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Termination Fees in Mergers and Acquisitions: Protecting Investors or Managers?

2007· article· en· W3125206817 on OpenAlex
Paúl André, Samer Khalil, Michel Magnan

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Business Finance &amp Accounting · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsConcordia University
Fundersnot available
KeywordsMergers and acquisitionsBusinessMonetary economicsCashAgency costStock (firearms)Agency (philosophy)FinanceAccountingFinancial systemEconomicsShareholder

Abstract

fetched live from OpenAlex

Abstract: Institutional investors closely monitor termination fees in mergers and acquisitions (M&A). We argue that their magnitude reflects either agency problems or efficiency considerations. Focusing on M&A involving Canadian targets between 1997 and 2004, we assess the determinants and market impact of termination fees. Our findings show that the Thomson's SDC Platinum™ Worldwide Mergers & Acquisitions Database underestimates their extent. Results suggest that termination fees are essentially an efficient mechanism as they are relatively higher in M&A with high merger costs, a cash component and expected operating synergies. Stock market returns surrounding the deal announcement do not differ across levels of relative termination fees.

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)0.002
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 score0.151
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
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 head0.022
GPT teacher head0.238
Teacher spread0.216 · 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