The Reciprocal Relationship Between Earnings Management, Disclosure Quality and Board Independence: UK Evidence
Bibliographic record
Abstract
We empirically examine the reciprocal relationships between disclosure quality, board independence and earnings management. Disclosure quality is measured using the IR Magazine Award, the number of forward looking information in the annual report as well as the analyst forecast accuracy. We estimate earnings management using modified Jones Model, while board independence is measured using the percentage of independent directors in the board. We remedied the simultaneity bias in our study using a simultaneous system of equation, which was estimated using two-stage least square regression (2SLS). Match-paired samples comprised of the winners and non-winners of the IR Magazine Award during the years from 2005-2008 were employed in our study. Our finding reported that there is a negative reciprocal relationship between disclosure quality and earnings management. We notice that these findings are robust across all disclosure quality measurement that we utilised in our 2 Stage Least Square (2SLS) regression. Only one way (negative) causality between board independence and earnings management is demonstrated (in the board independence equation). In regards to disclosure quality and board independence, we found mixed findings. In this instance, our result demonstrated that there is no reciprocal relationship between disclosure quality and board independence (measured using IRAWARD). Nonetheless, we reported a positive reciprocal relationship between board independence and disclosure quality when forward looking information is utilized as to represent disclosure quality and a negative relationship between these variables when analyst forecast accuracy is employed. Our finding suggests that future research should take into account the potential simultaneity bias when examining the relationship between disclosure quality, earnings management and board independence.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".