The Role of Corporate Governance on the Relationship Between IFRS Adoption and Earnings Management: Evidence From Bangladesh
Bibliographic record
Abstract
Purpose: This study investigates the relationship between IFRS adoption and earnings management (EM) i.e. discretionary accruals (DA) and real earnings management (REM) in developing economy like Bangladesh. Moreover, the study examine the relationship between corporate governance (CG) strength and EM as well as moderating role of CG strength on the relationship between IFRS adoption and EM.Design/methodology/approach: The study employs 94 firms listed in Dhaka Stock Exchange (DSE) for 6 years i.e. 564 firm years observation, over two time period as pre (2004-06) and post (2013/14-15/16) adoption of IFRS. Underpinning theory of the study is agency theory which explained the relationship among variables. Based on earlier literature a CG index is developed to measure the strength of CG. The study uses random effect GLS with robust regression in a balanced panel data.Findings: The results show that IFRS and CGI both have significant negative relationship with EM. Moreover, it is documented that the CG strength significantly moderates the relationship between IFRS and REM. It implies that the presence of good CG may help to attain the objectives of IFRS adoptionOriginality/value: To the best of the author’s knowledge, this is one of the first empirical attempts at providing evidence about the role of CG on the relationship between IFRS adoption and EM in Bangladesh. The findings of this study can be beneficial for the member of the regulatory bodies and researchers to formulate new policy and enhance corporate governance practices in Bangladeshi companies as well as develop a better framework for all stakeholders involved in financial reporting. Future studies may also investigate the interacting effect of corporate governance strength on other related variables which may influence the level of earnings management.
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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 | 0.002 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".