IDEAS home Printed from https://ideas.repec.org/a/spr/fininn/v10y2024i1d10.1186_s40854-023-00606-y.html
   My bibliography  Save this article

Google search volume index and investor attention in stock market: a systematic review

Author

Listed:
  • María José Ayala

    (Catholic University of Murcia)

  • Nicolás Gonzálvez-Gallego

    (Catholic University of Murcia)

  • Rocío Arteaga-Sánchez

    (University of Seville)

Abstract

This study systematically reviewed the literature on using the Google Search Volume Index (GSVI) as a proxy variable for investor attention and stock market movements. We analyzed 56 academic studies published between 2010 and 2021 using the Web of Sciences and ScienceDirect databases. The articles were classified and synthesized based on the selection criteria for building the GSVI: keywords of the search term, market region, and frequency of the data sample. Next, we analyze the effect of returns, volatility, and trading volume on the financial variables. The main results can be summarized as follows. (1) The GSVI is positively related to volatility and trading volume regardless of the keyword, market region, or frequency used for the sample. Hence, increasing investor attention toward a specific financial term will increase volatility and trading volume. (2) The GSVI can improve forecasting models for stock market movements. To conclude, this study consolidates, for the first time, the research literature on GSVI, which is highly valuable for academic practitioners in the area.

Suggested Citation

  • María José Ayala & Nicolás Gonzálvez-Gallego & Rocío Arteaga-Sánchez, 2024. "Google search volume index and investor attention in stock market: a systematic review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-29, December.
    • Handle: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-023-00606-y
    DOI: 10.1186/s40854-023-00606-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40854-023-00606-y
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1186/s40854-023-00606-y?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. Bazán-Palomino, Walter & Svogun, Daniel, 2023. "On the drivers of technical analysis profits in cryptocurrency markets: A Distributed Lag approach," International Review of Financial Analysis, Elsevier, vol. 86(C).
    2. Hamid, Alain & Heiden, Moritz, 2015. "Forecasting volatility with empirical similarity and Google Trends," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 62-81.
    3. Basistha, Arabinda & Kurov, Alexander & Wolfe, Marketa Halova, 2019. "Volatility Forecasting: The Role of Internet Search Activity and Implied Volatility," MPRA Paper 111037, University Library of Munich, Germany.
    4. Aouadi, Amal & Arouri, Mohamed & Teulon, Frédéric, 2013. "Investor attention and stock market activity: Evidence from France," Economic Modelling, Elsevier, vol. 35(C), pages 674-681.
    5. Goddard, John & Kita, Arben & Wang, Qingwei, 2015. "Investor attention and FX market volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 79-96.
    6. Tang, Wenbin & Zhu, Lili, 2017. "How security prices respond to a surge in investor attention: Evidence from Google Search of ADRs," Global Finance Journal, Elsevier, vol. 33(C), pages 38-50.
    7. Eli Arditi & Eldad Yechiam & Gal Zahavi, 2015. "Association between Stock Market Gains and Losses and Google Searches," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-12, October.
    8. Zhou, Haonan & Lu, Xinjie, 2023. "Investor attention on the Russia-Ukraine conflict and stock market volatility: Evidence from China," Finance Research Letters, Elsevier, vol. 52(C).
    9. Ekinci, Cumhur & Bulut, Ali Eray, 2021. "Google search and stock returns: A study on BIST 100 stocks," Global Finance Journal, Elsevier, vol. 47(C).
    10. Marcelo S. Perlin & João F. Caldeira & André A. P. Santos & Martin Pontuschka, 2017. "Can We Predict the Financial Markets Based on Google's Search Queries?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(4), pages 454-467, July.
    11. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    12. Tihana Škrinjarić, 2019. "Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets," IJFS, MDPI, vol. 7(4), pages 1-30, October.
    13. Tao Chen, 2017. "Investor Attention and Global Stock Returns," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 18(3), pages 358-372, July.
    14. Mondria, Jordi & Wu, Thomas & Zhang, Yi, 2010. "The determinants of international investment and attention allocation: Using internet search query data," Journal of International Economics, Elsevier, vol. 82(1), pages 85-95, September.
    15. Maedeh Tajmazinani & Hossein Hassani & Reza Raei & Saeed Rouhani, 2022. "Modeling Stock Price Movements Prediction Based On News Sentiment Analysis And Deep Learning," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 1-19, March.
    16. Xi Zhang & Yunjia Zhang & Senzhang Wang & Yuntao Yao & Binxing Fang & Philip S. Yu, 2018. "Improving Stock Market Prediction via Heterogeneous Information Fusion," Papers 1801.00588, arXiv.org.
    17. Vozlyublennaia, Nadia, 2014. "Investor attention, index performance, and return predictability," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 17-35.
    18. Erik J. Mayer, 2021. "Advertising, investor attention, and stock prices: Evidence from a natural experiment," Financial Management, Financial Management Association International, vol. 50(1), pages 281-314, March.
    19. Michael S. Drake & Jared Jennings & Darren T. Roulstone & Jacob R. Thornock, 2017. "The Comovement of Investor Attention," Management Science, INFORMS, vol. 63(9), pages 2847-2867, September.
    20. Lobão, Júlio & Pacheco, Luís & Pereira, Carlos, 2017. "The Use of the Recognition Heuristic as an Investment Strategy in European Stockmarkets," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 22(43), pages 207-223.
    21. Sifat, Imtiaz Mohammad & Thaker, Hassanudin Mohd Thas, 2020. "Predictive power of web search behavior in five ASEAN stock markets," Research in International Business and Finance, Elsevier, vol. 52(C).
    22. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    23. Adachi, Yuta & Masuda, Motoki & Takeda, Fumiko, 2017. "Google search intensity and its relationship to the returns and liquidity of Japanese startup stocks," Pacific-Basin Finance Journal, Elsevier, vol. 46(PB), pages 243-257.
    24. Li, Jun & Yu, Jianfeng, 2012. "Investor attention, psychological anchors, and stock return predictability," Journal of Financial Economics, Elsevier, vol. 104(2), pages 401-419.
    25. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    26. Xu, Liao & Zhang, Xuan & Zhao, Jing, 2023. "Limited investor attention and biased reactions to information: Evidence from the COVID-19 pandemic," Journal of Financial Markets, Elsevier, vol. 62(C).
    27. Thomas Dimpfl & Stephan Jank, 2016. "Can Internet Search Queries Help to Predict Stock Market Volatility?," European Financial Management, European Financial Management Association, vol. 22(2), pages 171-192, March.
    28. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    29. Chronopoulos, Dimitris K. & Papadimitriou, Fotios I. & Vlastakis, Nikolaos, 2018. "Information demand and stock return predictability," Journal of International Money and Finance, Elsevier, vol. 80(C), pages 59-74.
    30. Peng, Lin & Xiong, Wei, 2006. "Investor attention, overconfidence and category learning," Journal of Financial Economics, Elsevier, vol. 80(3), pages 563-602, June.
    31. Matthias Bank & Martin Larch & Georg Peter, 2011. "Google search volume and its influence on liquidity and returns of German stocks," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 25(3), pages 239-264, September.
    32. Vighneswara Swamy & Munusamy Dharani, 2019. "Investor attention using the Google search volume index – impact on stock returns," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 11(1), pages 56-70, May.
    33. Stephanos Papadamou & Alexandros Koulis & Constantinos Kyriakopoulos & Athanasios P. Fassas, 2022. "Cannabis Stocks Returns: The Role of Liquidity and Investors’ Attention via Google Metrics," IJFS, MDPI, vol. 10(1), pages 1-11, January.
    34. Fumiko Takeda & Hiroaki Yamazaki, 2006. "Stock Price Reactions to Public TV Programs on Listed Japanese Companies," Economics Bulletin, AccessEcon, vol. 13(7), pages 1-7.
    35. repec:ebl:ecbull:v:13:y:2006:i:7:p:1-7 is not listed on IDEAS
    36. Melody Y. Huang & Randall R. Rojas & Patrick D. Convery, 2020. "Forecasting stock market movements using Google Trend searches," Empirical Economics, Springer, vol. 59(6), pages 2821-2839, December.
    37. Azi Ben-Rephael & Zhi Da & Ryan D. Israelsen, 2017. "It Depends on Where You Search: Institutional Investor Attention and Underreaction to News," The Review of Financial Studies, Society for Financial Studies, vol. 30(9), pages 3009-3047.
    38. Takeda, Fumiko & Wakao, Takumi, 2014. "Google search intensity and its relationship with returns and trading volume of Japanese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 1-18.
    39. Tantaopas, Parkpoom & Padungsaksawasdi, Chaiyuth & Treepongkaruna, Sirimon, 2016. "Attention effect via internet search intensity in Asia-Pacific stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 38(C), pages 107-124.
    40. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    41. Sowmya Subramaniam & Madhumita Chakraborty, 2021. "COVID-19 fear index: does it matter for stock market returns?," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 13(1), pages 40-50, March.
    42. Ding Ding & Chong Guan & Calvin M. L. Chan & Wenting Liu, 2020. "Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-21, December.
    43. Dong Lou, 2014. "Attracting Investor Attention through Advertising," The Review of Financial Studies, Society for Financial Studies, vol. 27(6), pages 1797-1829.
    44. Raphael H Heiberger, 2015. "Collective Attention and Stock Prices: Evidence from Google Trends Data on Standard and Poor's 100," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-14, August.
    45. Huei-Hwa Lai & Tzu-Pu Chang & Cheng-Han Hu & Po-Ching Chou, 2022. "Can google search volume index predict the returns and trading volumes of stocks in a retail investor dominant market," Cogent Economics & Finance, Taylor & Francis Journals, vol. 10(1), pages 2014640-201, December.
    46. Joseph, Kissan & Babajide Wintoki, M. & Zhang, Zelin, 2011. "Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1116-1127, October.
    47. Júlio Lobão & Luís Pacheco & Carlos Pereira, 2017. "The use of the recognition heuristic as an investment strategy in European stock markets," Journal of Economics, Finance and Administrative Science, Emerald Group Publishing Limited, vol. 22(43), pages 207-223, November.
    48. Dzielinski, Michal, 2012. "Measuring economic uncertainty and its impact on the stock market," Finance Research Letters, Elsevier, vol. 9(3), pages 167-175.
    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. Tihana Škrinjarić, 2019. "Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets," IJFS, MDPI, vol. 7(4), pages 1-30, October.
    2. Chen, Zhongdong & Craig, Karen Ann, 2023. "Active attention, retail investor base, and stock returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    3. Desagre, Christophe & D’Hondt, Catherine, 2021. "Googlization and retail trading activity," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    4. Christophe Desagre & Catherine D'Hondt, 2020. "Googlization and retail investors' trading activity," LIDAM Discussion Papers LFIN 2020004, Université catholique de Louvain, Louvain Finance (LFIN).
    5. Goodell, John W. & Kumar, Satish & Li, Xiao & Pattnaik, Debidutta & Sharma, Anuj, 2022. "Foundations and research clusters in investor attention: Evidence from bibliometric and topic modelling analysis," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 511-529.
    6. Smales, L.A., 2021. "Investor attention and global market returns during the COVID-19 crisis," International Review of Financial Analysis, Elsevier, vol. 73(C).
    7. Sifat, Imtiaz Mohammad & Thaker, Hassanudin Mohd Thas, 2020. "Predictive power of web search behavior in five ASEAN stock markets," Research in International Business and Finance, Elsevier, vol. 52(C).
    8. Papadamou, Stephanos & Fassas, Athanasios & Kenourgios, Dimitris & Dimitriou, Dimitrios, 2020. "Direct and Indirect Effects of COVID-19 Pandemic on Implied Stock Market Volatility: Evidence from Panel Data Analysis," MPRA Paper 100020, University Library of Munich, Germany.
    9. Ramos, Sofia B. & Latoeiro, Pedro & Veiga, Helena, 2020. "Limited attention, salience of information and stock market activity," Economic Modelling, Elsevier, vol. 87(C), pages 92-108.
    10. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2024. "Google search trends and stock markets: Sentiment, attention or uncertainty?," International Review of Financial Analysis, Elsevier, vol. 91(C).
    11. Geng, Yuedan & Ye, Qiang & Jin, Yu & Shi, Wen, 2022. "Crowd wisdom and internet searches: What happens when investors search for stocks?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    12. Chaiyuth Padungsaksawasdi & Sirimon Treepongkaruna & Robert Brooks, 2019. "Investor Attention and Stock Market Activities: New Evidence from Panel Data," IJFS, MDPI, vol. 7(2), pages 1-19, June.
    13. Nguyen, Cuong & Hoang, Lai & Shim, Jungwook & Truong, Phuong, 2020. "Internet search intensity, liquidity and returns in emerging markets," Research in International Business and Finance, Elsevier, vol. 52(C).
    14. Tantaopas, Parkpoom & Padungsaksawasdi, Chaiyuth & Treepongkaruna, Sirimon, 2016. "Attention effect via internet search intensity in Asia-Pacific stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 38(C), pages 107-124.
    15. Imene Ben El Hadj Said & Skander Slim, 2022. "The Dynamic Relationship between Investor Attention and Stock Market Volatility: International Evidence," JRFM, MDPI, vol. 15(2), pages 1-25, February.
    16. Kim, Neri & Lučivjanská, Katarína & Molnár, Peter & Villa, Roviel, 2019. "Google searches and stock market activity: Evidence from Norway," Finance Research Letters, Elsevier, vol. 28(C), pages 208-220.
    17. Cheng, Feiyang & Chiao, Chaoshin & Wang, Chunfeng & Fang, Zhenming & Yao, Shouyu, 2021. "Does retail investor attention improve stock liquidity? A dynamic perspective," Economic Modelling, Elsevier, vol. 94(C), pages 170-183.
    18. Dong, Dayong & Wu, Keke & Fang, Jianchun & Gozgor, Giray & Yan, Cheng, 2022. "Investor attention factors and stock returns: Evidence from China," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    19. Tariq Aziz & Valeed Ahmad Ansari, 2021. "How Does Google Search Affect the Stock Market? Evidence from Indian Companies," Vision, , vol. 25(2), pages 224-232, June.
    20. González-Fernández, Marcos & González-Velasco, Carmen, 2020. "A sentiment index to measure sovereign risk using Google data," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 406-418.

    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:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-023-00606-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.