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

Ilia D. Dichev and Jingyi Qian

Journal of Accounting and Economics, 2022, vol. 74, issue 1

Abstract: 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%–19%, 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.

Keywords: Transactional data; Consumer purchase; Equity valuation; Market efficiency (search for similar items in EconPapers)
JEL-codes: G12 G14 M31 M41 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jaecon:v:74:y:2022:i:1:s0165410122000180

DOI: 10.1016/j.jacceco.2022.101495

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