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Enhancing Financial Market Analysis and Prediction with Emotion Corpora and News Co-Occurrence Network

Author

Listed:
  • Shawn McCarthy

    (Department of Computer Science and Engineering, University of Colorado Denver, Denver, CO 80204, USA)

  • Gita Alaghband

    (Department of Computer Science and Engineering, University of Colorado Denver, Denver, CO 80204, USA)

Abstract

This study employs an improved natural language processing algorithm to analyze over 500,000 financial news articles from sixteen major sources across 12 sectors, with the top 10 companies in each sector. The analysis identifies shifting economic activity based on emotional news sentiment and develops a news co-occurrence network to show relationships between companies even across sectors. This study created an improved corpus and algorithm to identify emotions in financial news. The improved method identified 18 additional emotions beyond what was previously analyzed. The researchers labeled financial terms from Investopedia to validate the categorization performance of the new method. Using the improved algorithm, we analyzed how emotions in financial news relate to market movement of pairs of companies. We found a moderate correlation (above 60%) between emotion sentiment and market movement. To validate this finding, we further checked the correlation coefficients between sentiment alone, and found that consumer discretionary, consumer staples, financials, industrials, and technology sectors showed similar trends. Our findings suggest that emotional sentiment analysis provide valuable insights for financial market analysis and prediction. The technical analysis framework developed in this study can be integrated into a larger investment strategy, enabling organizations to identify potential opportunities and develop informed strategies. The insights derived from the co-occurrence model may be leveraged by companies to strengthen their risk management functions, making it an asset within a comprehensive investment strategy.

Suggested Citation

  • Shawn McCarthy & Gita Alaghband, 2023. "Enhancing Financial Market Analysis and Prediction with Emotion Corpora and News Co-Occurrence Network," JRFM, MDPI, vol. 16(4), pages 1-19, April.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:4:p:226-:d:1115702
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    References listed on IDEAS

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    1. Suparna Dhar & Indranil Bose, 2020. "Emotions in Twitter communication and stock prices of firms: the impact of Covid-19 pandemic," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 385-399, December.
    2. Yi Tang & Yilu Zhou & Marshall Hong, 2019. "News Co-Occurrences, Stock Return Correlations, and Portfolio Construction Implications," JRFM, MDPI, vol. 12(1), pages 1-21, March.
    3. Shapiro, Adam Hale & Sudhof, Moritz & Wilson, Daniel J., 2022. "Measuring news sentiment," Journal of Econometrics, Elsevier, vol. 228(2), pages 221-243.
    4. Xingchen Wan & Jie Yang & Slavi Marinov & Jan-Peter Calliess & Stefan Zohren & Xiaowen Dong, 2020. "Sentiment Correlation in Financial News Networks and Associated Market Movements," Papers 2011.06430, arXiv.org, revised Feb 2021.
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    Cited by:

    1. Shawn McCarthy & Gita Alaghband, 2024. "Fin-ALICE: Artificial Linguistic Intelligence Causal Econometrics," JRFM, MDPI, vol. 17(12), pages 1-21, November.

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