Earnings Management before a Share for Share Bid under IFRS
Bibliographic record
Abstract
Previous studies have shown that acquirers tend to manage earnings before the share swap transactions. According to Erickson and Wang (1999), Louis (2004) and Botsari and Meeks (2008), earnings are managed the most often through working capital accruals a quarter or even a year before the share bid. International Financial Reporting Standards (IFRS) became mandatory in the European Union in 2005. Some researchers have stated that there are less earnings management, more timely loss recognition and less discretionary accruals under IFRS than before under national accounting standards. Thus, quality of earnings is better under IFRS. However, there are reversed findings too. \n\nIn this thesis it was studied whether Finnish listed acquirers have managed earnings before the share for share bid announcement and how the implementation of IFRS has influenced the issue. The data consisted of firms that had made a successful share for share bid between 2001 and 2008. The deals made under Finnish Accounting Standards (FAS) and under IFRS were compared. The modified Jones model was applied to measure discretionary accruals of the acquirers. Discretionary accruals imply the amount of managerial discretion used in financial statement, and therefore, quality of earnings.\n\nThe results of the Jones model indicate that firms seem to manage earnings slightly over a quarter before the share for share bid announcement mainly through working capital accruals. Earnings persistence analysis provided a finding that firms seem to prefer smooth development in net income during the last four interim financial statements before the share bid. Moreover, the results stated that there are no statistically significant differences in the levels of discretionary accruals between FAS-sample and IFRS-sample. The findings suggest that quality of earnings has not increased significantly after IFRS implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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; both teacher heads agree on what is shown here.
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".