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Sentiment, emotions and stock market predictability in developed and emerging markets

Author

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  • Steyn, Dimitri H. W.
  • Greyling, Talita
  • Rossouw, Stephanie
  • Mwamba, John M.

Abstract

This paper investigates the predictability of stock market movements using text data extracted from the social media platform, Twitter. We analyse text data to determine the sentiment and the emotion embedded in the Tweets and use them as explanatory variables to predict stock market movements. The study contributes to the literature by analysing high-frequency data and comparing the results obtained from analysing emerging and developed markets, respectively. To this end, the study uses three different Machine Learning Classification Algorithms, the Naïve Bayes, K-Nearest Neighbours and the Support Vector Machine algorithm. Furthermore, we use several evaluation metrics such as the Precision, Recall, Specificity and the F-1 score to test and compare the performance of these algorithms. Lastly, we use the K-Fold Cross-Validation technique to validate the results of our machine learning models and the Variable Importance Analysis to show which variables play an important role in the prediction of our models. The predictability of the market movements is estimated by first including sentiment only and then sentiment with emotions. Our results indicate that investor sentiment and emotions derived from stock market-related Tweets are significant predictors of stock market movements, not only in developed markets but also in emerging markets.

Suggested Citation

  • Steyn, Dimitri H. W. & Greyling, Talita & Rossouw, Stephanie & Mwamba, John M., 2020. "Sentiment, emotions and stock market predictability in developed and emerging markets," GLO Discussion Paper Series 502, Global Labor Organization (GLO).
  • Handle: RePEc:zbw:glodps:502
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    References listed on IDEAS

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    5. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
    6. Broadstock, David C. & Zhang, Dayong, 2019. "Social-media and intraday stock returns: The pricing power of sentiment," Finance Research Letters, Elsevier, vol. 30(C), pages 116-123.
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    11. Shen, Dehua & Liu, Lanbiao & Zhang, Yongjie, 2018. "Quantifying the cross-sectional relationship between online sentiment and the skewness of stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 928-934.
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    Cited by:

    1. Rossouw, Stephanie & Greyling, Talita, 2020. "Big Data and Happiness," GLO Discussion Paper Series 634, Global Labor Organization (GLO).
    2. Greyling, Talita & Rossouw, Stephanie & Adhikari, Tamanna, 2020. "Happiness-lost: Did Governments make the right decisions to combat Covid-19?," GLO Discussion Paper Series 556, Global Labor Organization (GLO).

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    More about this item

    Keywords

    Sentiment Analysis; Classification; Stock Prediction; Machine Learning;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • G0 - Financial Economics - - General
    • G4 - Financial Economics - - Behavioral Finance

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