Document Type

Article

Publication Date

2024

DOI

10.3390/jrfm17090415

Publication Title

Journal of Risk and Financial Management

Volume

17

Issue

9

Pages

415 (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.

Rights

© 2024 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Data Availability

Article states: "The dollar index data and the factors affecting crude oil prices can be downloaded from the MarketWatch website (https://www.marketwatch.com/investing/future/dx00/download-data?mod=mw_quote_tab, accessed on 30 June 2024) and the EIA website (https://www.eia.gov/finance/markets/crudeoil, accessed on 30 June 2024), respectively.

Original Publication Citation

Aldabagh, H., Zheng, X., Najand, M., & Mukkamala, R. (2024). Forecasting crude oil price using multiple factors. Journal of Risk and Financial Management, 17(9), 1-15, Article 415. https://doi.org/10.3390/jrfm17090415

ORCID

0000-0001-6323-9789 (Mukkamala)

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