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Record W2620234624

On the Minimization of Regulatory Margin Requirements for Portfolios of Financial Securities

2011· dissertation· en· W2620234624 on OpenAlexvenueaboutno aff
Justin D. Toupin

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2011
Typedissertation
Languageen
FieldComputer Science
TopicArtificial Intelligence in Games
Canadian institutionsnot available
Fundersnot available
KeywordsMargin (machine learning)BusinessActuarial scienceComputer scienceMachine learning
DOInot available

Abstract

fetched live from OpenAlex

A margin account is a type of brokerage account that allows investors to buy and sell financial securities using credit. The account’s margin requirement is the amount of collateral required, from the investor, to cover the funds or securities extended by the broker to the investor. In Canada, the primary driver of an account’s margin requirement is the account’s Capital Charge [CC] which is calculated using a set of regulatory rules. The regulations are degenerate in that hundreds of valid CCs often exist for a single account. This work outlines a linear optimization model for selecting the minimal CC out of the set of valid CCs for a given margin account. The method proposed is consistent with all of the regulatory requirements and is guaranteed optimal in most cases. Relative to existing methods, the new method produced an average CC reduction of approximately 2% and displayed qualitatively better run-times.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.180
Teacher spread0.170 · 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 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

Citations0
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueLibrary and Archives Canada (Government of Canada)Same topicArtificial Intelligence in GamesFrench-language works237,207