Does Ownership Structure Improve Financial Reporting Quality? Evidence of Real Earnings Manipulation among Pakistani Firms
Bibliographic record
Abstract
This paper examines whether ownership structure improve the financial reporting quality. We built on two different econometric techniques including Feasible Generalized Least Square (FGLS) and Panel Corrected Standard error Model (PCSE) by using a sample of 150 non-financial firms listed on Pakistan Stock exchange for the period of 2008-2017. The results propose that institutional ownership and as well as managerial ownership are negatively related to real earnings manipulation, which implies that both these types of ownership structure act as a best monitoring mechanism in reducing real earnings manipulation and thus enhancing the financial reporting quality. Whereas, state ownership and family ownership are positively associated to real earnings manipulation, which suggest that family and state ownerships engage in real earnings manipulation and thus reducing the financial reporting quality. Overall results supports the alignment hypothesis, entrenchment effect and efficient monitoring hypothesis of the agency theory. The results of the study provide practical implication for investors and policymakers in understanding the role of ownership on financial reporting quality. Keywords: Ownership Structure; Financial Reporting Quality; Real Earnings Manipulation; Agency Theory
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".