Development of new approaches to forecasting and evaluating the effectiveness of mergers and acquisitions
Bibliographic record
Abstract
The article touches upon the problem of inconsistencies and insufficient accuracy of quantitative methods applied in analyzing mergers and acquisitions. The authors point out that the criteria for the effectiveness of the concluded deals used by the majority of researchers do not conform to the real factors which are not taken into consideration by companies’ top managers when making decisions about a deal. This results in the gap between theoretical developments and their practical application and also limits the implementation of scientific results in practice. The authors study and identify the main disadvantages of the most common analysis methods: Cumulative Abnormal Returns, or CARs, Buy and Hold Abnormal Returns, or BHAR, and also, they suggest hypotheses of medium-term profitability and long-term payback illustrating the criteria of predicting the success of mergers and acquisitions from the viewpoint of management. The authors have developed new approaches to statistical analysis of the market of mergers and acquisitions that allow to identify the deals the results of which differ significantly from the average in one direction or another. Further development and empirical testing of the suggested approaches can provide for the introduction of new techniques of evaluation of effectiveness of mergers and acquisitions, as well as for creation of sectoral and universal recommendations for top managers of the buyer companies on their basis.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".