IDEAS home Printed from https://ideas.repec.org/p/hig/wpaper/22-fe-2013.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://www.hse.ru/data/2013/12/19/1338956076/22FE2013.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Leighton Vaughan Williams & J. James Reade, 2016. "Prediction Markets, Social Media and Information Efficiency," Kyklos, Wiley Blackwell, vol. 69(3), pages 518-556, August.
    2. Leighton Vaughan Williams & James Reade, 2014. "Prediction Markets, Twitter and Bigotgate," Economics Discussion Papers em-dp2014-09, Department of Economics, University of Reading.
    3. 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.
    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hig:wpaper:22/fe/2013. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Shamil Abdulaev or Shamil Abdulaev (email available below). General contact details of provider: https://edirc.repec.org/data/hsecoru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.