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Analyzing the correlation between online texts and stock price movements at micro-level using machine learning

Author

Listed:
  • Frantisek Darena

    (Department of Informatics, Faculty of Business and Economics, Mendel University in Brno, Zemedelska 1, 613 00 Brno, Czech Republic)

  • Jonas Petrovsky

    (Department of Informatics, Faculty of Business and Economics, Mendel University in Brno, Zemedelska 1, 613 00 Brno, Czech Republic)

  • Jan Zizka

    (Department of Informatics, Faculty of Business and Economics, Mendel University in Brno, Zemedelska 1, 613 00 Brno, Czech Republic)

  • Jan Prichystal

    (Department of Informatics, Faculty of Business and Economics, Mendel University in Brno, Zemedelska 1, 613 00 Brno, Czech Republic)

Abstract

The paper presents the result of experiments that were designed with the goal of revealing the correlation between texts published in online environments (Yahoo! Finance, Facebook, and Twitter) and changes in stock prices of the corresponding companies at a micro level. The association between lexicon detected sentiment and stock prices movements was not confirmed. It was, however, possible to reveal a dependence with application of machine learning based classification. From the experiments it was obvious that the data preparation procedure had a substantial impact on the results. Thus, different stock prices smoothing, lags between documents’ release and related stock price changes, five levels of a minimal stock price change, three different weighting schemes for structured document representation, and six classifiers were studied. It has been shown that at least a part of stock price movements is associated to the texts with a proper combination of these parameters.

Suggested Citation

  • Frantisek Darena & Jonas Petrovsky & Jan Zizka & Jan Prichystal, 2016. "Analyzing the correlation between online texts and stock price movements at micro-level using machine learning," MENDELU Working Papers in Business and Economics 2016-67, Mendel University in Brno, Faculty of Business and Economics.
    • Handle: RePEc:men:wpaper:67_2016
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    References listed on IDEAS

    as
    1. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    2. Gottschlich, Jörg & Hinz, Oliver, 2014. "A Decision Support System for Stock Investment Recommendations Using Collective Wisdom," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 69939, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. Kearney, Colm & Liu, Sha, 2014. "Textual sentiment in finance: A survey of methods and models," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 171-185.
    4. Clifford S. Ang, 2015. "Analyzing Financial Data and Implementing Financial Models Using R," Springer Texts in Business and Economics, Springer, edition 127, number 978-3-319-14075-9, August.
    5. Feng Li, 2010. "The Information Content of Forward‐Looking Statements in Corporate Filings—A Naïve Bayesian Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 48(5), pages 1049-1102, December.
    6. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    7. Bukovina, Jaroslav, 2016. "Social media big data and capital markets—An overview," Journal of Behavioral and Experimental Finance, Elsevier, vol. 11(C), pages 18-26.
    8. Blau, Benjamin M. & Griffith, Todd G., 2016. "Price clustering and the stability of stock prices," Journal of Business Research, Elsevier, vol. 69(10), pages 3933-3942.
    9. Felix Ming Fai Wong & Zhenming Liu & Mung Chiang, 2014. "Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization," Papers 1406.7330, arXiv.org.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Stock price movements; machine learning; classification; textual documents; sentiment;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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