Principles-Based Securities Regulation in the Wake of the Global Financial Crisis
Bibliographic record
Abstract
The recent global financial crisis contains cautionary lessons about the risks associated with principles-based regulation when it is not reinforced by an effective regulatory presence. Our response to the crisis, however, should not be a rush to enact more rules-based regulatory approaches. On the contrary, principles-based securities regulation offers more viable solutions to the challenges that such a crisis presents for contemporary financial markets regulation. The author draws on the lesson of the global financial crisis to identify three critical factors for effective principles-based securities regulation. First, regulators must have the necessary capacity in terms of numbers, access to information, and expertise in order to act as an effective counterweight to industry. Second, regulation needs to grapple with the impact of complexity on financial markets and their regulation. Third, increased diversity among regulators and greater independence from industry are required to avoid conflicts of interest, overreliance on market discipline, and “groupthink”. The paper calls for a continuing commitment to principles-based regulation, accompanied by meaningful enforcement and oversight.
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.
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.011 | 0.012 |
| 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".