Predicting Performance Using Consumer Big Data
研究利用店内客流量、网站流量和品牌兴趣三种大数据代理变量预测企业业绩，发现基于这些指标的交易策略在2009-2020年间能获得显著超额收益，且疫情期间线上活动激增而线下锐减。
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.