IDEAS home Printed from https://ideas.repec.org/a/vrs/ecobur/v11y2025i2p39-65n1002.html

What makes stocks sensitive to investor sentiment: An analysis based on Google Trends

Author

Listed:
  • Qureshi Adeel Ali

    (Department of Corporate Finance, Poznań University of Economics and Business, al. Niepodległości 10, 61-875 Poznań, Poland)

Abstract

We capture Google’s vast search volume through Google Trends to generate a weekly investor sentiment index (2018–2022) using the most popular keywords (extracted from Google Search) from a keywords collection of 92,000+ words found in business, finance, and common language dictionaries. The results show that Google Trends is an efficient measure of investor sentiment as reflected in relative trading volume. To check what makes stocks sensitive to investor sentiment, 500 randomly selected US firms from various industries are categorised by firm characteristics. We generate two sub-portfolios: large, old, profitable, and dividend-yielding firms versus small, young, unprofitable, and non-dividend-yielding firms—and find the relative trading volume of the latter to be more sensitive to investor sentiment. Our results remain robust when control and auto regressive variables are introduced, in addition to when an alternative measure of sentiment is used, thereby confirming our primary findings.

Suggested Citation

  • Qureshi Adeel Ali, 2025. "What makes stocks sensitive to investor sentiment: An analysis based on Google Trends," Economics and Business Review, Sciendo, vol. 11(2), pages 39-65.
    • Handle: RePEc:vrs:ecobur:v:11:y:2025:i:2:p:39-65:n:1002
    DOI: 10.18559/ebr.2025.2.1790
    as

    Download full text from publisher

    File URL: https://doi.org/10.18559/ebr.2025.2.1790
    Download Restriction: no
    File URL: https://libkey.io/10.18559/ebr.2025.2.1790?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    2. Aboody, David & Even-Tov, Omri & Lehavy, Reuven & Trueman, Brett, 2018. "Overnight Returns and Firm-Specific Investor Sentiment," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(2), pages 485-505, April.
    3. Smales, L.A., 2021. "Investor attention and global market returns during the COVID-19 crisis," International Review of Financial Analysis, Elsevier, vol. 73(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yongqiang Meng & Dehua Shen & Xiong Xiong & Jørgen Vitting Andersen, 2020. "A Socio-Finance Model: The Case of Bitcoin," Post-Print halshs-03048777, HAL.
    2. John Garcia, 2021. "Analyst herding and firm-level investor sentiment," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(4), pages 461-494, December.
    3. Chen, Haozhi & Zhang, Yue, 2023. "Research on the effect of firm-specific investor sentiment on the idiosyncratic volatility anomaly: Evidence from the Chinese market," Pacific-Basin Finance Journal, Elsevier, vol. 81(C).
    4. Bashir, Hajam Abid & Kumar, Dilip, 2025. "Unveiling investor sentiment, attention, and speed of price adjustment in Indian market," International Review of Economics & Finance, Elsevier, vol. 101(C).
    5. Papadamou, Stephanos & Fassas, Athanasios P. & Kenourgios, Dimitris & Dimitriou, Dimitrios, 2023. "Effects of the first wave of COVID-19 pandemic on implied stock market volatility: International evidence using a google trend measure," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    6. Zhou, Liyun & Yang, Chunpeng, 2019. "Stochastic investor sentiment, crowdedness and deviation of asset prices from fundamentals," Economic Modelling, Elsevier, vol. 79(C), pages 130-140.
    7. Xiong, Xiong & Meng, Yongqiang & Li, Xiao & Shen, Dehua, 2020. "Can overnight return really serve as a proxy for firm-specific investor sentiment? Cross-country evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 64(C).
    8. Zhang, Hang & Tsai, Wei-Che & Weng, Pei-Shih & Tsai, Pin-Chieh, 2023. "Overnight returns and investor sentiment: Further evidence from the Taiwan stock market," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    9. Yang Gao & Chengjie Zhao & Bianxia Sun & Wandi Zhao, 2022. "Effects of investor sentiment on stock volatility: new evidences from multi-source data in China’s green stock markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-30, December.
    10. Ahmed El Oubani, 2024. "Investor sentiment and sustainable investment: evidence from North African stock markets," Future Business Journal, Springer, vol. 10(1), pages 1-20, December.
    11. Yarovaya, Larisa & Brzeszczyński, Janusz & Goodell, John W. & Lucey, Brian & Lau, Chi Keung Marco, 2022. "Rethinking financial contagion: Information transmission mechanism during the COVID-19 pandemic," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    12. Ballinari, Daniele & Audrino, Francesco & Sigrist, Fabio, 2022. "When does attention matter? The effect of investor attention on stock market volatility around news releases," International Review of Financial Analysis, Elsevier, vol. 82(C).
    13. Ham, Hyuna & Ryu, Doojin & Webb, Robert I., 2022. "The effects of overnight events on daytime trading sessions," International Review of Financial Analysis, Elsevier, vol. 83(C).
    14. Mahmoudi, Nader & Docherty, Paul & Melia, Adrian, 2022. "Firm-level investor sentiment and corporate announcement returns," Journal of Banking & Finance, Elsevier, vol. 144(C).
    15. Xiong, Xiong & Meng, Yongqiang & Joseph, Nathan Lael & Shen, Dehua, 2020. "Stock mispricing, hard-to-value stocks and the influence of internet stock message boards," International Review of Financial Analysis, Elsevier, vol. 72(C).
    16. Lan, Yueqin & Huang, Yong & Yan, Chao, 2021. "Investor sentiment and stock price: Empirical evidence from Chinese SEOs," Economic Modelling, Elsevier, vol. 94(C), pages 703-714.
    17. Seok, Sang Ik & Cho, Hoon & Ryu, Doojin, 2019. "Firm-specific investor sentiment and daily stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    18. Bouteska, Ahmed, 2019. "The effect of investor sentiment on market reactions to financial earnings restatements: Lessons from the United States," Journal of Behavioral and Experimental Finance, Elsevier, vol. 24(C).
    19. Piñeiro-Chousa, Juan & López-Cabarcos, M. Ángeles & Quiñoá-Piñeiro, Lara & Pérez-Pico, Ada M., 2022. "US biopharmaceutical companies' stock market reaction to the COVID-19 pandemic. Understanding the concept of the ‘paradoxical spiral’ from a sustainability perspective," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    20. Fahmy, Hany, 2025. "Empty pledges and powerless conventions: How transition climate risks are disrupting financial markets?," International Review of Financial Analysis, Elsevier, vol. 105(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G40 - Financial Economics - - Behavioral Finance - - - General
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:ecobur:v:11:y:2025:i:2:p:39-65:n:1002. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.