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Investor sentiment and machine learning: Predicting the price of China's crude oil futures market

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  • Jiang, Zhe
  • Zhang, Lin
  • Zhang, Lingling
  • Wen, Bo

Abstract

Sentiment analysis technology has made it possible to precisely calculate the daily reactions and opinions of investors, which has been found to have a significant influence on financial asset pricing. Thus, in this study, we examine the impacts that predictive power investor sentiment has over the price of China's crude oil. We first constructed investor sentiment indexes of China's crude oil futures based on specific economic variables and comments found on one of the most active online financial forums. Then, five popular machine learning tools were utilized to generate predictions. According to our findings, the long short-term memory model combined with the composite sentiment index performed the best due to a lower rate of prediction errors and greater directional accuracy for time-series forecasting of one-day-ahead prices. In this way, this study could aid researchers to more effectively investigate the energy sector which is rapidly changing and highly speculative in nature

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  • Jiang, Zhe & Zhang, Lin & Zhang, Lingling & Wen, Bo, 2022. "Investor sentiment and machine learning: Predicting the price of China's crude oil futures market," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003747
    DOI: 10.1016/j.energy.2022.123471
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    2. Huang, Wenyang & Gao, Tianxiao & Hao, Yun & Wang, Xiuqing, 2023. "Transformer-based forecasting for intraday trading in the Shanghai crude oil market: Analyzing open-high-low-close prices," Energy Economics, Elsevier, vol. 127(PA).
    3. Marcus Vinicius Santos & Fernando Morgado-Dias & Thiago C. Silva, 2023. "Oil Sector and Sentiment Analysis—A Review," Energies, MDPI, vol. 16(12), pages 1-29, June.

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