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Daily Market News Sentiment and Stock Prices

Author

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  • David E. Allen

    (Centre for Applied Financial Studies, Adelaide, University of South Australia, and University of Sydney, Australia)

  • Michael McAleer

    (National Tsing Hua University, Taiwan, Erasmus University Rotterdam, the Netherlands, and Complutense University of Madrid, Spain)

  • Abhay K. Singh

    (Edith Cowan University, Perth, Australia)

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

  • David E. Allen & Michael McAleer & Abhay K. Singh, 2015. "Daily Market News Sentiment and Stock Prices," Tinbergen Institute Discussion Papers 15-090/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20150090
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    2. Tian Guo & Emmanuel Hauptmann, 2024. "Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow," Papers 2407.18103, arXiv.org, revised Aug 2024.
    3. Na, Haejung & Kim, Soonho, 2021. "Predicting stock prices based on informed traders’ activities using deep neural networks," Economics Letters, Elsevier, vol. 204(C).
    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. David E. Allen & Michael McAleer & Abhay K. Singh, 2014. "Machine news and volatility: The Dow Jones Industrial Average and the TRNA sentiment series," Documentos de Trabajo del ICAE 2014-02, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    10. 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.
    11. Cai, Yi & Tang, Zhenpeng & Chen, Ying, 2024. "Can real-time investor sentiment help predict the high-frequency stock returns? Evidence from a mixed-frequency-rolling decomposition forecasting method," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    12. 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.
    13. Seok, Sangik & Cho, Hoon & Ryu, Doojin, 2024. "Dual effects of investor sentiment and uncertainty in financial markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 300-315.
    14. 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).
    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).
    16. Anca Ioana, Iacob (Troto), 2021. "Investor Sentiment - Theoretical Aspects And Practical Conclusions, In The Context Of The Pandemic Crisis," Management Strategies Journal, Constantin Brancoveanu University, vol. 51(1), pages 122-128.
    17. 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.
    18. Suchismita Mishra & Le Zhao, 2021. "Order Routing Decisions for a Fragmented Market: A Review," JRFM, MDPI, vol. 14(11), pages 1-32, November.
    19. 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).
    20. Durand, Robert B. & Khuu, Joyce & Smales, Lee A., 2023. "Lost in translation. When sentiment metrics for one market are derived from two different languages," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    21. 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).
    22. Zihan Dong & Xinyu Fan & Zhiyuan Peng, 2024. "FNSPID: A Comprehensive Financial News Dataset in Time Series," Papers 2402.06698, arXiv.org.
    23. Kao, Yu-Sheng & Day, Min-Yuh & Chou, Ke-Hsin, 2024. "A comparison of bitcoin futures return and return volatility based on news sentiment contemporaneously or lead-lag," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).

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    More about this item

    Keywords

    Sentiment Analysis; Financial News; Factor Models; Asset Pricing;
    All these keywords.

    JEL classification:

    • 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
    • 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

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