Predicting Performance Using Consumer Big Data
Forthcoming in the Journal of Portfolio Management
By Ken Froot, Namho Kang, Gideon Ozik, and Ronnie Sadka
To predict firms’ fundamentals, the authors construct three proxies for real-time corporate sales
from fully distinct information sources: In-store foot traffic (IN-STORE), web traffic to
companies’ websites (WEB), and consumers’ interest level in corporate brands and products
(BRAND). The authors demonstrate that trading using these proxies, estimated for a sample of
330 firms over 2009–2020, result in significant net-of-transaction-costs profitability. During the
pandemic, WEB activity increases significantly while there is remarkable decrease in IN-
STORE, reflecting the migration of consumers from physical stores toward online. The results
suggest that the information contained in IN-STORE and BRAND is not immediately available
to investors, while the WEB information is diffused more quickly, and that overall information
diffusion worsened during the pandemic.
We will post a link to the Journal of Portfolio Management when the paper is published.