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.