Analysis of the relation between crude oil futures prices and spot price using nonlinear artificial neural networks
- Authors: Haidar, Imad , Pan, Heping , Kulkarni, Siddhivinayak
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at 37th Annual Conference of Economists, Gold Coast, Queensland : 30th September - 4th October 2008
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- Description: As the oil demand continues to surge ahead and production continues to decline, it is believed that oil prices will continue to rise to unprecedented levels. As a reference, in 2004, the crude oil price was averaging $41 per barrel while it is above $130 in today’s market. This oil price increase is affecting the economy from both developing and developed countries. This paper investigates the possibility of using oil futures price to forecast spot price direction for short term, one day ahead using multilayer feedforward neural networks. The data was pre-processed to reflect the direction and the turning point of the price. Our approach is to create a benchmark based on lagged value of pre-processed spot price, then add pre-processed futures prices 1, 2, 3 and 4 months for maturity one by one and also altogether. For all the experiments, that include futures data as an input, the results show that on the short term, one day ahead, there is weak evidence to support futures price do hold new information on the spot price direction. This evidence is stronger for futures 1, 2 months to maturity.
- Description: 2003007719
Forecasting model for crude oil prices based on artificial neural networks
- Authors: Haidar, Imad , Kulkarni, Siddhivinayak , Pan, Heping
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2008, Sydney, New South Wales : 15th-18th December 2008 p. 103-108
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- Description: This paper presents short-term forecasting model for crude oil prices based on three layer feedforward neural network. Careful attention was paid on finding the optimal network structure. Moreover, a number of features were tested as an inputs such as crude oil futures prices, dollar index, gold spot price, heating oil spot price and S&P 500 index. The results show that with adequate network design and appropriate selection of the training inputs, feedforward networks are capable of forecasting noisy time series with high accuracy.
- Description: 2003006659
Short-term forecasting model for crude oil price based on artificial neural networks
- Authors: Haidar, Imad
- Date: 2008
- Type: Text , Thesis , Masters
- Full Text: false
- Description: This thesis examines the ability of Artificial Neural Networks (ANN) to predict crude oil spot price direction and short-term trends.
- Description: Masters of Computing