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Record W4286623112 · doi:10.5465/ambpp.2022.306

The Impact of Protective Takeover Regulations on Acquiring Firms

2022· article· en· W4286623112 on OpenAlexaffabout
Ipsita Sahoo, Abhirup Chakrabarti

Bibliographic record

VenueAcademy of Management Proceedings · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsQueen's University
Fundersnot available
KeywordsBusinessShareholderAffect (linguistics)Sample (material)Unintended consequencesMergers and acquisitionsIndustrial organizationFinanceCorporate governance

Abstract

fetched live from OpenAlex

This study explores how protective takeover regulations indirectly affect acquiring firms. Protective takeover regulations, such as the new bid regime introduced in Canada in 2016, provide targets more time to respond to takeover offers and offer greater protection to minority shareholders. Yet, such strategies could also influence acquiring firms to change their strategies over time. Using a sample of Canadian manufacturing firms that operated between 2012-2020 we find that the new bid regime had no impact on reducing hostile acquisitions, but that it increased the tendency of acquiring firms to undertake unrelated and cross-border acquisitions. In turn, it indirectly reduced the ability of acquiring firms to successfully complete their acquisitions. The findings suggest that protective takeover regulations may have broader and unintended effects by driving acquiring firms to undertake riskier acquisitions and experience poorer acquisition outcomes.

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)0.022
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 score0.074
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.251
Teacher spread0.228 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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