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Record W2593955014 · doi:10.1287/mnsc.2016.2635

Full-Stock-Payment Marginalization in Merger and Acquisition Transactions

2017· article· en· W2593955014 on OpenAlexaboutno aff
Eric de Bodt, Jean-Gabriel Cousin, Richard Roll

Bibliographic record

VenueManagement Science · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
FundersCalifornia Institute of Technology
KeywordsPoolingCounterfactual thinkingGoodwillPaymentStock (firearms)BusinessMonetary economicsShares outstandingIncentiveAccountingEconomicsActuarial scienceFinanceMicroeconomicsShareholderCorporate governance

Abstract

fetched live from OpenAlex

The number of merger and acquisition (M&A) transactions paid fully in stock in the U.S. market declined sharply after 2001, when pooling and goodwill amortization were abolished by the Financial Accounting Standards Board. Did this accounting rule change really have such far reaching implications? Using a difference-in-differences test and Canada as a counterfactual, this study reveals that it did. We also report several other results confirming the role of pooling abolishment, including (i) that the decrease in full stock payment relates to CEO incentives and (ii) that previously documented determinants of the M&A mode of payment cannot explain the post-pooling abolishment pattern. These results are also robust to controls for various factors, such as the Internet bubble, the exclusion of cross-border deals, the presence of Canadian cross-listed firms, the use of a constant sample of acquirers across the pooling and post-pooling abolishment periods, the use of Europe as an alternative counterfactual, and controls for the SEC Rule 10b-18 share repurchase safe harbor amendments of 2003. This paper was accepted by Shivaram Rajgopal, accounting.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.017
GPT teacher head0.234
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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

Citations40
Published2017
Admission routes1
Has abstractyes

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