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Fortify the investment performance of crude oil market by integrating sentiment analysis and an interval-based trading strategy

Author

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  • Yang, Kun
  • Cheng, Zishu
  • Li, Mingchen
  • Wang, Shouyang
  • Wei, Yunjie

Abstract

To mitigate the impact of market uncertainty on trading investments, this paper proposes a forecasting and investing framework for crude oil market by integrating interval models and machine learning models. Firstly, natural language processing technique is employed to analyze text information from social and news media, enabling the capture of market and societal sentiment. Subsequently, deep learning models are integrated to combine sentiment data with other economic variables for more accurate predictions of crude oil prices. Furthermore, this paper introduces a trading strategy with interval constraints based on interval prediction models to reduce trading risk arising from the uncertainty of point forecasts in investments. Through trading simulations, it is discovered that employing the interval constrained strategy is more effective in reducing trading risk and enhancing investment returns compared to point-based trading strategies. This interval-based strategy offers a novel approach to mitigating investment risk in the crude oil market.

Suggested Citation

  • Yang, Kun & Cheng, Zishu & Li, Mingchen & Wang, Shouyang & Wei, Yunjie, 2024. "Fortify the investment performance of crude oil market by integrating sentiment analysis and an interval-based trading strategy," Applied Energy, Elsevier, vol. 353(PA).
    • Handle: RePEc:eee:appene:v:353:y:2024:i:pa:s0306261923014666
    DOI: 10.1016/j.apenergy.2023.122102
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    3. Di Sha & Xianyi Zeng & Arne Johannssen & Ruolin Wang & Kim Phuc Tran, 2026. "A Two‐Stage NLP‐Driven Framework for Interval‐Valued Carbon Price Prediction Using Sentiment Analysis and Error Correction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(2), pages 806-818, March.
    4. Kun Yang & Ruxin Deng & Yunjie Wei & Shouyang Wang, 2025. "The power of ChatGPT in processing text: Evidence from analysis and prediction in the exchange rate markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-33, December.
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    7. Wang, Zhengzhong & Wei, Yunjie & Wang, Shouyang, 2025. "Forecasting the carbon price of China's national carbon market: A novel dynamic interval-valued framework," Energy Economics, Elsevier, vol. 141(C).
    8. Dinggao Liu & Liuqing Wang & Shuo Lin & Zhenpeng Tang, 2025. "A Novel Multi-Task Learning Framework for Interval-Valued Carbon Price Forecasting Using Online News and Search Engine Data," Mathematics, MDPI, vol. 13(3), pages 1-23, January.
    9. Zhuo, Xingxuan & Ye, Jianjiang & Liu, Han & Lin, Feng, 2025. "Analyzing dynamics of crude oil price amid sudden events and intervention measures: Insights from a Prophet-QR model," Applied Energy, Elsevier, vol. 401(PB).
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