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Record W2560308623 · doi:10.29015/cerem.214

AGGREGATE SIZE MEASURES OF MERGER MARKET: EMPIRICAL EVIDENCE FROM POLAND, 2002-2013

2016· article· en· W2560308623 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Central European Review of Economics and Management · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Fiscal Studies
Canadian institutionsnot available
Fundersnot available
KeywordsConsolidation (business)Database transactionAggregate (composite)Order (exchange)BusinessEconomicsQuarter (Canadian coin)EconometricsIndustrial organizationAccountingComputer scienceFinanceDatabase

Abstract

fetched live from OpenAlex

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measures are proposed to assess the size of a merger and acquisition market, namely: announced, backlog, completed and withdrawn volumes.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.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.401
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.081
GPT teacher head0.229
Teacher spread0.148 · 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