Reputation and self-regulation in securities markets: A study of the London Stock Exchange's Alternative Investment Market (AIM)
Bibliographic record
Abstract
The continual debate over the appropriateness of self-regulation in securities markets has largely focused on North American self-regulatory organizations (SROs). Proponents and detractors typically advocate for more or less ‘regulation’, which is taken to mean legislation or administrative agency rule-making backed by public enforcement. This thesis adds to the debate by conducting a case study of an often-overlooked securities market, AIM, which began in 1995 as the Alternative Investment Market of the London Stock Exchange. This thesis conducts a holistic analysis of AIM. It begins with an analysis of black-letter law and legally enforceable regulation, but it does not end there. It continues by gathering evidence of market practice, regulation ‘off the books’, and how private rule-making on AIM has evolved over time, bringing to light how AIM has significantly changed since its 2007 heyday. The main contribution of this thesis is to provide empirical evidence and analysis of 25 years of self-regulation on AIM, which is operated and regulated by the London Stock Exchange plc (Exchange). AIM, despite boasts as ‘the world’s largest growth market’, has received little serious legal scholarly treatment in the past decade. A second contribution is to demonstrate how reputational incentives and informal regulation, such as norms and unwritten rules that are imposed by both local market participants and the Exchange as private regulator, constitute an integral part of securities regulation and profoundly influence market conduct. Taken as a whole, this thesis seeks to shift the focus from calls for more or less regulation, and instead emphasizes the need for contextual inquiry of how reputation and informal regulatory mechanisms contribute to self-regulation in any securities market, given its public regulatory environment and private rule-making incentives.
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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 | 0.004 | 0.008 |
| 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 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".