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Daily Market News Sentiment and Stock Prices

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  • Allen, D.E.
  • McAleer, M.J.
  • Singh, A.K.

Abstract

In recent years there has been a tremendous growth in the influx of news related to traded assets in international financial markets. This financial news is now available via print media but also through real-time online sources such as internet news and social media sources. The increase in the availability of financial news and investor’s ease of access to it has a potentially significant impact on market price formation as these news items are swiftly transformed into investors sentiment which in turn drives prices. Various commercial agencies have started developing their own financial news data sets which are used by investors and traders to support their algorithmic trading strategies. Thomson Reuters News Analytics (TRNA)1 is one such data set. In this study we use the TRNA data set to construct a series of daily sentiment scores for Dow Jones Industrial Average (DJIA) stock index component companies. We use these daily DJIA market sentiment scores to study the influence of financial news sentiment scores on the stock prices of these companies using a multi-factor model. We use an augmented Fama French Three Factor Model to evaluate the additional effects of financial news sentiment on stock prices in the context of this model. Our results suggest that even when market factors are taken into account, sentiment scores have a significant effect on Dow Jones constituent company returns and that lagged daily sentiment scores are often significant, suggesting that information compounded in these scores is not immediately reflected in security prices and related return series.

Suggested Citation

  • Allen, D.E. & McAleer, M.J. & Singh, A.K., 2015. "Daily Market News Sentiment and Stock Prices," Econometric Institute Research Papers EI2015-23, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:78713
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    References listed on IDEAS

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    Cited by:

    1. Shahid Raza & Sun Baiqing & Pwint Kay-Khine & Muhammad Ali Kemal, 2023. "Uncovering the Effect of News Signals on Daily Stock Market Performance: An Econometric Analysis," IJFS, MDPI, vol. 11(3), pages 1-25, August.
    2. David E. Allen & Michael McAleer, 2019. "Fake News and Propaganda: Trump’s Democratic America and Hitler’s National Socialist (Nazi) Germany," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    3. Krystian M. Zawadzki & Marcin Potrykus, 2023. "Stock Markets’ Reactions to the Announcement of the Hosts. An Event Study in the Analysis of Large Sporting Events in the Years 1976–2032," Journal of Sports Economics, , vol. 24(6), pages 759-800, August.
    4. David E Allen & Michael McAleer & Abhay K Singh, 2017. "An entropy-based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series," Applied Economics, Taylor & Francis Journals, vol. 49(7), pages 677-692, February.
    5. Steven Buigut and Burcu Kapar, 2022. "Do COVID-19 Incidence and Government Intervention Influence Media Indices?," Bulletin of Applied Economics, Risk Market Journals, vol. 9(2), pages 79-100.
    6. Seok, Sangik & Cho, Hoon & Ryu, Doojin, 2022. "Scheduled macroeconomic news announcements and intraday market sentiment," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    7. Suchismita Mishra & Le Zhao, 2021. "Order Routing Decisions for a Fragmented Market: A Review," JRFM, MDPI, vol. 14(11), pages 1-32, November.
    8. Yao, Can-Zhong & Li, Hong-Yu, 2020. "Time-varying lead–lag structure between investor sentiment and stock market," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    9. Seok, Sang Ik & Cho, Hoon & Ryu, Doojin, 2021. "Stock Market’s responses to intraday investor sentiment," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    10. Zihan Dong & Xinyu Fan & Zhiyuan Peng, 2024. "FNSPID: A Comprehensive Financial News Dataset in Time Series," Papers 2402.06698, arXiv.org.
    11. David E. Allen & Michael McAleer & Abhay K. Singh, 2014. "Machine news and volatility: The Dow Jones Industrial Average and the TRNA sentiment series," Working Papers in Economics 14/04, University of Canterbury, Department of Economics and Finance.
    12. 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.
    13. Na, Haejung & Kim, Soonho, 2021. "Predicting stock prices based on informed traders’ activities using deep neural networks," Economics Letters, Elsevier, vol. 204(C).
    14. Chou, Ke-Hsin & Day, Min-Yuh & Chiu, Chien-Liang, 2023. "Do bitcoin news information flow and return volatility fit the sequential information arrival hypothesis and the mixture of distribution hypothesis?," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 365-385.
    15. Wang, Gaoshan & Yu, Guangjin & Shen, Xiaohong, 2021. "The effect of online environmental news on green industry stocks: The mediating role of investor sentiment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).

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    More about this item

    Keywords

    sentiment analysis; financial news; factor models; asset pricing;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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