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A comparative study on effect of news sentiment on stock price prediction with deep learning architecture

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Listed:
  • Keshab Raj Dahal
  • Nawa Raj Pokhrel
  • Santosh Gaire
  • Sharad Mahatara
  • Rajendra P Joshi
  • Ankrit Gupta
  • Huta R Banjade
  • Jeorge Joshi

Abstract

The accelerated progress in artificial intelligence encourages sophisticated deep learning methods in predicting stock prices. In the meantime, easy accessibility of the stock market in the palm of one’s hand has made its behavior more fuzzy, volatile, and complex than ever. The world is looking at an accurate and reliable model that uses text and numerical data which better represents the market’s highly volatile and non-linear behavior in a broader spectrum. A research gap exists in accurately predicting a target stock’s closing price utilizing the combined numerical and text data. This study uses long short-term memory (LSTM) and gated recurrent unit (GRU) to predict the stock price using stock features alone and incorporating financial news data in conjunction with stock features. The comparative study carried out under identical conditions dispassionately evaluates the importance of incorporating financial news in stock price prediction. Our experiment concludes that incorporating financial news data produces better prediction accuracy than using the stock fundamental features alone. The performances of the model architecture are compared using the standard assessment metrics —Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Correlation Coefficient (R). Furthermore, statistical tests are conducted to further verify the models’ robustness and reliability.

Suggested Citation

  • Keshab Raj Dahal & Nawa Raj Pokhrel & Santosh Gaire & Sharad Mahatara & Rajendra P Joshi & Ankrit Gupta & Huta R Banjade & Jeorge Joshi, 2023. "A comparative study on effect of news sentiment on stock price prediction with deep learning architecture," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-19, April.
    • Handle: RePEc:plo:pone00:0284695
    DOI: 10.1371/journal.pone.0284695
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    References listed on IDEAS

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    1. Ayodele Ariyo Adebiyi & Aderemi Oluyinka Adewumi & Charles Korede Ayo, 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    2. Peng, Lu & Wang, Lin & Xia, De & Gao, Qinglu, 2022. "Effective energy consumption forecasting using empirical wavelet transform and long short-term memory," Energy, Elsevier, vol. 238(PB).
    3. Ayodele Ariyo Adebiyi & Aderemi Oluyinka Adewumi & Charles Korede Ayo, 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-7, March.
    4. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
    5. Wu, Binrong & Wang, Lin & Wang, Sirui & Zeng, Yu-Rong, 2021. "Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic," Energy, Elsevier, vol. 226(C).
    6. Tej Bahadur Shahi & Ashish Shrestha & Arjun Neupane & William Guo, 2020. "Stock Price Forecasting with Deep Learning: A Comparative Study," Mathematics, MDPI, vol. 8(9), pages 1-15, August.
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    2. Abel Díaz Berenguer & Yifei Da & Matías Nicolás Bossa & Meshia Cédric Oveneke & Hichem Sahli, 2024. "Causality-driven multivariate stock movement forecasting," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-41, April.
    3. David Winkelmann & Christian Deutscher, 2025. "Do Betting Markets Sense a Goal Coming? Evidence from the German Bundesliga," Papers 2505.21275, arXiv.org.

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