Using econometric and machine learning models to forecast crude oil prices: Insights from economic history

Abstract

The volatility of the crude oil market and its effects on the global economy increased the concerns of individual investors, states/governments, and corporations. Forecasting the price of crude oil is difficult owing to its complicated, nonlinear, and chaotic nature in economic history. Multiple variables influence crude oil prices, such as the economic history, economic cycle, international relations, and geopolitics. Predicting the price of crude oil is a complex but valuable endeavor. Crude oil price forecasting is done using historical data (time series method) or dependent variables/factors (regression method) using traditional econometric or machine learning models. In this study, we use both methods (regression and time series) to examine the prediction performance of both models (econometric and machine learning models) for daily WTI crude oil prices covering the period December 18, 2011, through December 31, 2018. We present a performance analysis of conventional econometric models (ARIMA, GARCH, and OLS), Artificial Neural Network (ANN) regression models, and ANN Time Series models to compare their results to find out the best-performing method (time series or regression) and the best model (econometric or machine learning model). Based on our study results, we propose a novel Artificial Neural Network model to improve the prediction performance of existing models by adjusting the bias and weights of ANN hidden layers. We used historical prices of 14 different variables, including gold, silver, S&P500, USD Index price, and US-EU conversion rates for regression models, whereas historical time series data of WTI crude oil for time series models. Analysis of the results reveals that the performance of our proposed model remained better than all tested models. The comparative results of existing models show that the overall performance of Neural Networks remained better than econometric models. Our results have substantial implications for governments, businesses, and investors, and for the sustainable growth of economies that rely on energy.