The Optimal Machine Learning Modeling of Brent Crude Oil Price - Obite, Chukwudi Paul; Bartholomew, Desmond Chekwube; Nwosu, Ugochinyere Ihuoma; Esiaba, Gladys Ezenwanyi; Kiwu, Lawrence Chizoba (Quarterly Journal of Econometrics Research, 2021)
The price of Brent crude oil is very important to the global economy as it has a huge influence and serves as one of the benchmarks in how other countries and organizations value their crude oil. Few original studies on modeling the Brent crude oil price used predominantly different classical models but the application of machine learning methods in modeling the Brent crude oil price has been grossly understudied. In this study, we identified the optimal MLMD (MLMD) amongst the Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Network (ANN), and Deep Neural Network (DNN) in modeling the Brent crude oil price and also showed that the optimal MLMD is a better fit to the Brent crude oil price than the classical Autoregressive Integrated Moving Average (ARIMA) model that has been used in original studies. Daily secondary data from the U.S. Energy Information Administration were used in this study. The results showed that the ANN and DNN models behaved alike and both outperformed the SVR and RF models and are chosen as the optimal MLMDs in modeling the Brent crude oil price. The ANN was also better than the classical ARIMA model that performed very poorly. The ANN and DNN models are therefore suggested for a close monitoring of the Brent crude oil price and also for a pre-knowledge of future Brent crude oil price changes.