IDEAS home Printed from https://ideas.repec.org/a/wut/journl/v34y2024i2p85-107id6.html
   My bibliography  Save this article

Predicting stock market by sentiment analysis and deep learning

Author

Listed:
  • Süreyya Özöğür Akyüz
  • Pınar Karadayı Ataş
  • Aymane Benkhaldoun

Abstract

The stock market may be unpredictable; understanding when to purchase and sell can greatly assist businesses and individuals in maximizing profits and minimizing losses. Many companies have previously modified time-series analysis, a data mining technique, to forecast stock price movement. The idea of textual data mining has recently come up in debates about stock market forecasts. In this study, five of the largest firms’ historical stock prices were used to train two deep learning models—long short-term memory (LSTM) and one-dimensional convolutional neural network (1D CNN), then the results of all the models were compared. To connect price value fluctuations with the general public, sentiment scores were offered in addition to stock price values by employing natural language processing techniques (TextBlob) to tweets.

Suggested Citation

  • Süreyya Özöğür Akyüz & Pınar Karadayı Ataş & Aymane Benkhaldoun, 2024. "Predicting stock market by sentiment analysis and deep learning," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(2), pages 85-107.
    • Handle: RePEc:wut:journl:v:34:y:2024:i:2:p:85-107:id:6
    DOI: 10.37190/ord240206
    as

    Download full text from publisher

    File URL: https://ord.pwr.edu.pl/assets/papers_archive/ord2024vol34no2_6.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.37190/ord240206?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
    ---><---

    References listed on IDEAS

    as
    1. Mohammed G. Ragab & Said J. Abdulkadir & Norshakirah Aziz & Qasem Al-Tashi & Yousif Alyousifi & Hitham Alhussian & Alawi Alqushaibi, 2020. "A Novel One-Dimensional CNN with Exponential Adaptive Gradients for Air Pollution Index Prediction," Sustainability, MDPI, vol. 12(23), pages 1-22, December.
    2. Adriana Breaban & Charles N Noussair, 2018. "Emotional State and Market Behavior [Bubbling with excitement: en experiment]," Review of Finance, European Finance Association, vol. 22(1), pages 279-309.
    3. Huicheng Liu, 2018. "Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network," Papers 1811.06173, arXiv.org.
    4. Adriana Breaban & Charles N Noussair, 2018. "Emotional State and Market Behavior [Bubbling with excitement: en experiment]," Review of Finance, European Finance Association, vol. 22(1), pages 279-309.
    5. Baresa, Suzana & Bogdan , Sinisa & Ivanovic, Zoran, 2013. "Strategy Of Stock Valuation By Fundamental Analysis," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 4(1), pages 45-51.
    Full references (including those not matched with items on IDEAS)

    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. Cars Hommes & Anita Kopányi-Peuker & Joep Sonnemans, 2021. "Bubbles, crashes and information contagion in large-group asset market experiments," Experimental Economics, Springer;Economic Science Association, vol. 24(2), pages 414-433, June.
    2. Tiziana Assenza & Te Bao & Cars Hommes & Domenico Massaro, 2014. "Experiments on Expectations in Macroeconomics and Finance," Research in Experimental Economics, in: Experiments in Macroeconomics, volume 17, pages 11-70, Emerald Group Publishing Limited.
    3. Lenka Fiala & Charles N. Noussair, 2017. "Charitable Giving, Emotions, And The Default Effect," Economic Inquiry, Western Economic Association International, vol. 55(4), pages 1792-1812, October.
    4. Hermann, Daniel & Mußhoff, Oliver & Rau, Holger A., 2019. "The disposition effect when deciding on behalf of others," Journal of Economic Psychology, Elsevier, vol. 74(C).
    5. Farjam, Mike & Kirchkamp, Oliver, 2018. "Bubbles in hybrid markets: How expectations about algorithmic trading affect human trading," Journal of Economic Behavior & Organization, Elsevier, vol. 146(C), pages 248-269.
    6. Maria Karmeliuk & Martin G. Kocher & Georg Schmidt, 2022. "Teams and individuals in standard auction formats: decisions and emotions," Experimental Economics, Springer;Economic Science Association, vol. 25(5), pages 1327-1348, November.
    7. Kurowski, Łukasz & Smaga, Paweł, 2023. "Analysing financial stability reports as crisis predictors with the use of text-mining," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    8. Charles N. Noussair, 2017. "The Discovery of Bubbles and Crashes in Experimental Asset Markets and the Contribution of Vernon Smith to Experimental Finance," Southern Economic Journal, John Wiley & Sons, vol. 83(3), pages 644-648, January.
    9. Idris Adjerid & Sonam Samat & Alessandro Acquisti, 2016. "A Query-Theory Perspective of Privacy Decision Making," The Journal of Legal Studies, University of Chicago Press, vol. 45(S2), pages 97-121.
    10. Brice Corgnet & Camille Cornand & Nobuyuki Hanaki, 2021. "Emotional Markets: Competitive Arousal, Overbidding and Bubbles," Working Papers 2117, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    11. Gagnon, Nickolas & Noussair, C., 2016. "Does Reciprocity Persist Over Time?," Research Memorandum 033, Maastricht University, Graduate School of Business and Economics (GSBE).
    12. Domonkos F. Vamossy, 2023. "Social Media Emotions and IPO Returns," Papers 2306.12602, arXiv.org, revised Apr 2024.
    13. Curti, Filippo & Kazinnik, Sophia, 2023. "Let's face it: Quantifying the impact of nonverbal communication in FOMC press conferences," Journal of Monetary Economics, Elsevier, vol. 139(C), pages 110-126.
    14. Brice Corgnet & Camille Cornand & Nobuyuki Hanaki, 2021. "Risk-Taking and Tail Events Across Trading Institutions," Working Papers halshs-03357898, HAL.
    15. David J. Cooper & Ian Krajbich & Charles N. Noussair, 2019. "Choice-Process Data in Experimental Economics," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 1-13, August.
    16. Razen, Michael & Huber, Jürgen & Kirchler, Michael, 2017. "Cash inflow and trading horizon in asset markets," European Economic Review, Elsevier, vol. 92(C), pages 359-384.
    17. Loukas Balafoutas & Simon Czermak & Marc Eulerich & Helena Fornwagner, 2020. "Incentives For Dishonesty: An Experimental Study With Internal Auditors," Economic Inquiry, Western Economic Association International, vol. 58(2), pages 764-779, April.
    18. Brice Corgnet & Camille Cornand & Nobuyuki Hanaki, 2020. "Negative Tail Events, Emotions & Risk Taking," Working Papers 2016, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    19. Michael Razen & Jürgen Huber & Michael Kirchler, 2016. "Cash Inflow and Trading Horizon in Asset Markets," Working Papers 2016-06, Faculty of Economics and Statistics, Universität Innsbruck.
    20. Martin G Kocher & Konstantin E Lucks & David Schindler, 2019. "Unleashing Animal Spirits: Self-Control and Overpricing in Experimental Asset Markets," The Review of Financial Studies, Society for Financial Studies, vol. 32(6), pages 2149-2178.

    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:wut:journl:v:34:y:2024:i:2:p:85-107:id:6. 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: Adam Kasperski (email available below). General contact details of provider: https://edirc.repec.org/data/iopwrpl.html .

    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.