Using Google searches of firm products to detect revenue management

Peng-Chia Chiu, Siew Hong Teoh, Yinglei Zhang and Xuan Huang

Accounting, Organizations and Society, 2023, vol. 109, issue C

Abstract: We introduce a novel Big Data analytics model to detect upward revenue misreporting. The model uses freely available Google searches of firm products to provide external entity business state (EBS) evidence. The veracity of the reported numbers is enhanced when auditors can obtain external EBS evidence congruent with the reported numbers. The Google search volume index (SVI) of firm products is a good candidate for such EBS evidence because it nowcasts (i.e. predicts present) firm sales and is independent of management control. A large discrepancy such as a high sales growth together with a large decline in the SVI suggests possible manipulation upwards of revenues. We find that an indicator variable, MUP, of a firm in the top sales growth quartile and bottom ΔSVI quartile in each industry-quarter predicts revenue misstatements incrementally to the F_Score, Discretionary-Revenues model, two alternative upward revenue manipulation identifiers, and analyst and media coverages. MUP predictability is stronger in end-user industries and in interim quarters relative to the fourth quarter. We also find corroborating evidence that MUP firms have lower sales growth persistence, larger increases in accounts receivables, and lower allowances for bad debts, consistent with their lower revenue quality.

Keywords: Big data analytics; Audit risk assessment; Revenue fraud detection; Financial reporting quality; Auditing theory of evidentiary triangulation; Earnings management; Predicting accounting fraud (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:aosoci:v:109:y:2023:i:c:s0361368223000284

DOI: 10.1016/j.aos.2023.101457

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