Forecasting Crude Oil Price Using Multiple Factors

Hind Aldabagh, Xianrong Zheng (), Mohammad Najand and Ravi Mukkamala
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Hind Aldabagh: Computer Science Department, Old Dominion University, Norfolk, VA 23529, USA
Xianrong Zheng: Information Technology & Decision Sciences Department, Old Dominion University, Norfolk, VA 23529, USA
Mohammad Najand: Finance Department, Old Dominion University, Norfolk, VA 23529, USA
Ravi Mukkamala: Computer Science Department, Old Dominion University, Norfolk, VA 23529, USA

JRFM, 2024, vol. 17, issue 9, 1-15

Abstract: In this paper, we predict crude oil price using various factors that may influence its price. The factors considered are physical market, financial, and trading market factors, including seven key factors and the dollar index. Firstly, we select the main factors that may greatly influence the prices. Then, we develop a hybrid model based on a convolutional neural network (CNN) and long short-term memory (LSTM) network to predict the prices. Lastly, we compare the CNN–LSTM model with other models, namely gradient boosting (GB), decision trees (DTs), random forests (RFs), neural networks (NNs), CNN, LSTM, and bidirectional LSTM (Bi–LSTM). The empirical results show that the CNN–LSTM model outperforms these models.

Keywords: crude oil price; LSTM; CNN; dollar index (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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