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Enhanced news sentiment analysis using deep learning methods

Author

Listed:
  • Wataru Souma

    (Nihon University)

  • Irena Vodenska

    (Boston University
    Boston University)

  • Hideaki Aoyama

    (Kyoto University
    Trade and Industry)

Abstract

We explore the predictive power of historical news sentiments based on financial market performance to forecast financial news sentiments. We define news sentiments based on stock price returns averaged over one minute right after a news article has been released. If the stock price exhibits positive (negative) return, we classify the news article released just prior to the observed stock return as positive (negative). We use Wikipedia and Gigaword five corpus articles from 2014 and we apply the global vectors for word representation method to this corpus to create word vectors to use as inputs into the deep learning TensorFlow network. We analyze high-frequency (intraday) Thompson Reuters News Archive as well as the high-frequency price tick history of the Dow Jones Industrial Average (DJIA 30) Index individual stocks for the period between 1/1/2003 and 12/30/2013. We apply a combination of deep learning methodologies of recurrent neural network with long short-term memory units to train the Thompson Reuters News Archive Data from 2003 to 2012, and we test the forecasting power of our method on 2013 News Archive data. We find that the forecasting accuracy of our methodology improves when we switch from random selection of positive and negative news to selecting the news with highest positive scores as positive news and news with highest negative scores as negative news to create our training data set.

Suggested Citation

  • Wataru Souma & Irena Vodenska & Hideaki Aoyama, 2019. "Enhanced news sentiment analysis using deep learning methods," Journal of Computational Social Science, Springer, vol. 2(1), pages 33-46, January.
    • Handle: RePEc:spr:jcsosc:v:2:y:2019:i:1:d:10.1007_s42001-019-00035-x
    DOI: 10.1007/s42001-019-00035-x
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    References listed on IDEAS

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    1. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    2. Luigi Troiano & Elena Mejuto & Pravesh Kriplani, 2017. "On Feature Reduction using Deep Learning for Trend Prediction in Finance," Papers 1704.03205, arXiv.org.
    3. Linda Ponta & Silvano Cincotti, 2018. "Traders’ Networks of Interactions and Structural Properties of Financial Markets: An Agent-Based Approach," Complexity, Hindawi, vol. 2018, pages 1-9, January.
    4. J. B. Heaton & N. G. Polson & J. H. Witte, 2017. "Deep learning for finance: deep portfolios," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(1), pages 3-12, January.
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    Cited by:

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    2. Ali Lashgari, 2023. "Assessing Text Mining and Technical Analyses on Forecasting Financial Time Series," Papers 2304.14544, arXiv.org.
    3. Amartya Chakraborty & Sunanda Bose, 2020. "Around the world in 60 days: an exploratory study of impact of COVID-19 on online global news sentiment," Journal of Computational Social Science, Springer, vol. 3(2), pages 367-400, November.
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    5. Yupeng Cao & Zhi Chen & Qingyun Pei & Fabrizio Dimino & Lorenzo Ausiello & Prashant Kumar & K. P. Subbalakshmi & Papa Momar Ndiaye, 2024. "RiskLabs: Predicting Financial Risk Using Large Language Model Based on Multi-Sources Data," Papers 2404.07452, arXiv.org.
    6. Zexin Hu & Yiqi Zhao & Matloob Khushi, 2021. "A Survey of Forex and Stock Price Prediction Using Deep Learning," Papers 2103.09750, arXiv.org.
    7. Aaryan Gupta & Vinya Dengre & Hamza Abubakar Kheruwala & Manan Shah, 2020. "Comprehensive review of text-mining applications in finance," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-25, December.
    8. Kao, Yu-Sheng & Day, Min-Yuh & Chou, Ke-Hsin, 2024. "A comparison of bitcoin futures return and return volatility based on news sentiment contemporaneously or lead-lag," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
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    10. Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023. "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics 2023_13, University of São Paulo (FEA-USP).
    11. Prajwal Eachempati & Praveen Ranjan Srivastava, 2021. "Accounting for unadjusted news sentiment for asset pricing," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 13(3), pages 383-422, May.
    12. Bledar Fazlija & Pedro Harder, 2022. "Using Financial News Sentiment for Stock Price Direction Prediction," Mathematics, MDPI, vol. 10(13), pages 1-20, June.
    13. Gianluca Anese & Marco Corazza & Michele Costola & Loriana Pelizzon, 2023. "Impact of public news sentiment on stock market index return and volatility," Computational Management Science, Springer, vol. 20(1), pages 1-36, December.
    14. Anna Ruelens, 2022. "Analyzing user-generated content using natural language processing: a case study of public satisfaction with healthcare systems," Journal of Computational Social Science, Springer, vol. 5(1), pages 731-749, May.

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