  This paper presents a model based on multilayer feedforward neural network to
forecast crude oil spot price direction in the short-term, up to three days
ahead. A great deal of attention was paid on finding the optimal ANN model
structure. In addition, several methods of data pre-processing were tested. Our
approach is to create a benchmark based on lagged value of pre-processed spot
price, then add pre-processed futures prices for 1, 2, 3,and four months to
maturity, one by one and also altogether. The results on the benchmark suggest
that a dynamic model of 13 lags is the optimal to forecast spot price direction
for the short-term. Further, the forecast accuracy of the direction of the
market was 78%, 66%, and 53% for one, two, and three days in future
conclusively. For all the experiments, that include futures data as an input,
the results show that on the short-term, futures prices do hold new information
on the spot price direction. The results obtained will generate comprehensive
understanding of the crude oil dynamic which help investors and individuals for
risk managements.
