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Google Search intensity and stock returns in frontier markets: Evidence from the Vietnamese market

Author

Listed:
  • Duc Dang Thi Viet

    (Faculty of Finance and Accounting, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam)

  • Hoai Nguyen Thu

    (Department of Economic and Management, Thang Long University, Hanoi, Vietnam)

  • Nguyen Van Phuoc

    (Faculty of Business Administration, Posts and Telecommunications Institute of Technology, Ho Chi Minh city Campus, Vietnam)

  • Nguyen Dang Phong

    (Faculty of Finance and Accounting, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam)

  • Anh Nguyen Huong

    (Faculty of Finance and Accounting, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam)

  • Hai Ho Hong

    (College of Business & Management, VinUniversity, Hanoi, Vietnam)

Abstract

The study investigates the impact of investor attention on stock trading by modeling the relationship between Google Search intensity and stock return with stocks listed in frontier markets in Vietnam from October 2016 to October 2021. The study has three findings. First, the study confirms the price pressure hypothesis and attention theory that Google Search intensity positively affects stock returns. Second, this study indicates that the impact of Google Search intensity on stock price is short-term. The positive effect is within the week of searching and reverses the following week, although the reverse force is not strong. Third, the relationship is more robust during COVID-19 than in the pre-pandemic period, suggesting that after a shock, more new individual investors enter the market, the impact of GSVI on stock return is more substantial.

Suggested Citation

  • Duc Dang Thi Viet & Hoai Nguyen Thu & Nguyen Van Phuoc & Nguyen Dang Phong & Anh Nguyen Huong & Hai Ho Hong, 2024. "Google Search intensity and stock returns in frontier markets: Evidence from the Vietnamese market," Economics and Business Review, Sciendo, vol. 10(1), pages 30-56, April.
    • Handle: RePEc:vrs:ecobur:v:10:y:2024:i:1:p:30-56:n:9
    DOI: 10.18559/ebr.2024.1.778
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    References listed on IDEAS

    as
    1. Shleifer, Andrei & Summers, Lawrence H, 1990. "The Noise Trader Approach to Finance," Journal of Economic Perspectives, American Economic Association, vol. 4(2), pages 19-33, Spring.
    2. Siganos, Antonios, 2013. "Google attention and target price run ups," International Review of Financial Analysis, Elsevier, vol. 29(C), pages 219-226.
    3. Tao Chen, 2017. "Investor Attention and Global Stock Returns," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 18(3), pages 358-372, July.
    4. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    5. Bijl, Laurens & Kringhaug, Glenn & Molnár, Peter & Sandvik, Eirik, 2016. "Google searches and stock returns," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 150-156.
    6. 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.
    7. 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.
    8. Chundakkadan, Radeef & Nedumparambil, Elizabeth, 2022. "In search of COVID-19 and stock market behavior," Global Finance Journal, Elsevier, vol. 54(C).
    9. 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.
    10. Dehua Shen & Yongjie Zhang & Xiong Xiong & Wei Zhang, 2017. "Baidu index and predictability of Chinese stock returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-8, December.
    11. Pyo, Dong-Jin, 2017. "Can Big Data Help Predict Financial Market Dynamics?: Evidence from the Korean Stock Market," East Asian Economic Review, Korea Institute for International Economic Policy, vol. 21(2), pages 147-165, June.
    12. Roger K. Loh, 2010. "Investor Inattention and the Underreaction to Stock Recommendations," Financial Management, Financial Management Association International, vol. 39(3), pages 1223-1252, September.
    13. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
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    More about this item

    Keywords

    investor attention; search intensity; Google Search; stock returns; frontier markets; Vietnam; COVID-19;
    All these keywords.

    JEL classification:

    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • G1 - Financial Economics - - General Financial Markets
    • G4 - Financial Economics - - Behavioral Finance

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