May 23, 2022

Research Paper: Search Engine Data a Useful Predictor of Stock Returns

From a University of Kansas Story:

New research by a team of researchers at the KU School of Business demonstrates that online ticker searches – for example, “XOM” for Exxon Mobil – can predict abnormal stock returns and trading volumes during the subsequent week. The research also shows that highly volatile stocks will be more sensitive to online search intensity than less volatile stocks.


The research was conducted by Kissan Joseph, associate professor of marketing; Jide Wintoki, assistant professor of finance; and Zelin Zhang, doctoral candidate in marketing, all at the KU School of Business. Their results will appear in an upcoming issue of the International Journal of Forecasting.

“There’s growing evidence in various disciplines that online search data can predict behavior,” Joseph said. “We’ve demonstrated that search engine data – the kind you can easily retrieve from Google Insights for Search, for example – is a reliable predictor of stock returns and trading volumes, especially for volatile stocks whose true value is hard to gauge.”

Read the Complete Story

See Also: Here’s a Preprint of the Full Text Research Paper

Joseph, Kissan, Jide Wintoki, and Zelin Zhang (2011), “Forecasting Abnormal Stock Returns and Trading Volume Using Investor Sentiment: Evidence from Online Search,” Forthcoming, International Journal of Forecasting.

See Also: Google Insights for Search

About Gary Price

Gary Price ( is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. Before launching INFOdocket, Price and Shirl Kennedy were the founders and senior editors at ResourceShelf and DocuTicker for 10 years. From 2006-2009 he was Director of Online Information Services at, and is currently a contributing editor at Search Engine Land.