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Record W2804791993 · doi:10.1111/1911-3846.12404

Blockholder Exit Threats and Financial Reporting Quality

2018· article· en· W2804791993 on OpenAlexaffvenue
Yiwei Dou, Ole‐Kristian Hope, Wayne B. Thomas, Youli Zou

Bibliographic record

VenueContemporary Accounting Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsShareholderBusinessCorporate governanceIncentiveEarningsStock (firearms)FinanceEarnings managementAccountingQuality (philosophy)Earnings qualityEconomicsMarket economyAccrual

Abstract

fetched live from OpenAlex

Abstract Recent theoretical and empirical studies suggest that blockholders (shareholders with ownership ≥ 5 percent) exert governance through the threat of exit. Blockholders have strong incentives to gather private information and sell their shares when managers are perceived to underperform. To prevent blockholders from selling their shares and the firm from suffering a stock price decline, managers align their actions with the interests of shareholders. As a result of the greater manager‐shareholder alignment, managers' actions are more likely to be in shareholders' best interest, and consequently there is less need for managers to manipulate earnings. Consistent with these predictions from economic theory, we find evidence that as exit threat increases, firms have higher financial reporting quality. Theory also predicts that the impact of blockholders' exit threat on financial reporting quality (FRQ) should increase as the manager's wealth is tied more closely to the stock price, and this is what we find. Our study contributes to the research on the impact of shareholders on FRQ and to an emerging literature on the impact of blockholders in financial markets. Blockholders play an important role in managers' reporting outcomes through their actions as informed investors.

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)0.013
metaresearch head score (Gemma)0.060
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.060
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.003
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.120
GPT teacher head0.366
Teacher spread0.246 · 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

Citations167
Published2018
Admission routes2
Has abstractyes

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