Impact of Improved Corporate Governance and Regulations on Earnings Management Practices—Analysis of 7 Industries from the Indian National Stock Exchange
Bibliographic record
Abstract
This study investigates discretionary earnings management practices, tracing the changes over the years in selected top performing and highly liquid listed Indian firms. It empirically measures the impact of corporate governance, financial legislation and global reporting standards on the firms’ earnings management practices. The study analyses a sample of 712 firm-year data comprising 89 listed Indian companies across 7 different sectoral indices of the National Stock Exchange of India (NSE) over 8 years (2011–2018). The Modified Jones model was used to compute Discretionary Accruals to measure Earnings Management based on data obtained using Bloomberg terminals. Statistical results and plots generated in Stata offer evidence that instances of earnings management have significantly reduced after the enactment of the Companies Act 2013 and the adoption of Indian Accounting standards which are converged with the IFRS. Findings suggest that services firms are engaging in relatively higher levels of earnings management compared to manufacturing firms. This study reveals the positive impact of improved corporate governance, regulation, and enforcement by significantly reducing the levels of earnings management among listed firms in India.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".