The development and regulation of robo-advisors in Hong Kong: empirical and comparative perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In July 2019, Hong Kong issued a comprehensive regulation on robo-advisors in the capital markets, with a view to striking a balance between investor protection and innovation promotion. This regulation has generated some noticeable positive impact on the market, but some problems remain to be addressed. It is critically assessed by way of a comparison with the laws in the US, the UK, Singapore, and Mainland China. It has strengths in terms of regulatory form and approach, the comprehensiveness of guidance provided and clear allocation of responsibility. There are also several weaknesses and challenges. Firstly, greater efforts need to be made to ensure proper compliance and enforcement. Secondly, more attention should be paid to the increasing complexity of algorithms. Thirdly, the issue of outsourcing arrangements requires further thoughts. Finally, Hong Kong should consider a staged reform on its financial regulatory architecture to address the issue of regulatory arbitrage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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 it