A Multi-recurrent Network for Crude Oil Price Prediction

Crude oil is fundamental for global growth and stability. The factors influencing crude oil prices and more generally, the oil market, are well known to be dynamic, volatile and evolving. Subsequently, crude oil prediction is a complex and notoriously difficult task. In this paper, we evaluate the Multi-recurrent Network (MRN), a simple yet powerful recurrent neural network, for oil price forecasting at various forecast horizons. Although similar models, such as Long Short-Term Memory (LSTM) networks, have shown some success in this domain, the MRN is a comparatively simplified neural network model which exhibits complex state-based memories that are both flexible and rigid. We evaluate the MRN against the standard Feedforward Multilayered Perceptron (FFMLP) and the Simple Recurrent Network (SRN) in addition to the current state-ofthe-art LSTM for specifically modelling the shocks in oil prices caused by the financial crisis. The in-sample data consists of key indicator variables sampled across the pre-financial crisis period (July 1969 to September 2003) and the out-sample data used to evaluate the models, is before, during and beyond the crisis (October 2003 to March 2015). We show that such simple sluggish state-based models are superior to the FFMLP, SRN and LSTM models. Furthermore, the MRN appears to have discovered important latent features embedded within the input signal five years prior to the 2008 financial crisis. This suggests that the indicator variables could provide Central Banks and governments with early warning indicators of impending financial perturbations which we consider an invaluable finding and worthy of further exploration.