AGGREGATE SIZE MEASURES OF MERGER MARKET: EMPIRICAL EVIDENCE FROM POLAND, 2002-2013
Bibliographic record
Abstract
Merger and acquisition activity is very important economic phenomenon often leading to a permanent organizational changes of single industries or even entire economies. Theoretical part of this article is an attempt to define aggregate size measures which allow gaining quantitative view on its dimensions. Four measures are proposed to assess the size of a merger and acquisition market, namely: announced, backlog, completed and withdrawn volumes. Relationship between these measures is introduced. Their accuracy is dependent on assumed transaction and registration announcement definitions. Limitations of the research based on the commercial vendors’ datasets (for example Thomson Reuters) are presented. In order to overcome these limitations, alternative data collection methodology for merger transactions is derived from legal consolidation procedure defined in The Code of Commercial Partnerships and Companies. This approach allows collecting the information about 3870 merger transactions which have taken place in the period between 1st January 2002 and 31st December 2013 in Poland. Announced, backlog and completed volumes are calculated quarterly. All these quantitative measure exhibit strong seasonality. Besides, their stable growth on Polish market was observed from 2002 till 2011. After 2011 this trend has reverted, but rebound of the backlog volume in the second quarter of 2013 suggests that at least completed volume levels should be higher in the upcoming quarters.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".