AGGREGATE SIZE MEASURES OF MERGER MARKET: EMPIRICAL EVIDENCE FROM POLAND, 2002-2013
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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measures are proposed to assess the size of a merger and acquisition market, namely: announced, backlog, completed and withdrawn volumes.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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it