The Impact of Buyouts on Swedish Portfolio Companies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".