D&O Insurance, Corporate Governance and Mandatory Disclosure: An Empirical Legal Study of Taiwan
Bibliographic record
Abstract
Abstract The purpose of this paper is to test the signal effect of Directors and Officers (D&O) insurance and to analyze the necessity of mandatory disclosure of D&O insurance in Taiwan. D&O insurance is usually viewed as a signal mechanism of insured firms’ corporate governance and thus its mandatory disclosure has been argued. However, there is no complete mandatory disclosure of D&O insurance in the United States and other countries. This issue is not only popular in common law worlds but also sprouting in civil caw jurisdictions such as Taiwan. In the first part of this research, the signal effect of D&O insurance in Taiwan will be empirically tested. The evidence suggests that the information about D&O insurance in Taiwan could statistically and significantly signal the qualities of corporate governance of insured firms. Then, this study addresses the mandatory disclosure of D&O insurance by comparative law and law & economic approaches. This paper compares the regulation about D&O insurance disclosure in the United States, Canada and Taiwan, and find out the reasons affecting the mandatory disclosure of D&O insurance. The Cost and benefit analysis is also applied to discuss whether or not the Canadian mandatory disclosed system should be transplanted. It concludes that the D&O insurance can signal the information of insured firms’ corporate governance, and mandatory disclosure is required and justified. Such interdisciplinary research will provide through recommendations for the Taiwan and other emerging countries in Asia.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".