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Record W4401473948 · doi:10.1111/jbfa.12826

EPS‐motivated share repurchases and wealth transfer

2024· article· en· W4401473948 on OpenAlexafffund
Christina A. Mashruwala, Shamin Mashruwala

Bibliographic record

VenueJournal of Business Finance &amp Accounting · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversity of Alberta
FundersUniversity of AlbertaTemple UniversityCanadian Academic Accounting AssociationWashington and Lee University
KeywordsTransfer (computing)BusinessMonetary economicsFinancial systemEconomicsComputer science

Abstract

fetched live from OpenAlex

Abstract We study the association between earnings‐per‐share (EPS)‐motivated share repurchases and wealth transfer between the repurchasing firm's ongoing shareholders and selling/transacting shareholders. Compared to other repurchases, EPS‐accretive repurchases are associated with greater wealth transfer from ongoing to selling shareholders, thereby reducing shareholder value for ongoing shareholders. We also find that EPS‐accretive repurchases used to meet/beat analyst forecasts are associated with incrementally more wealth reduction for ongoing shareholders, compared to other EPS‐accretive repurchases. These findings suggest that, compared to other repurchases, repurchases driven by EPS concerns are more likely to benefit selling shareholders at the expense of ongoing shareholders (all else equal). Using quarterly earnings announcements, we find that investors price this one‐time repurchase‐induced wealth reduction for ongoing shareholders. Despite this, however, investors appear to take a positive overall view of EPS‐driven repurchases, suggesting that the benefits of such repurchases for ongoing shareholders outweigh the one‐time wealth reduction from such repurchases. Consistent with this, we find that EPS‐motivated repurchases are associated with better future operating performance.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.005
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 head0.023
GPT teacher head0.228
Teacher spread0.206 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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