A Study of Association between Taiwan’s Corporate Governance Appraisal and Financial Performance: Evidence from Taiwan Listed Companies
Bibliographic record
Abstract
To enable all listed companies to gradually upgrade and implement corporate governance, the appraisals are promoted to assist investors through the comparison of corporate governance appraisal (CGA) in Taiwan’s market. Using a panel data is based on the companies listed on the Taiwan Stock Exchange during the period 2014-2016; this paper provides evidence that earnings management is affected negatively by corporate governance quality. This is expected to guide healthy competition between enterprises and strengthen corporate governance. Recent studies have pointed out that managers are more favorable to their actions due to weak corporate governance. While most studies explored the relationship between corporate governance and financial performance, few studies have included in corporate governance appraisal (CGA). This study examines how CGA in Taiwan listed companies will affect their earnings quality and this study uses earnings management (EM) as measure of financial performance. In addition, reference is made to the Big 4 accounting firms to explain the consequences of CGA and, specifically, its effect on the quality of financial statements. The empirical results show that CGA and earnings management have a significantly negative correlation. In addition, the CGA of companies audited by the Big 4 indicate that those with better earnings quality also conduct less earnings management.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".