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

Hsiu-lang Chen () and Jolana Stejskalová
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Hsiu-lang Chen: Department of Finance, University of Illinois Chicago, 601 South Morgan Street, Chicago, IL 60607, USA

JRFM, 2024, vol. 17, issue 10, 1-19

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.

Keywords: Google search volume index; S&P 500 sector indices; standardized unexpected earnings (SUEs) (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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