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Record W3198736480 · doi:10.55016/ojs/sppp.v7i1.42491

Muddling up the Market: New Exempt- Market Regulations may do more Harm than Good to the Integrity of Markets

2014· article· en· W3198736480 on OpenAlexaffabout
Jack Mintz

Bibliographic record

VenueThe School of Public Policy Publications · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHarmBusinessAccountingLawPolitical science

Abstract

fetched live from OpenAlex

From private debt and equity markets to crowd funding, exempt markets have been used to raise more money for Canadian enterprises in recent years than all public offerings put together. Vastly more: Between 2010 and 2012, exempt-market offerings raised four times as much capital as the initial and secondary public offerings during the same period. The precise reasons behind the immense popularity of exempt markets can only be guessed at; it may well be due to the desire, by both issuers and by investors, to avoid the regulatory costs associated with raising capital in public markets. We are left to speculate, however, because the Canadian exempt market remains relatively unstudied, despite its enormous role in funding capital investments in Canada. The lack of information about exempt markets, however, is not stopping provincial regulators in Canada’s largest markets from charging ahead with new proposals for rules that would govern exempt markets. Unfortunately, with so little information available about these markets, whatever the aim of the reforms in pursuing the goals of effective market regulation, they may end up being more harmful than helpful. Ontario is proposing to broaden the category of investors eligible to participate in these markets under a new exemption. But the category will remain stricter than in many other markets and Ontario proposes to also put very low limits on how much each investor is allowed to put at risk. Quebec, Alberta and Saskatchewan are also proposing the same $30,000 limit for any given 12-month period. And Ontario will prohibit the sale of exemptmarket securities by agents that are related to, or affiliated with, the registrant, even if measures are employed that have previously been accepted in managing and mitigating conflicts of interest. This will have a direct and damaging impact on exempt-market dealers, who are only allowed to sell exempt-market securities. All of these proposals are intended to protect investors from the higher risks that are presumed of exempt markets. However, there is no evidence — given the paucity of information about them — that exempt markets necessarily pose a greater risk of fraud or poorer returns and losses than do heavily regulated public markets. And if risk is indeed higher in the exempt markets, one would expect these proposed regulations to assist highquality firms from distinguishing themselves in the exempt market from low-quality firms. However, these regulations may actually have the opposite effect, making it harder for better-quality firms to signal their worthiness to investors. Canadian productivity — which continues to lag relative to other developed economies — relies heavily on businesses being able to acquire capital for investing in new technologies. Canadian companies and investors appear to be voting with their feet for exempt markets in raising that capital, possibly discouraged from public markets by regulatory costs and inefficiencies. For policy-makers to layer additional regulation on top of exempt markets without fully understanding the impact that it will have, could well result in making Canadian markets, and Canada’s economy, weaker, rather than stronger.

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.681
Threshold uncertainty score0.642

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.040
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0110.020
Scholarly communication0.0230.012
Open science0.0030.004
Research integrity0.0100.014
Insufficient payload (model declined to judge)0.0140.002

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.

Opus teacher head0.040
GPT teacher head0.301
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2014
Admission routes2
Has abstractyes

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