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Record W3123945133 · doi:10.1007/s11142-010-9136-1

Are CEOs compensated for value destroying growth in earnings?

2010· article· en· W3123945133 on OpenAlexaff
Sudhakar V. Balachandran, Partha S. Mohanram

Bibliographic record

VenueReview of Accounting Studies · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProfitability indexEarnings growthEarningsInvestment (military)Executive compensationMonetary economicsValue (mathematics)EconomicsCompensation (psychology)ShareholderBusinessLabour economicsCorporate governanceFinance

Abstract

fetched live from OpenAlex

Prior research shows that firms generating earnings growth by improving profitability create shareholder value, while firms generating earnings growth through investment destroy value. This paper examines whether compensation committees consider this while determining CEO compensation. We first confirm prior results that growth from increased profitability is perceived by markets to add value while growth from investment does not. While growth from increased profitability is positively associated with compensation, so is growth from investment. The presence of institutional ownership increases the weight on growth from increased profitability, but does not reduce the weight on growth from investment. Further, value-oriented institutional ownership increases the sensitivity of compensation growth to growth from increased profitability and reduces the sensitivity to growth from investment. Contrarily, growth-oriented institutional ownership increases the sensitivity of compensation growth to growth from investment. Our results highlight the importance of understanding the nature of earnings growth in determining executive compensation.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.079
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.038
GPT teacher head0.287
Teacher spread0.249 · 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

Citations13
Published2010
Admission routes1
Has abstractyes

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