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Sentiment Correlation in Financial News Networks and Associated Market Movements

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  • Xingchen Wan
  • Jie Yang
  • Slavi Marinov
  • Jan-Peter Calliess
  • Stefan Zohren
  • Xiaowen Dong

Abstract

In an increasingly connected global market, news sentiment towards one company may not only indicate its own market performance, but can also be associated with a broader movement on the sentiment and performance of other companies from the same or even different sectors. In this paper, we apply NLP techniques to understand news sentiment of 87 companies among the most reported on Reuters for a period of seven years. We investigate the propagation of such sentiment in company networks and evaluate the associated market movements in terms of stock price and volatility. Our results suggest that, in certain sectors, strong media sentiment towards one company may indicate a significant change in media sentiment towards related companies measured as neighbours in a financial network constructed from news co-occurrence. Furthermore, there exists a weak but statistically significant association between strong media sentiment and abnormal market return as well as volatility. Such an association is more significant at the level of individual companies, but nevertheless remains visible at the level of sectors or groups of companies.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2011.06430
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    References listed on IDEAS

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    Cited by:

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    3. 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.
    4. Edward Turner, 2021. "Graph Auto-Encoders for Financial Clustering," Papers 2111.13519, arXiv.org, revised Dec 2021.
    5. Yoontae Hwang & Stefan Zohren & Yongjae Lee, 2024. "Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management," Papers 2407.13751, arXiv.org.
    6. Gianluca Anese & Marco Corazza & Michele Costola & Loriana Pelizzon, 2023. "Impact of public news sentiment on stock market index return and volatility," Computational Management Science, Springer, vol. 20(1), pages 1-36, December.
    7. Bo Yan & Mengru Liang & Yinxin Zhao, 2024. "Market sentiment and price dynamics in weak markets: A comprehensive empirical analysis of the soybean meal option market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(5), pages 744-766, May.
    8. Philipp Wirth & Francesca Medda & Thomas Schroder, 2024. "Longitudinal market structure detection using a dynamic modularity-spectral algorithm," Papers 2407.04500, arXiv.org.
    9. Rian Dolphin & Barry Smyth & Ruihai Dong, 2022. "A Multimodal Embedding-Based Approach to Industry Classification in Financial Markets," Papers 2211.06378, arXiv.org.
    10. Joshua Eklund & Jong‐Min Kim, 2024. "Forecasting Consumer Price Index with Federal Open Market Committee Sentiment Index," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1795-1813, September.
    11. Fabian Billert & Stefan Conrad, 2024. "A Framework for the Construction of a Sentiment-Driven Performance Index: The Case of DAX40," Papers 2409.20397, arXiv.org.
    12. Qinkai Chen, 2021. "Stock Movement Prediction with Financial News using Contextualized Embedding from BERT," Papers 2107.08721, arXiv.org.
    13. 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).
    14. Xiaohong Shen & Gaoshan Wang & Yue Wang & Alfred Peris, 2021. "The Influence of Research Reports on Stock Returns: The Mediating Effect of Machine-Learning-Based Investor Sentiment," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-14, December.
    15. Shangyang Mou & Qiang Xue & Xunquan Chen & Jinhui Chen & Ryoichi Takashima & Tetsuya Takiguchi & Yasuo Ariki, 2025. "Prefix tuning with prompt augmentation for efficient financial news summarization," Journal of Computational Social Science, Springer, vol. 8(1), pages 1-16, February.
    16. Dragos Gorduza & Xiaowen Dong & Stefan Zohren, 2022. "Understanding stock market instability via graph auto-encoders," Papers 2212.04974, arXiv.org.
    17. Semen Budennyy & Alexey Kazakov & Elizaveta Kovtun & Leonid Zhukov, 2022. "New drugs and stock market: how to predict pharma market reaction to clinical trial announcements," Papers 2208.07248, arXiv.org, revised Aug 2022.
    18. Kingstone Nyakurukwa & Yudhvir Seetharam, 2025. "Investor sentiment networks: mapping connectedness in DJIA stocks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-19, December.
    19. Aurthur Vimalachandran Thomas Jayachandran, 2022. "The financial crash of 2020 and the retail trader’s boon: a correlation between sentiment and technical analysis," SN Business & Economics, Springer, vol. 2(6), pages 1-8, June.

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