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Record W2753816643 · doi:10.1111/1911-3846.12353

Is Real Earnings Smoothing Harmful? Evidence from Firm‐Specific Stock Price Crash Risk

2017· article· en· W2753816643 on OpenAlexvenueno aff
Inder K. Khurana, Raynolde Pereira, Eliza Xia Zhang

Bibliographic record

VenueContemporary Accounting Research · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsSmoothingEarningsCrashShareholderStock (firearms)Stock priceEconomicsEconometricsBusinessMonetary economicsFinancial economicsFinanceCorporate governanceComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract This study examines whether and when real earnings smoothing influences firm‐specific stock price crash risk. Using a sample of U.S. public firms for the years 1993 through 2014, we find real earnings smoothing to be positively associated with firm‐specific stock price crash risk. This finding is consistent with the view that real earnings smoothing helps managers withhold bad news, keep poor‐performing projects, conceal resource diversion, and engage in ineffective risk management, which increases crash risk. Further, we find a stronger relation between crash risk and real earnings smoothing when firm uncertainty is higher, product market competition is lower, and balance sheet constraint is higher. Overall, our study suggests that real earnings smoothing destroys shareholder value in that it increases stock price crash risk.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.009
metaresearch head score (Gemma)0.044
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.001
Scholarly communication0.0080.012
Open science0.0040.005
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.004

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 head0.098
GPT teacher head0.332
Teacher spread0.235 · 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; both teacher heads agree on what is shown here.

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

Citations166
Published2017
Admission routes1
Has abstractyes

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