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Using Financial News Sentiment for Stock Price Direction Prediction

Author

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  • Bledar Fazlija

    (School of Management and Law, ZHAW Zurich University of Applied Sciences, 8400 Winterthur, Switzerland)

  • Pedro Harder

    (School of Management and Law, ZHAW Zurich University of Applied Sciences, 8400 Winterthur, Switzerland)

Abstract

Using sentiment information in the analysis of financial markets has attracted much attention. Natural language processing methods can be used to extract market sentiment information from texts such as news articles. The objective of this paper is to extract financial market sentiment information from news articles and use the estimated sentiment scores to predict the price direction of the stock market index Standard & Poor’s 500. To achieve the best possible performance in sentiment classification, state-of-the-art bidirectional encoder representations from transformers (BERT) models are used. The pretrained transformer networks are fine-tuned on a labeled financial text dataset and applied to news articles from known providers of financial news content to predict their sentiment scores. The generated sentiment scores for the titles of the given news articles, for the (text) content of said news articles, and for the combined title-content consideration are posited against past time series information of the stock market index. To forecast the price direction of the stock market index, the predicted sentiment scores are used in a simple strategy and as features for a random forest classifier. The results show that sentiment scores based on news content are particularly useful for stock price direction prediction.

Suggested Citation

  • Bledar Fazlija & Pedro Harder, 2022. "Using Financial News Sentiment for Stock Price Direction Prediction," Mathematics, MDPI, vol. 10(13), pages 1-20, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2156-:d:843797
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    References listed on IDEAS

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    Cited by:

    1. Costola, Michele & Hinz, Oliver & Nofer, Michael & Pelizzon, Loriana, 2023. "Machine learning sentiment analysis, COVID-19 news and stock market reactions," Research in International Business and Finance, Elsevier, vol. 64(C).
    2. Felix Drinkall & Janet B. Pierrehumbert & Stefan Zohren, 2025. "When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Tasks," Papers 2502.02199, arXiv.org.
    3. Yinheng Li & Shaofei Wang & Han Ding & Hang Chen, 2023. "Large Language Models in Finance: A Survey," Papers 2311.10723, arXiv.org, revised Jul 2024.
    4. Yujia Hu, 2023. "A Heuristic Approach to Forecasting and Selection of a Portfolio with Extra High Dimensions," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    5. Denisa Millo & Blerina Vika & Nevila Baci, 2024. "Integrating Natural Language Processing Techniques of Text Mining Into Financial System: Applications and Limitations," Papers 2412.20438, arXiv.org.

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