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A Hybrid Approach for Stock Market Prediction Using Financial News and Stocktwits

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 12880)


Stock market prediction is a difficult problem that has always attracted researchers from different domains. Recently, different studies using text mining and machine learning methods were proposed. However, the efficiency of these methods is still highly dependant on the retrieval of relevant information. In this paper, we investigate novel data sources (Stocktwits in combination with financial news) and we tackle the problem as a binary classification task (i.e., stock prices moving up or down). Furthermore, we use for that end a hybrid approach which consists of sentiment and event-based features. We find that the use of Stocktwits data systematically outperforms the sole use of price data to predict the close prices of 8 companies from the NASDAQ100. We conclude on what the limits of these novel data sources are and how they could be further investigated.


  • Stock market
  • Sentiment analysis
  • Online news
  • Stocktwits
  • Classification

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  • DOI: 10.1007/978-3-030-85251-1_2
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    Vader (, Textblob (

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    FIBO: The Financial Industry Business Ontology.


  1. Nassirtoussi, A.K., Aghabozorgi, S., Wah, T.Y., Ngo, D.C.L.: Text mining for market prediction: a systematic review. Expert Syst. Appl. 41(16), 7653–7670 (2014)

    CrossRef  Google Scholar 

  2. Hur, J., Raj, M., Riyanto, Y.E.: Finance and trade: a cross-country empirical analysis on the impact of financial development and asset tangibility on international trade. World Dev. 34(10), 1728–1741 (2006)

    CrossRef  Google Scholar 

  3. Fung, G.P.C., Yu, J.X., Lam, W.: News sensitive stock trend prediction. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 481–493. Springer, Heidelberg (2002).

    CrossRef  Google Scholar 

  4. Fama, E.F.: Efficient market hypothesis. Dissertation Ph.D. thesis, Ph.D. dissertation (1960)

    Google Scholar 

  5. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  6. Yasef Kaya, M.I., Elif Karsligil, M.: Stock price prediction using financial news articles. In: 2010 2nd IEEE International Conference on Information and Financial Engineering, pp. 478–482. IEEE (2010)

    Google Scholar 

  7. Dang, M., Duong, D.: Improvement methods for stock market prediction using financial news articles. In: 2016 3rd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS), pp. 125–129. IEEE (2016)

    Google Scholar 

  8. Mohan, S., Mullapudi, S., Sammeta, S., Vijayvergia, P., Anastasiu, D.C.: Stock price prediction using news sentiment analysis. In: 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), pp. 205–208. IEEE (2019)

    Google Scholar 

  9. Agarwal, A.: Sentiment analysis of financial news. In: 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 312–315. IEEE (2020)

    Google Scholar 

  10. Hutto, C., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8 (2014)

    Google Scholar 

  11. Sohangir, S., Petty, N., Wang, D.: Financial sentiment lexicon analysis. In: 2018 IEEE 12th International Conference on Semantic Computing (ICSC), pp. 286–289. IEEE (2018)

    Google Scholar 

  12. Consoli, S., Barbaglia, L., Manzan, S.: Fine-grained, aspect-based sentiment analysis on economic and financial lexicon. Aspect-Based Sentiment Analysis on Economic and Financial Lexicon (January 14, 2021) (2021)

    Google Scholar 

  13. Barbagliaa, L., Consolia, S., Manzanb, S.: Forecasting with economic news. Available at SSRN (2020)

    Google Scholar 

  14. Barbaglia, L., Consoli, S., Manzan, S.: Monitoring the business cycle with fine-grained, aspect-based sentiment extraction from news. In: Bitetta, V., Bordino, I., Ferretti, A., Gullo, F., Pascolutti, S., Ponti, G. (eds.) MIDAS 2019. LNCS (LNAI), vol. 11985, pp. 101–106. Springer, Cham (2020).

    CrossRef  Google Scholar 

  15. Feldman, R., Rosenfeld, B., Bar-Haim, R., Fresko, M.: The stock sonar—sentiment analysis of stocks based on a hybrid approach. In: Twenty-Third IAAI Conference (2011)

    Google Scholar 

  16. Han, S., Hao, X., Huang, H.: An event-extraction approach for business analysis from online Chinese news. Electron. Commer. Res. Appl. 28, 244–260 (2018)

    CrossRef  Google Scholar 

  17. Shao, S., Stoumbos, R., Frank Zhang, X.: The power of firm fundamental information in explaining stock returns. Rev. Account. Stud. 1–41 (2021)

    Google Scholar 

  18. Boudoukh, J., Feldman, R., Kogan, S., Richardson, M.: Information, trading, and volatility: evidence from firm-specific news. Rev. Finan. Stud. 32(3), 992–1033 (2019)

    CrossRef  Google Scholar 

  19. Peng, Y., Jiang, H.: Leverage financial news to predict stock price movements using word embeddings and deep neural networks. arXiv preprint arXiv:1506.07220 (2015)

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Correspondence to Alaa Alhamzeh .

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Alhamzeh, A. et al. (2021). A Hybrid Approach for Stock Market Prediction Using Financial News and Stocktwits. In: , et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2021. Lecture Notes in Computer Science(), vol 12880. Springer, Cham.

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  • Print ISBN: 978-3-030-85250-4

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