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Record W3022387591 · doi:10.1080/09638180.2020.1756885

Press Release Management around Accelerated Share Repurchases

2020· article· en· W3022387591 on OpenAlex
Kai Chen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Accounting Review · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsShare repurchaseBusinessStock priceOpen market operationStock (firearms)PopularityShare priceMonetary economicsFinanceShareholderEconomicsCorporate governanceStock exchangeMonetary policy

Abstract

fetched live from OpenAlex

Accelerated share repurchases (ASRs) have gained popularity in recent years. At the inception of an ASR, a firm receives most of the shares that would be repurchased at the current stock price. At the completion of the ASR, the repurchase price is adjusted to the average stock price during the ASR contract period. I find that managers increase the media coverage of negative press releases before the inception date but not during the contract period. While prior research has documented that managers deflate stock prices before open market repurchase announcements to reduce subsequent repurchase prices, this paper suggests that, in ASRs, managers rank accelerating share delivery higher than reducing the repurchase price.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.764
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.100
GPT teacher head0.245
Teacher spread0.145 · 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