Family Ownership, Earnings Informativeness, and Role of Audit Committees: An Empirical Investigation in India
Bibliographic record
Abstract
This paper investigates the role and impact of audit committees on the relationship between family ownership and earnings informativeness. The sample set comprises of 368 Indian firms over a period of 6 years (2007-2012) with a panel dataset of 2208 firm-years. Earnings informativeness was measured through the relationship between accounting earnings and cumulative abnormal stock returns (CAR). Audit committee independence has statistically significant positive association with earnings informativeness in India. However, family firms exhibit lower earnings informativeness compared to widely held companies. The finding indicates information asymmetry among family firms despite the presence of audit committees. This finding supports entrenchment effect and affirms the type II agency issues of dominating verses minority shareholders among Indian companies. Hence, protection of minority rights assumes tremendous importance for regulators. Audit committee size had a positive impact on the level of earnings informativeness only in widely held companies. Audit fees had a positive impact, while consulting fees paid to auditors had a negative impact on earnings informativeness. This observation lends support to recent regulations restricting auditor engagement for non-audit services. Broadly, the results support the hypothesis that audit committees strengthen earnings informativeness among Indian family firms. The findings enable cross section of stake holders to appreciate the dynamics among governance mechanisms, concentrated control and their impact on the earnings informativeness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".