THE GLOBAL MERGERS AND ACQUISITIONS MARKET: STATUS AND FEATURES OF TRANSFORMATION
Bibliographic record
Abstract
Objective. The objective of the present study is to analyse the state and peculiarities of the transformation of the global M&A market in 2013-2022. Methods. The following methods and techniques of cognition were used: analysis and synthesis, induction and deduction (to substantiate the importance and role of the study of the status and features of the global M&A market), generalisation and systematisation (to identify the main indicators of the global M&A market), analysis of time series (to identify trends and patterns of development, features of transformation of the global M&A market in 2013-2022), graphical (for a visual representation of the peculiarities of the global M&A market development). Results. Based on the analysis of the data of the United Nations Conference on Trade and Development (UNCTAD), the author found that in 2013-2022 the global M&A market was characterised by dynamic development, in particular, an increase in the aggregate value and number of M&A transactions concluded globally; significant structural imbalances in development; significant variability and growth in the average value of M&A transactions observed in different groups of countries and different sectors of the economy. Thus, it is established that in 2013-2022 there was an increase in the total value of M&A transactions concluded in the world - the total value of M&A transactions concluded in the world increased by more than USD 444 billion and amounted to USD 706.6 billion as of 2022. Increase in the number of M&A transactions - the number of M&A transactions in the world increased by 2827 transactions to 7763 transactions as of 2022. The article establishes that the most dynamically growing structural element of the global M&A market is the relevant market of developed countries. Developed countries are significantly ahead of developing countries both in terms of value and quantity of M&A transactions. In macro-regional terms, the highest aggregate value and number of M&A transactions were characteristic of Europe (on average, 43.2% of the aggregate value of M&A transactions in the world, 50.1% of the total number of M&A transactions in the world), in particular the European Union (on average, 23.9% and 33.9%, respectively); Americas, in particular North America (on average 33.4% and 22.8%, respectively); Asia (on average 11.3% and 11.5%, respectively), in particular East and South-East Asia (on average 6.98% and 6.7%, respectively).In terms of countries, the United Kingdom, the United States, Australia, Sweden, France, Canada, Switzerland, Saudi Arabia, the Netherlands, China, Spain, Germany and Denmark accounted for the largest aggregate value and number of M&A deals. These countries accounted for more than 50% of the total value and number of M&A transactions. In terms of sector, the tertiary sector of the economy accounted for the largest total value and number of M&A transactions. On average, the tertiary sector accounted for 51.1% of the total value and 70.4% of the total number of M&A deals concluded globally. In terms of industry sectors, the largest aggregate value and number of M&A transactions were in the following industries: information and communications; mining; finance and insurance; transportation and storage; pharmaceuticals, electronics and electrical equipment; trade; professional services; food, beverages and tobacco; and real estate. On average, these industries accounted for more than 70% of the total value and number of M&A transactions. It was determined that the average value of M&A transactions in the world increased by an average of USD 37.8 million and amounted to USD 91 million in 2022. The average value of M&A deals in emerging markets was higher than the average value of the corresponding deals both globally and in developed countries. The average value of M&A transactions in the primary sector of the economy is significantly higher than the value of M&A transactions in other sectors of the economy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".