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Record W2332293522 · doi:10.1108/sef-11-2014-0224

The impact of capital shocks on M&A transactions

2016· article· en· W2332293522 on OpenAlexaff
Hongchao Zeng, Ying Huang

Bibliographic record

VenueStudies in Economics and Finance · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsShareholderEndogeneityAgency costFinanceShock (circulatory)Monetary economicsCash flowBusinessEconomicsPropensity score matchingInitial public offeringInvestment (military)Corporate governanceEconometrics

Abstract

fetched live from OpenAlex

Purpose – This paper aims to examine whether an exogenous shock to the supply of financial capital mitigates agency conflicts between managers and shareholders and incentivizes managers to channel available financial resources into value-increasing acquisitions. Design/methodology/approach – The authors use a difference-in-differences approach to mitigate endogeneity concerns. To address the concern that substantial differences between the treatment and control groups may violate the parallel-trend assumption of the D-in-D approach, the authors use a propensity score-matching procedure and construct a matched sample for their empirical analysis. Findings – The authors find that below-investment-grade firms are significantly less likely to make acquisitions relative to unrated firms following the collapse of the junk bond market in 1989. Conditional on initiating a successful acquisition, below-investment-grade acquirers are less likely to acquire diversifying targets and public targets, but more likely to acquire subsidiary targets. In addition, below-investment-grade acquirers experience higher post-merger operating and stock performance for acquisitions initiated in the post-shock period. Originality/value – The authors demonstrate that capital shocks negatively impact managers’ propensity to make acquisitions, which are considered a well-established outlet for agency conflicts between managers and shareholders. In addition, managers who are subject to capital shocks tend to manage available financial resources more efficiently and make better acquisition decisions that lead to greater value creation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.031
GPT teacher head0.252
Teacher spread0.221 · 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.

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

Citations6
Published2016
Admission routes1
Has abstractyes

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