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Record W2189400932

The Impact of Buyouts on Swedish Portfolio Companies

2012· article· en· W2189400932 on OpenAlexaboutno aff
Daniel Mork, Joel Pettersson, Andreas Stephan, Louise Nordström

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsnot available
Fundersnot available
KeywordsPortfolioPrivate equityLeveraged buyoutBusinessPrivate equity firmEquity (law)Market valueFinance
DOInot available

Abstract

fetched live from OpenAlex

The private equity market has been a hot topic of debate since it first started to emerge. As it has become more frequently apparent in the Swedish economic and financial market, the debate regarding its impact in Sweden has also risen. The debate contains various opinions whether the private equity firms, through buyouts uses tax breaks or arbitrage opportunities to “quick flip” the investments on the expense of workers and wages or if the PE firms create operational and economic value. Previous studies regarding this topic have mostly been conducted in the U.S, Canada or to some extent in the U.K. This paper aims to, through a quantitative method, investigate and present whether the buyouts have an impact on Swedish portfolio companies or not, and if they do, what kind of impact the buyouts have. This will be done through an event study, based on data collection from portfolio companies that have experienced a buyout made by a private equity firm. The data will then be compared with data from peer groups and analyzed through statistical methods in SPSS. The main findings in this thesis suggest that buyouts do not have, neither a positive nor negative impact on the performance of portfolio companies in Sweden. The tests allow us to conclude that PE firms do not have market timing skills. Neither can we conclude that PE firms cause layoffs of workers or lowering of wages.

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)0.001
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 score0.205
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.072
GPT teacher head0.361
Teacher spread0.289 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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