Analysis of Corporate Mergers and Acquisitions: Evidence from Baidu's Acquisition of YY Live
Bibliographic record
Abstract
Contemporarily, the live streaming business has experienced exponential growth and appeal. After recognizing the enormous potential of the live streaming sector, Baidu pursued the strategic acquisition of YY Live. This article analyzes the case of Baidu’s merger and acquisition of YY live. First of all, it introduces the related research and theory about the financial performance before and after acquisition. Then, we choose Baidu with its acquisition of YY live. To be specific, this study introduces the merger and acquisition process in a brief way, using event research method and financial index method to analyze the motivation of Baidu merger and acquisition of YY live and Baidu’s corporate financial performance change after the implementation of merger and acquisition strategy. It is found that the acquisition behavior has improved the profitability of the company, has no obvious impact on the solvency, has a negative impact on the operating ability and improves the development ability. Finally, we draw the conclusion, and put forward corresponding suggestions for enterprise merger and acquisition. These results have certain reference value and guiding significance for researchers in live broadcasting industry and enterprise acquisition.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".