The benefits of transaction-level data: The case of NielsenIQ scanner data

• 成果类型: Article
• 署名作者: Dichev, Ilia D.; Qian, Jingyi
• 署名单位: Emory University
• 刊物名称: JOURNAL OF ACCOUNTING & ECONOMICS
• ISSN/ISSBN: 0165-4101
• DOI: 10.1016/j.jacceco.2022.101495
• 发表日期: 2022
• 关键词: FULLY REFLECT; stock-prices; earnings; INFORMATION; dependence; anomalies

摘要

This study explores whether NielsenIQ scanner data from U.S. retailers contain incremental information about the GAAP revenue of corresponding manufacturers. Using retail product/store/week data from 2006 to 2018, we construct a measure of aggregated consumer purchases at the manufacturer/quarter level, and find that it strongly predicts GAAP revenues. In addition, analyst forecasts of revenues have predictable errors, which implies that analysts do not fully incorporate the information in consumer purchases. Exploring investment implications, we find that hedge portfolios that buy (sell) stocks of firms with high (low) abnormal consumer purchases generate annualized returns on the magnitude of 14%e19%, depending on specification. Overall, these findings suggest that scanner data on consumer purchases provide an information edge over GAAP revenue, shedding light on the benefits of using transactional data.