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Deep learning model with sentiment score and weekend effect in stock price prediction

Author

Listed:
  • Jingyi Gu

    (New Jersey Institute of Technology)

  • Sarvesh Shukla

    (New Jersey Institute of Technology)

  • Junyi Ye

    (New Jersey Institute of Technology)

  • Ajim Uddin

    (New Jersey Institute of Technology)

  • Guiling Wang

    (New Jersey Institute of Technology)

Abstract

Stock market forecasting is a popular area for both investment and research. It is also challenging due to the strong noise generated by the news, government policies, and investor emotions. Emerging works show that the sentiment from news accumulated over weekends significantly affects stock prices. In this paper, we propose a deep learning framework to incorporate the sentiment from weekend news on social media to predict stock price, and then conduct a comprehensive set of popular benchmarks for comparison. Specifically, our model uses Valence Aware Dictionary and Sentiment Reasoner (VADER) and self-defined sentiment measure to extract lexical features and evaluate sentiment opinions. Then our model employs a recurrent neural network to capture potential dependency from sentiment and price-based features. Extensive experiments are implemented on stock indices and Reddit news in a high volatility period, which show that neural networks outperform all benchmarks significantly and validate the weekend effect of news on the stock market.

Suggested Citation

  • Jingyi Gu & Sarvesh Shukla & Junyi Ye & Ajim Uddin & Guiling Wang, 2023. "Deep learning model with sentiment score and weekend effect in stock price prediction," SN Business & Economics, Springer, vol. 3(7), pages 1-20, July.
    • Handle: RePEc:spr:snbeco:v:3:y:2023:i:7:d:10.1007_s43546-023-00497-2
    DOI: 10.1007/s43546-023-00497-2
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    References listed on IDEAS

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    1. Weiwei Jiang, 2020. "Applications of deep learning in stock market prediction: recent progress," Papers 2003.01859, arXiv.org.
    2. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
    3. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    4. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    5. Kim, Karam & Ryu, Doojin, 2022. "Sentiment changes and the Monday effect," Finance Research Letters, Elsevier, vol. 47(PB).
    6. repec:pri:cepsud:91malkiel is not listed on IDEAS
    7. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    8. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    9. Xinyi Li & Yinchuan Li & Yuancheng Zhan & Xiao-Yang Liu, 2019. "Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation," Papers 1907.01503, arXiv.org.
    10. French, Kenneth R., 1980. "Stock returns and the weekend effect," Journal of Financial Economics, Elsevier, vol. 8(1), pages 55-69, March.
    11. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    12. Jingyi Gu & Fadi P. Deek & Guiling Wang, 2023. "Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network," Papers 2302.14164, arXiv.org.
    13. Werner Antweiler & Murray Z. Frank, 2004. "Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards," Journal of Finance, American Finance Association, vol. 59(3), pages 1259-1294, June.
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    Cited by:

    1. Jingyi Gu & Wenlu Du & Guiling Wang, 2024. "RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction," Papers 2402.10760, arXiv.org.

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