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Financial Sentiment on Twitter's Community and it's Connection to Polish Stock Market Movements in Context of Behavior Modelling

Author

Listed:
  • Eryka Probierz
  • Adam Galuszka
  • Katarzyna Klimczak
  • Karol Jedrasiak
  • Tomasz Wisniewski
  • Tomasz Dzida

Abstract

Purpose: The paper analyzes the relationship between tweets published on social media related to the stock market and stock market movements. The research conducted aims to find patterns that may account for behavioral modelling patterns between social media and the stock market. Design/Methodology/Approach: To conduct the research, 452,776 tweets containing a hashtag with the name of at least one company included in the WIG20 of the Polish stock market were analyzed. The data obtained also included information about the reach of the tweet and the popularity of the author. The analyzed text also contained a timestamp allowing the tweets to be linked to the behavior of the stock market. Additionally, the analysis implemented developed Polish financial sentiment analysis vocabularies allowing for better pattern retrieval. Findings: The obtained results indicate that among the analyzed data, three patterns related to the sentiment of statements accompanying a given company in social media can be distinguished. A consistent sentiment present in many statements with a wide range is decisive. When different sentiments are present, the presence of a pattern is not identifiable. The implementation of Polish financial dictionaries allowed to distinguish also individual words characterizing positive and negative sentiment. Practical Implications: The results obtained can form the basis for further, more in-depth analyses between sentiment published in social media and stock market movements. Originality value: Studies analyzing the relationship between the analysis of tweets and stock markets are conducted but analyses related to the Polish stock market are not a popular field of analysis. Due to the unique nature of each stock exchange, it is indicated to be innovative and to be able to emerge some patterns, useful in the context of investing.

Suggested Citation

  • Eryka Probierz & Adam Galuszka & Katarzyna Klimczak & Karol Jedrasiak & Tomasz Wisniewski & Tomasz Dzida, 2021. "Financial Sentiment on Twitter's Community and it's Connection to Polish Stock Market Movements in Context of Behavior Modelling," European Research Studies Journal, European Research Studies Journal, vol. 0(4B), pages 56-65.
    • Handle: RePEc:ers:journl:v:xxiv:y:2021:i:4b:p:56-65
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    References listed on IDEAS

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    1. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    2. Nicky J. Ferguson & Dennis Philip & Herbert Y. T. Lam & Jie Michael Guo, 2015. "Media Content and Stock Returns: The Predictive Power of Press," Multinational Finance Journal, Multinational Finance Journal, vol. 19(1), pages 1-31, March.
    3. Zachary McGurk & Adam Nowak & Joshua C. Hall, 2020. "Stock returns and investor sentiment: textual analysis and social media," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 44(3), pages 458-485, July.
    4. Angela K. Davis & Jeremy M. Piger & Lisa M. Sedor, 2012. "Beyond the Numbers: Measuring the Information Content of Earnings Press Release Language," Contemporary Accounting Research, John Wiley & Sons, vol. 29(3), pages 845-868, September.
    5. Price, S. McKay & Doran, James S. & Peterson, David R. & Bliss, Barbara A., 2012. "Earnings conference calls and stock returns: The incremental informativeness of textual tone," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 992-1011.
    6. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    7. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    8. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    9. Mark Johnman & Bruce James Vanstone & Adrian Gepp, 2018. "Predicting FTSE 100 returns and volatility using sentiment analysis," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 253-274, November.
    10. Nofer, Michael & Hinz, Oliver, 2015. "Using Twitter to Predict the Stock Market: Where is the Mood Effect?," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77140, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    11. Blau, Benjamin M. & DeLisle, Jared R. & Price, S. McKay, 2015. "Do sophisticated investors interpret earnings conference call tone differently than investors at large? Evidence from short sales," Journal of Corporate Finance, Elsevier, vol. 31(C), pages 203-219.
    12. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    13. Michael Nofer & Oliver Hinz, 2015. "Using Twitter to Predict the Stock Market," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 57(4), pages 229-242, August.
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    More about this item

    Keywords

    Sentiment analysis; stock market; twitter; social media; behavior modelling.;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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