IDEAS home Printed from https://ideas.repec.org/a/spr/snbeco/v3y2023i7d10.1007_s43546-023-00497-2.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43546-023-00497-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s43546-023-00497-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rompotis, Gerasimos G., 2011. "Testing weak-form efficiency of exchange traded funds market," MPRA Paper 36020, University Library of Munich, Germany.
    2. Michael Heinrich Baumann, 2022. "Beating the market? A mathematical puzzle for market efficiency," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 45(1), pages 279-325, June.
    3. Cristi Spulbar & Ramona Birau & Lucian Florin Spulbar, 2021. "A Critical Survey on Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH) and Fractal Markets Hypothesis (FMH) Considering Their Implication on Stock Markets Behavior," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 1161-1165, December.
    4. Rešovský, Marcel & Gróf, Marek & Horváth, Denis & Gazda, Vladimír, 2014. "Analysis of the Lead-Lag Relationship on South Africa capital market," MPRA Paper 57309, University Library of Munich, Germany.
    5. Paul Handro & Bogdan Dima, 2024. "Analyzing Financial Markets Efficiency: Insights from a Bibliometric and Content Review," Journal of Financial Studies, Institute of Financial Studies, vol. 16(9), pages 119-175, May.
    6. Rasim Özcan & Asad ul Islam KHAN & Sundas Iftikhar, 2024. "Whether The Cr Whether The Crypto Market Is Efficient? E et Is Efficient? Evidence F vidence From Testing The Validity Of The Efficient Market Hypothesis," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 27(1), pages 113-132, March.
    7. Qianwei Ying & Tahir Yousaf & Qurat ul Ain & Yasmeen Akhtar & Muhammad Shahid Rasheed, 2019. "Stock Investment and Excess Returns: A Critical Review in the Light of the Efficient Market Hypothesis," JRFM, MDPI, vol. 12(2), pages 1-22, June.
    8. Stavarek, Daniel & Heryan, Tomas, 2012. "Day of the week effect in central European stock markets," MPRA Paper 38431, University Library of Munich, Germany.
    9. Brice Corgnet & Cary Deck & Mark DeSantis & David Porter, 2022. "Forecasting Skills in Experimental Markets: Illusion or Reality?," Management Science, INFORMS, vol. 68(7), pages 5216-5232, July.
    10. Jürgen Huber & Michael Kirchler, 2013. "Corporate campaign contributions and abnormal stock returns after presidential elections," Public Choice, Springer, vol. 156(1), pages 285-307, July.
    11. Assia Kamoune & Nafii Ibenrissoul, 2022. "Traditional versus Behavioral Finance Theory [La théorie de la finance traditionnelle contre la théorie de la finance comportementale]," Post-Print hal-03634756, HAL.
    12. Thomas Holtfort, 2019. "From standard to evolutionary finance: a literature survey," Management Review Quarterly, Springer, vol. 69(2), pages 207-232, June.
    13. Supriya Maheshwari & Raj S. Dhankar, 2017. "Profitability of Volume-based Momentum and Contrarian Strategies in the Indian Stock Market," Global Business Review, International Management Institute, vol. 18(4), pages 974-992, August.
    14. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    15. Francois Mercier & Makesh Narsimhan, 2022. "Discovering material information using hierarchical Reformer model on financial regulatory filings," Papers 2204.05979, arXiv.org.
    16. Cristi Spulbar & Elena Loredana Minea, 2020. "Critical Conceptual Analysis on Modern Finance Theories," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 1081-1086, December.
    17. Taufiq Choudhry & Ranadeva Jayasekera, 2015. "Level of efficiency in the UK equity market: empirical study of the effects of the global financial crisis," Review of Quantitative Finance and Accounting, Springer, vol. 44(2), pages 213-242, February.
    18. Firat Melih Yilmaz & Engin Yildiztepe, 2024. "Statistical Evaluation of Deep Learning Models for Stock Return Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 221-244, January.
    19. Rajesh Elangovan & Francis Gnanasekar Irudayasamy & Satyanarayana Parayitam, 2022. "Month-of-the-Year Effect: Empirical Evidence from Indian Stock Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(3), pages 449-476, September.
    20. I. Marta Miranda García & María‐Jesús Segovia‐Vargas & Usue Mori & José A. Lozano, 2023. "Early prediction of Ibex 35 movements," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1150-1166, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:snbeco:v:3:y:2023:i:7:d:10.1007_s43546-023-00497-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.