The Impact of the UK Corporate Governance Code 2010 on Earnings Management around Mergers and Acquisitions
Bibliographic record
Abstract
Several studies revealed earnings management (EM) around mergers and acquisitions (M&As) by both acquirers and target firms. Rosa et al. (2003) suggest that a systematic EM is associated with the use of stock as payment in takeovers. This and other corporate malpractices have prompted authorities to tighten regulations by passing the United Kingdom (UK) Corporate Governance (CG) Code to guide companies in the UK in their corporate management and financial reporting. This study is to investigate the impact of the UK CG Code on accruals EM around M&As in the UK. The study applied the Modified Jones (1991) model as modified by Dechow et al. (1995) and the Pearson Product Moment Correlation in analysing a sample data from 66 companies listed on the LSE that have undertaken M&As within the period of January 2007 to December 2014. The results produced by the modified Jones model indicate some level of income increasing discretionary accruals in the pre-CG period but showed an opposite situation in the post-CG period. A test for significance indicates the means of pre-CG discretionary accruals and post-CG discretionary accruals were different and significant. The hypothesis that “the level of earnings management around mergers and acquisitions in the UK has significantly reduced after the enactment of the UK Corporate Governance code 2010” was therefore accepted. Results from the Pearson Correlation Coefficient were inconclusive on EM but indicate some changes in the level of activities in the earnings between the two periods. This may also points to some effect of CG Code on the reported earnings of these companies. The results from this study is consistent with existing studies that evince the effectiveness of CG in controlling EM as Hsu and Koh (2005); Osma (2008) suggest that best corporate governance practices minimise EM and reduce fraud drastically.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".