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Improving prediction of stock market indices by analyzing the psychological states of twitter users

Author

Listed:
  • Alexander Porshnev

    (National Research University Higher School of Economics, Social Science department, N.Novgorod, Russia)

  • Ilya Redkin

    (National Research University Higher School of Economics, Business Informatics faculty, N.Novgorod, Russia)

  • Alexey Shevchenko

    (National Research University Higher School of Economics, Business Informatics faculty, N.Novgorod, Russia)

Abstract

In our paper, we analyze the possibility of improving the prediction of stock market indicators by conducting a sentiment analysis of Twitter posts. We use a dictionary-based approach for sentiment analysis, which allows us to distinguish eight basic emotions in the tweets of users. We compare the results of applying the Support Vector Machine algorithm trained on three sets of data: historical data, historical and “Worry”, “Fear”, “Hope” words count data, historical data and data on the present eight categories of emotions. Our results suggest that the Twitter sentiment analysis data provides additional information and improves prediction as compared to a model based solely on information on previous shifts in stock indicators.

Suggested Citation

  • Alexander Porshnev & Ilya Redkin & Alexey Shevchenko, 2013. "Improving prediction of stock market indices by analyzing the psychological states of twitter users," HSE Working papers WP BRP 22/FE/2013, National Research University Higher School of Economics.
    • Handle: RePEc:hig:wpaper:22/fe/2013
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    File URL: http://www.hse.ru/data/2013/12/19/1338956076/22FE2013.pdf
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    Citations

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    Cited by:

    1. Porshnev, Alexander V. & Lakshina, Valeriya V. & Redkin, Ilya E., 2016. "Using Emotional Markers' Frequencies in Stock Market ARMAX-GARCH Model," MPRA Paper 82875, University Library of Munich, Germany.
    2. Leighton Vaughan Williams & J. James Reade, 2016. "Prediction Markets, Social Media and Information Efficiency," Kyklos, Wiley Blackwell, vol. 69(3), pages 518-556, August.
    3. Leighton Vaughan Williams & James Reade, 2014. "Prediction Markets, Twitter and Bigotgate," Economics Discussion Papers em-dp2014-09, Department of Economics, University of Reading.
    4. Alexander Porshnev & Valeria Lakshina & Ilya Redkin, 2016. "Could Emotional Markers in Twitter Posts Add Information to the Stock Market Armax-Garch Model," HSE Working papers WP BRP 54/FE/2016, National Research University Higher School of Economics.

    More about this item

    Keywords

    stock market; forecast; Twitter; mood; psychological states; Support Vectors Machine; machine learning/;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles

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