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Record W4413244033 · doi:10.3138/ccar.v17i1.053

The Short End of the Stick: Bolstering Legal Protections for Short Sellers in Ontario’s Secondary Market

2021· article· en· W4413244033 on OpenAlexaboutno aff
Jacob Medvedev

Bibliographic record

VenueCanadian Class Action Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsIssuerRedressSecondary marketBusinessPosition (finance)EnforcementStatuteInvestment bankingSecurity marketLawFinancePolitical science

Abstract

fetched live from OpenAlex

Abstract: In this paper, the author surveys Ontario’s secondary market civil liability framework. The author reviews the constituent continuous disclosure obligations as well as the enforcement mechanisms that are available under common law and statute. The author then explores how short sellers fit into Ontario’s secondary market securities laws. Avenues of legal recourse have seemingly crystallized for ordinary investors who are misled by reporting issuers in the secondary market. However, Ontario’s securities laws are unclear regarding the legal redress that is available to aggrieved short sellers who are resigned to a similar fate. To address this gap, the author argues in favour of strengthening legal protections for short sellers by: (1) recognizing a duty of care owed by public issuers to short sellers; and (2) revising the damage calculation formulas in Part XXIII.1 of Ontario’s Securities Act to ensure that they are capable of compensating short sellers in a manner that is commensurate with their investment position. In doing so, Ontario could better position itself as a robust securities market that provides adequate legal safeguards for diverse types of investors. Moreover, the implementation of remedial measures for short sellers may create market conditions that encourage the spread of negative information about stocks, paving the way for greater accuracy in the price discovery of shares and bolstered market efficiency overall.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.059
GPT teacher head0.325
Teacher spread0.266 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

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