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This paper presents a model based on multilayer feedforward neural network toforecast crude oil spot price direction in the short-term, up to three daysahead. A great deal of attention was paid on finding the optimal ANN modelstructure. In addition, several methods of data pre-processing were tested. Ourapproach is to create a benchmark based on lagged value of pre-processed spotprice, then add pre-processed futures prices for 1, 2, 3,and four months tomaturity, one by one and also altogether. The results on the benchmark suggestthat a dynamic model of 13 lags is the optimal to forecast spot price directionfor the short-term. Further, the forecast accuracy of the direction of themarket was 78%, 66%, and 53% for one, two, and three days in futureconclusively. For all the experiments, that include futures data as an input,the results show that on the short-term, futures prices do hold new informationon the spot price direction. The results obtained will generate comprehensiveunderstanding of the crude oil dynamic which help investors and individuals forrisk managements.

Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices