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

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

Applied Energy, 2024, vol. 353, issue PA, No S0306261923014666

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.

Keywords: Crude oil; Trading strategy; Interval data; Sentiment analysis; Deep learning; Natural language processing (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.apenergy.2023.122102

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