PT - JOURNAL ARTICLE AU - Kenneth Froot AU - Namho Kang AU - Gideon Ozik AU - Ronnie Sadka TI - Predicting Performance Using Consumer Big Data AID - 10.3905/jpm.2021.1.320 DP - 2021 Dec 16 TA - The Journal of Portfolio Management PG - jpm.2021.1.320 4099 - https://pm-research.com/content/early/2021/12/16/jpm.2021.1.320.short 4100 - https://pm-research.com/content/early/2021/12/16/jpm.2021.1.320.full AB - 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, results in significant net-of-transaction-costs profitability. During the pandemic, WEB activity increased significantly whereas IN-STORE experienced a remarkable decrease, reflecting the migration of consumers from physical stores toward online retailers. The results suggest that the information contained in IN-STORE and BRAND is not immediately available to investors, whereas the WEB information diffuses more quickly, and overall information diffusion worsened during the pandemic.