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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
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    References listed on IDEAS

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