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Article . 2018 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Modelling Receivables and Deferred Revenues to Detect Revenue Management

Authors: Jenny Zha Giedt;

Modelling Receivables and Deferred Revenues to Detect Revenue Management

Abstract

This study develops and calibrates a revenue accruals model. Changes in accounts receivable and deferred revenues are modelled using the respective income statement and cash flow numbers (i.e., revenues and cash flows from sales) that relate directly to the accruals’ origination and reversal. Compared to existing models, the proposed specification explains more variation in the data and, in simulations with seeded revenue manipulation, exhibits greater detection power and less bias. Furthermore, the abnormal revenue estimates are positively associated with cases of revenue misstatements identified by the Securities and Exchange Commission. Results imply that researchers, auditors, and regulators interested to detect earnings management should focus on modelling specific accruals. As a practical matter, to detect revenue management, they should consider broadening their scope to examine not only accounts receivable but also current and long‐term deferred revenues.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Top 10%
Average
Top 10%
hybrid