex computer algorithms, according to new research.
Oil prices
Predictions   Several years of data
Academics from the Gulf University for Science and Technology and Plymouth University used a range of programmed models to accurately predict previous rises and falls in the commodity's value over a period from January 1986 to June 2012.

They discovered that when provided with several years of data, a gene expression programming (GEP) model almost perfectly predicted subsequent years' figures, outperforming traditional statistical techniques.

It is also more accurate than other artificial neural network (NN) models, and the widely-used autoregressive integrated moving average (ARIMA) system.

Crude oil holds an important and growing role in the world economy, with past studies demonstrating a close relationship between oil price and the GDP growth rate.

Yet oil price prediction has always proved to be an intractable task due to the intrinsic complexity of oil market mechanisms, and other outside influences such as weather, stock levels, political aspects and even people's psychological expectations.

Dr Ahmed El-Masry, Associate Professor in Financial Management at Plymouth University, said:

"The price of oil affects people everywhere, whether they live in countries that are net importers or exporters of the commodity. And the fluctuations of recent times have led to great economic uncertainty and that will only continue as consumption – and therefore demand – increased.

"If policy makers and economists had a tool which could accurately predict future prices, it would enable them to plan for the future at the same time allowing consumers to have an idea of the rising or falling costs they might incur."


 

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