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REST: Relational Event-driven Stock Trend Forecasting

Author

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  • Wentao Xu
  • Weiqing Liu
  • Chang Xu
  • Jiang Bian
  • Jian Yin
  • Tie-Yan Liu

Abstract

Stock trend forecasting, aiming at predicting the stock future trends, is crucial for investors to seek maximized profits from the stock market. Many event-driven methods utilized the events extracted from news, social media, and discussion board to forecast the stock trend in recent years. However, existing event-driven methods have two main shortcomings: 1) overlooking the influence of event information differentiated by the stock-dependent properties; 2) neglecting the effect of event information from other related stocks. In this paper, we propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks. The experimental studies on the real-world data demonstrate the efficiency of our REST framework. The results of investment simulation show that our framework can achieve a higher return of investment than baselines.

Suggested Citation

  • Wentao Xu & Weiqing Liu & Chang Xu & Jiang Bian & Jian Yin & Tie-Yan Liu, 2021. "REST: Relational Event-driven Stock Trend Forecasting," Papers 2102.07372, arXiv.org, revised Feb 2021.
    • Handle: RePEc:arx:papers:2102.07372
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    References listed on IDEAS

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    Cited by:

    1. Sheng Xiang & Dawei Cheng & Chencheng Shang & Ying Zhang & Yuqi Liang, 2023. "Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction," Papers 2305.08740, arXiv.org.
    2. Lifan Zhao & Shuming Kong & Yanyan Shen, 2023. "DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting," Papers 2306.09862, arXiv.org, revised Apr 2024.
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    4. Shuo Yu & Hongyan Xue & Xiang Ao & Feiyang Pan & Jia He & Dandan Tu & Qing He, 2023. "Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning," Papers 2306.12964, arXiv.org.
    5. Wentao Xu & Weiqing Liu & Lewen Wang & Yingce Xia & Jiang Bian & Jian Yin & Tie-Yan Liu, 2021. "HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information," Papers 2110.13716, arXiv.org, revised Jan 2022.
    6. Liang Zeng & Lei Wang & Hui Niu & Ruchen Zhang & Ling Wang & Jian Li, 2021. "Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling," Papers 2107.11972, arXiv.org, revised May 2023.
    7. Zikai Wei & Bo Dai & Dahua Lin, 2023. "E2EAI: End-to-End Deep Learning Framework for Active Investing," Papers 2305.16364, arXiv.org.
    8. Wai Khuen Cheng & Khean Thye Bea & Steven Mun Hong Leow & Jireh Yi-Le Chan & Zeng-Wei Hong & Yen-Lin Chen, 2022. "A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
    9. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.

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