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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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Huicheng Liu, 2018. "Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network," Papers 1811.06173, arXiv.org.
    4. 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.
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