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Forecasting Oil Price Trends with Sentiment of Online News Articles

Author

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  • Jian Li

    (Research Base of Beijing Modern Manufacturing Development, College of Economics and Management, Beijing University of Technology, Beijing 100124, P. R. China)

  • Zhenjing Xu

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, P. R. China)

  • Huijuan Xu

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, P. R. China)

  • Ling Tang

    (School of Economics and Management, Beihang University, Beijing 100191, P. R. China)

  • Lean Yu

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, P. R. China)

Abstract

With the rapid development of the Internet and big data technologies, a rich of online data (including news releases) can helpfully facilitate forecasting oil price trends. Accordingly, this study introduces sentiment analysis, a useful big data analysis tool, to understand the relevant information of online news articles and formulate an oil price trend prediction method with sentiment. Three main steps are included in the proposed method, i.e., sentiment analysis, relationship investigation and trend prediction. In sentiment analysis, the sentiment (or tone) is extracted based on a dictionary-based approach to capture the relevant online information concerning oil markets and the driving factors. In relationship investigation, the Granger causality analysis is conducted to explore whether and how the sentiment impacts oil price. In trend prediction, the sentiment is used as an important independent variable, and some popular forecasting models, e.g., logistic regression, support vector machine, decision tree and back propagation neural network, are performed. With crude oil futures prices of the West Texas Intermediate (WTI) and news articles of the Thomson Reuters as studying samples, the empirical results statistically support the powerful predictive power of sentiment for oil price trends and hence the effectiveness of the proposed method.

Suggested Citation

  • Jian Li & Zhenjing Xu & Huijuan Xu & Ling Tang & Lean Yu, 2017. "Forecasting Oil Price Trends with Sentiment of Online News Articles," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(02), pages 1-22, April.
    • Handle: RePEc:wsi:apjorx:v:34:y:2017:i:02:n:s021759591740019x
    DOI: 10.1142/S021759591740019X
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    Cited by:

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    3. Wu, Yu-Xi & Wu, Qing-Biao & Zhu, Jia-Qi, 2019. "Improved EEMD-based crude oil price forecasting using LSTM networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 114-124.
    4. Lin, Ling & Jiang, Yong & Xiao, Helu & Zhou, Zhongbao, 2020. "Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
    5. Jiangwei Liu & Xiaohong Huang, 2021. "Forecasting Crude Oil Price Using Event Extraction," Papers 2111.09111, arXiv.org.
    6. Paola Zola & Paulo Cortez & Costantino Ragno & Eugenio Brentari, 2019. "Social Media Cross-Source and Cross-Domain Sentiment Classification," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1469-1499, September.
    7. Lucey, Brian & Ren, Boru, 2021. "Does news tone help forecast oil?," Economic Modelling, Elsevier, vol. 104(C).
    8. Xu Gong & Keqin Guan & Qiyang Chen, 2022. "The role of textual analysis in oil futures price forecasting based on machine learning approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1987-2017, October.
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    10. Hao, Jun & Feng, Qianqian & Yuan, Jiaxin & Sun, Xiaolei & Li, Jianping, 2022. "A dynamic ensemble learning with multi-objective optimization for oil prices prediction," Resources Policy, Elsevier, vol. 79(C).

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