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    Market trend prediction using sentiment analysis: lessons learned and paths forward

    Mudinas, Andrius and Zhang, Dell and Levene, Mark (2018) Market trend prediction using sentiment analysis: lessons learned and paths forward. In: Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM'18), 10 Aug 2018, London, UK.

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    Financial market forecasting is one of the most attractive practical applications of sentiment analysis. In this paper, we investigate the potential of using sentiment attitudes (positive vs negative) and also sentiment emotions (joy, sadness, etc.) extracted from financial news or tweets to help predict stock price movements. Our extensive experiments using the Granger-causality test have revealed that (i) in general sentiment attitudes do not seem to Granger-cause stock price changes; and (ii) while on some specific occasions sentiment emotions do seem to Granger-cause stock price changes, the exhibited pattern is not universal and must be looked at on a case by case basis. Furthermore, it has been observed that at least for certain stocks, integrating sentiment emotions as additional features into the machine learning based market trend prediction model could improve its accuracy.


    Item Type: Conference or Workshop Item (Paper)
    Keyword(s) / Subject(s): sentiment analysis, market trend prediction, causality analysis
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centres and Institutes: Birkbeck Knowledge Lab, Data Analytics, Birkbeck Institute for
    Depositing User: Dell Zhang
    Date Deposited: 26 Sep 2018 10:51
    Last Modified: 04 Jul 2022 12:30


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