Performance Analysis of Multinational M&A of Listed Companies--Based on Zipf Stock Price-Fundamentals Dynamic Evaluation Model’s Construction
Bibliographic record
Abstract
In this paper, the multinational M&A of listed companies are taken as the research objects. The Stock Price-Fundamentals Dynamic Evaluation Model (SF-DEM) based on Zipf’s law is constructed. Through the complementary cumulative distribution, we analyze the stock price and fundamentals distribution and changes of listed companies before and after multinational M&A. The two-way fixed effect model is introduced to verify the SF-DEM model. The research shows that multinational M&A have got a positive response from the market. Short-term average cumulative abnormal rate of return reached 2.8% after the announcement day, which had a long-term synergy and promoted companies’ value; As investors expected that high-priced stocks have a poorer growth and higher thresholds, multinational M&A have a significant and lasting effect on the low-priced companies, while the impact on high-priced stocks is small and short; 80% of the price changes were determined by fundamentals, and reached 85% after M&A. Multinational M&A enhances investors’ expectations and then shows the value investment trends. 80/20 law is not affected by M&A, companies have strong heterogeneity and large differences.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".