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Corporate Governance Reform and Executive Incentives: Implications for Investments and Risk Taking

2012· article· en· W2047935960 on OpenAlexvenueno aff
Daniel Cohen, Aiyesha Dey, Thomas Z. Lys

Bibliographic record

VenueContemporary Accounting Research · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceExecutive compensationIncentiveBusinessCompensation (psychology)Investment (military)AccountingFinanceMonetary economicsEconomicsMarket economy

Abstract

fetched live from OpenAlex

We investigate the mechanism through which the Sarbanes Oxley Act ( SOX ) was associated with changes in corporate investment strategies. We document that the passage of the governance regulations in SOX was followed by a significant decline in pay‐performance sensitivity (Delta) and incentives to take risk (Vega) in CEO s' compensation contracts. These changes in compensation contracts are related to a decline in investments, including research and development expenditures, capital investments and acquisitions. Moreover, consistent with the rules in SOX directly affecting CEO s' incentives to take risk, we document that the decline in investments exceeds the amount that would be expected from changes in compensation packages alone. Finally, we also find evidence that the changes in investments are related to lower operating performances of firms, suggesting that these changes were costly to investors. Our evidence speaks to the debate on how corporate governance regulation interacts with firms' and managers' incentives, and ultimately affects corporate operating and investment strategies. Our study suggests that one indirect cost of such regulations in SOX is the significant reductions in corporate risk‐taking activities in the post‐ SOX period. The changes in investments were in part due to changes in executive compensation contracts and in part related to increased executives' personal costs of engaging in risky activities.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.005
Open science0.0000.000
Research integrity0.0000.000
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 head0.126
GPT teacher head0.326
Teacher spread0.200 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations156
Published2012
Admission routes1
Has abstractyes

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