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Record W3121455965 · doi:10.1111/1911-3846.12546

The Switch‐Up: An Examination of Changes in Earnings Management after Receiving SEC Comment Letters

2019· article· en· W3121455965 on OpenAlexvenueno aff
Lauren M. Cunningham, Bret Johnson, E. Scott Johnson, Ling Lei Lisic

Bibliographic record

VenueContemporary Accounting Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAccrualEarnings managementScrutinyReceiptAccountingEarningsEconomicsBusinessMonetary economicsPolitical scienceLaw

Abstract

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ABSTRACT The SEC has long asserted that earnings management practices result in adverse consequences for investors. We examine whether SEC oversight affects firms' accounting quality in terms of earnings management trade‐offs. We expect that increased firm‐specific regulatory scrutiny, in the form of an SEC comment letter, will induce management to switch from accrual‐based earnings management (AEM), which is a main focus of the SEC, to real‐activities‐based earnings management (REM), which is not likely to be commented on in the SEC's review process. Consistent with our predictions, we find that AEM is lower and REM is higher following the receipt of a comment letter, relative to non‐comment‐letter years and a propensity‐score‐matched sample of non‐comment‐letter firms. However, we do not find a significant difference in total earnings management (i.e., the sum of AEM and REM), suggesting that the higher REM acts as a substitute for lower AEM activity. We further find that our results are driven by accounting comments relating to estimates and accruals and not by classification‐only comments, which suggests that a comment letter that does not question specific issues associated with estimates and accruals is not a strong enough signal to induce the firm to change earnings management behavior. Additionally, the shift to REM is attenuated for firms with high institutional ownership. These results collectively suggest that the comment letter process effectively constrains AEM but has the unintended consequence of firms, on average, switching to REM.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.270
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations143
Published2019
Admission routes1
Has abstractyes

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