THE IMPACT OF CORPORATE GOVERNANCE PRACTICES ON POST-MERGER PERFORMANCE
Bibliographic record
Abstract
The relation between the post-merger performance and corporate governance mechanism is examined using Linear regression model for a sample of US, Canada, EU-28 and Western European countries listed firms for the period from 2003 to 2012. We also examine as to whether the use of International Financial Reporting Standards (IFRS) improves corporate transparency, therefore, increasing financial reporting quality. Using a sample of banks from international countries, we present the following key findings: post-merger performance is significantly better common law in countries than code law in countries with better IFRS group banks during the post-merger performance. We also find that local GAAP reporting allows a more transparent assessment of financial performance on the basis of traditional indicators making it a superior tool for assessing potential acquisition targets. This study analysis changes in a country legal regulation as a measure of corporate governance and shows that these regulations play an important role in merger activity. Legal origins and owner-protection mechanisms are important in explaining the relationship between the quality of accounting standards and corporate governance practices following IFRS adoption. Overall, our empirical findings result consistent with Ciobanu (2015) find that merger and acquisition is influenced both the legal origin and accounting regulations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".