Ownership structure, earnings management and acquiring firm post‐merger market performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the link between ownership structure, earnings management (EM) preceding mergers and acquisitions (M&A) and the acquiring firm's subsequent long‐term market performance. Design/methodology/approach The authors measure the magnitude of discretionary current accruals using two methodologies, that of Teoh et al. and that of Kothari et al. The latter methodology is used to control for the presence of extreme performance prior to the event. The calendar‐time Fama‐French three‐factor model was used to evaluate long‐term stock performance and to minimize potential problems related to the cross‐sectional dependence of the returns. Findings It was found that firms using stock as a financing medium exhibit significant positive discretionary accruals during the year preceding the M&A and during the year of the acquisition. It was also documented that voting right concentration and control‐enhancing mechanisms are not associated with any significant level of earnings management. Finally, a negative association was found between EM and abnormal stock returns over a three‐year period following the acquisition. Research limitations/implications These results suggest that the concentrated ownership alignment effect dominates the entrenchment motives and acts as a deterrent mechanism to prevent controlling shareholders from managing earnings in stock‐financed M&A. Practical implications The authors’ results highlight the importance of maintaining good legal and extra‐legal protection of minority shareholders. Regulators can play an important role in preventing dominant shareholders from engaging in opportunistic EM in stock‐financed M&A. Originality/value The paper extends prior literature by taking a closer look at dominant shareholders’ motivations to manage earnings in stock‐financed M&A. Large shareholders have strong incentives to manage earnings upward prior to stock‐financed transactions to limit the dilution of their controlling position.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".