The Challenges and Contemporary Issues of Taiwan's Investor Protection System: A Model to Learn or to Avoid
Bibliographic record
Abstract
In January 2003, the Securities and Futures Investors Protection Center (SFIPC), an NPO and NGO, was established according to the Securities Investor and Futures Trader Protection Act (SFIPA), enacted in July 2002. The SFIPC was funded by the securities and futures industries. The creation of the SFIPC and the recent securities law reforms aim at enhancing the corporate governance and the investor protection systems. This article provides a comparison of the investor protection funds in Taiwan, Canada, China, Singapore and the U.S. In addition, special functions of the SFIPC are bringing class actions and derivative suits on behalf of investors and listed companies that suffered damages from securities fraud and other market misconducts. Since its establishment, the SFIPC has filed more than 200 class actions as of December 2015. The SFIPC can also bring derivative suits against corporate directors or to ask the court to remove unsuitable corporate directors. The investor protection institutions in other jurisdictions, such as Securities Investor Protection Corporation in the US and Canadian Investor Protection Fund, do not have these functions. This article introduces special features of the SFIPC. It also identifies and analyzes the contemporary issues regarding the investor protection system. Particularly, the SFIPC has been criticized for its conflict role in serving as an agent of the government, conducting compulsory mediation, bringing class actions and removing directors. Moreover, what are the duties of the SFIPC in bringing litigation and settling the cases and who are supervising its performance to ensure no breach of duties or abuse of its power? Furthermore, future challenges to the SFIPC will be discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".