IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i10p452-d1492882.html
   My bibliography  Save this article

A Collection of Wisdom in Predicting Sector Returns: The Use of Google Search Volume Index

Author

Listed:
  • Hsiu-lang Chen

    (Department of Finance, University of Illinois Chicago, 601 South Morgan Street, Chicago, IL 60607, USA)

  • Jolana Stejskalova

    (Department of Finance, Mendel University in Brno, 613 00 Brno, Czech Republic
    For helpful comments, we thank participants at the conference, World Finance & Banking Symposium in Budapest, Hungary, 17–18 December 2021; ITISE 2022 (8th International conference on Time Series and Forecasting), Gran Canaria, Spain, 27–30 June 2022; the 12th Biennial Conference of the Czech Economic Society in Prague, Czech Republic, 25–26 November 2022; and World Finance Conference in in Kristiansand, Norway, 2–4 August 2023.)

Abstract

This study investigates whether the aggregate investor information demand for all stocks in a sector demonstrated in the Google search volume index (SVI) can predict the sector’s performance. The evidence shows that a sector’s abnormal SVI can predict the sector’s performance next month, even after controlling for the sector’s contemporaneous standardized unexpected earnings and lagged returns on both the market and the sector. Also found is a partial reversal in the sector’s long-run performance that is not completely consistent with the price pressure hypothesis. This indicates that some fundamental information about a sector can be captured by the sector’s abnormal SVI on a timely basis.

Suggested Citation

  • Hsiu-lang Chen & Jolana Stejskalova, 2024. "A Collection of Wisdom in Predicting Sector Returns: The Use of Google Search Volume Index," JRFM, MDPI, vol. 17(10), pages 1-19, October.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:10:p:452-:d:1492882
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/10/452/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/17/10/452/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bernard, Victor L. & Thomas, Jacob K., 1990. "Evidence that stock prices do not fully reflect the implications of current earnings for future earnings," Journal of Accounting and Economics, Elsevier, vol. 13(4), pages 305-340, December.
    2. Bernard, Vl & Thomas, Jk, 1989. "Post-Earnings-Announcement Drift - Delayed Price Response Or Risk Premium," Journal of Accounting Research, Wiley Blackwell, vol. 27, pages 1-36.
    3. Mitchell A. Petersen, 2009. "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches," The Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 435-480, January.
    4. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    5. Hirshleifer, David & Teoh, Siew Hong, 2003. "Limited attention, information disclosure, and financial reporting," Journal of Accounting and Economics, Elsevier, vol. 36(1-3), pages 337-386, December.
    6. Joshua Livnat & Richard R. Mendenhall, 2006. "Comparing the Post–Earnings Announcement Drift for Surprises Calculated from Analyst and Time Series Forecasts," Journal of Accounting Research, Wiley Blackwell, vol. 44(1), pages 177-205, March.
    7. 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.
    8. Giuseppe Moscarini & Lones Smith, 2002. "The Law of Large Demand for Information," Econometrica, Econometric Society, vol. 70(6), pages 2351-2366, November.
    9. 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.
    10. Paul R. Milgrom, 1981. "Good News and Bad News: Representation Theorems and Applications," Bell Journal of Economics, The RAND Corporation, vol. 12(2), pages 380-391, Autumn.
    11. Gur Huberman & Tomer Regev, 2001. "Contagious Speculation and a Cure for Cancer: A Nonevent that Made Stock Prices Soar," Journal of Finance, American Finance Association, vol. 56(1), pages 387-396, February.
    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. Zhu, Hui, 2014. "Implications of limited investor attention to customer–supplier information transfers," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(3), pages 405-416.
    2. Verhoeks, Ralph C. & Verschoor, Willem F.C. & Zwinkels, Remco C.J., 2024. "Wall street watches Washington: Asset pricing implications of policy uncertainty," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
    3. Wen, Fenghua & Xu, Longhao & Ouyang, Guangda & Kou, Gang, 2019. "Retail investor attention and stock price crash risk: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 65(C).
    4. Zhuo Li & Meiyu Tian & Guangda Ouyang & Fenghua Wen, 2021. "Relationship between investor sentiment and earnings news in high‐ and low‐sentiment periods," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2748-2765, April.
    5. David Hirshleifer & Po-Hsuan Hsu & Dongmei Li, 2018. "Innovative Originality, Profitability, and Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2553-2605.
    6. Jiang, George J. & Zhu, Kevin X., 2017. "Information Shocks and Short-Term Market Underreaction," Journal of Financial Economics, Elsevier, vol. 124(1), pages 43-64.
    7. Vozlyublennaia, Nadia, 2014. "Investor attention, index performance, and return predictability," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 17-35.
    8. Stefano Dellavigna & Joshua M. Pollet, 2009. "Investor Inattention and Friday Earnings Announcements," Journal of Finance, American Finance Association, vol. 64(2), pages 709-749, April.
    9. Qian Chen & Xiang Gao & Gangchen Liu, 2021. "Limited Attention and Post-Earnings Announcement Drift: Evidence from China s Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 11(1), pages 1-17.
    10. Ashish Agarwal & Alvin Chung Man Leung & Prabhudev Konana & Alok Kumar, 2017. "Cosearch Attention and Stock Return Predictability in Supply Chains," Information Systems Research, INFORMS, vol. 28(2), pages 265-288, June.
    11. Jordan Moore, 2020. "Glamour among value: P/E ratios and value investor attention," Financial Management, Financial Management Association International, vol. 49(3), pages 673-706, September.
    12. Allison Koester & Russell Lundholm & Mark Soliman, 2016. "Attracting Attention in a Limited Attention World: Exploring the Causes and Consequences of Extreme Positive Earnings Surprises," Management Science, INFORMS, vol. 62(10), pages 2871-2896, October.
    13. Wang, Weimin & (Frank) Wang, Xu, 2014. "Predicting earnings in a poor information environment," Journal of Contemporary Accounting and Economics, Elsevier, vol. 10(1), pages 46-58.
    14. Michaely, Roni & Rubin, Amir & Vedrashko, Alexander, 2016. "Are Friday announcements special? Overcoming selection bias," Journal of Financial Economics, Elsevier, vol. 122(1), pages 65-85.
    15. Cedric Mbanga & Ali F. Darrat & Jung Chul Park, 2019. "Investor sentiment and aggregate stock returns: the role of investor attention," Review of Quantitative Finance and Accounting, Springer, vol. 53(2), pages 397-428, August.
    16. Daniel, Kent & Hirshleifer, David & Teoh, Siew Hong, 2002. "Investor psychology in capital markets: evidence and policy implications," Journal of Monetary Economics, Elsevier, vol. 49(1), pages 139-209, January.
    17. Chen, Linda H. & Jiang, George J. & Zhu, Kevin X., 2018. "Total attention: The effect of macroeconomic news on market reaction to earnings news," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 142-156.
    18. Qian Chen & Xiang Gao & Jianming Mo & Zhouling Xu, 2022. "Market Reaction to Local Attention around Earnings Announcements in China: Evidence from Internet Search Activity," IJFS, MDPI, vol. 10(4), pages 1-26, October.
    19. Smales, L.A., 2021. "Investor attention and global market returns during the COVID-19 crisis," International Review of Financial Analysis, Elsevier, vol. 73(C).
    20. Norio Kitagawa & Akinobu Shuto, 2015. "Credibility of management earnings forecasts and future returns," CARF F-Series CARF-F-367, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.

    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:gam:jjrfmx:v:17:y:2024:i:10:p:452-:d:1492882. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.