A forecasting model for oil prices using a large set of economic indicators

Abstract

This paper examines the predictability of the changes in Brent oil futures prices using a multilayer perceptron artificial neural network that exploits the information contained in the largest possible set of economic indicators. Feature engineering is employed to identify the most important predictors of the change in Brent oil futures prices. We find that oil-market-specific variables are important predictors. Our findings also suggest that forecasts of the change in the Brent oil futures prices from the multilayer perceptron that exploits the informational content of all and oil-market-specific predictors exhibit higher statistical forecast accuracy than the random walk. Tests of forecast optimality indicate that the forecasts generated using oil-market-specific predictors are optimal. We discuss the policymaking and practical relevance of our results.