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The Impact of Investor Sentiment on Direction of Stock Price Changes: Evidence from the Polish Stock Market

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  • Kamil Polak

    (Kozminski University, Warsaw, Poland)

Abstract

The purpose of this research is to examine the impact of sentiment derived from news headlines on the direction of stock price changes. The study examines stocks listed on the WIG-banking sub-sector index on the Warsaw Stock Exchange. Two types of data were used: textual and market data. The research period covers the years 2015–2018. Through the research, 7,074 observations were investigated, of which 3,390 with positive sentiment, 2,665 neutral, and 1,019 negative. In order to examine the predictive power of sentiment, six machine learning models were used: Decision Tree Classifier, Random Forest Classifier, XGBoost Classifier, KNN Classifier, SVC and Gaussian Naive Bayes Classifier. Empirical results show that the sentiment of news headlines has no significant explanatory power for the direction of stock price changes in one-day time frame.

Suggested Citation

  • Kamil Polak, 2021. "The Impact of Investor Sentiment on Direction of Stock Price Changes: Evidence from the Polish Stock Market," Journal of Banking and Financial Economics, University of Warsaw, Faculty of Management, vol. 2(16), pages 72-90, December.
    • Handle: RePEc:sgm:jbfeuw:v:2:y:2022:i:16:p:72-90
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    File URL: https://jbfe.wz.uw.edu.pl/resources/html/article/details?id=227965
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    More about this item

    Keywords

    sentiment analysis; natural language processing; machine learning; financial forecasting; behavioral finance;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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