Estimation of OPEC crude oil price using binomial tree, time series and artificial neural networks

Oil price is the most important and effective economic parameter during the course of oil projects evaluation process. Oil price uncertainty is affected by some factors such as political issues, supply and demand, advancement of technology etc. Therefore, evaluating an oil project is unreliable unless these uncertainties are taken into account and in some circumstances, not doing so may mislead the oil projects evaluators, managers and shareholders. To solve this problem, many researchers have tried to present intelligent models for estimating oil prices using fuzzy logic, neural networks, etc. In addition to their high accuracy, these methods gives easier and faster estimation. In the present study, taking the importance of the oil price prediction into account, OPEC crude oil data were collected weekly during 2013-2016, and some prediction models were presented using artificial neural network method, time series functions and binomial tree. Comparing the results obtained from the three models and those of the real data showed that the estimation made by neural network method is much more reliable.