Testing Crude Oil Market Efficiency Using Artificial Neural Networks

This paper evaluates the weak-form efficiency of the crude oil markets using the artificial neural network (ANN) model. Based on the daily historical data of the West Texas Intermediate (WTI) crude oil spot price over the period (02 January 198631 December 2013), the model was trained using backpropagation algorithm. The output of the neural network represents the predicted prices which are considered as trading signals (buy or sell) for investors. Furthermore, an empirical investigation of profitability has been conducted. Compared to a naïve trading strategy as the Random Walk (RW), the profitability results show that ANN model outperformed the RW model. Therefore, the crude oil market is inefficient according to the Efficient Market Hypothesis (EMH). From these findings, we can argues that is possible to earn excess profits by making trading strategy based on the information embedded in the historical crude oil prices. Finally, the proposed neural networkbased approach becomes an interesting trading rule for the practitioners to make or to support their investment decisions. JEL Classification: G14, Q47, C45.