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A Study on Transmission Mechanism of Financial Supervision with Chaos Theory

2009· article· en· W1912920761 on OpenAlexvenueno aff
Jin-guang Wu, Li Ma

Bibliographic record

VenueCanadian social science · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOrder (exchange)Financial institutionMathematicsEconomicsMathematical economicsFinance

Abstract

fetched live from OpenAlex

The linear rule was a general mode in the analysis of market order and financial supervision, but it frequently caused serious deviation of the market order from the supervision goal. This paper was devoted to solve the deviation with chaos model. Firstly, it substituted the operating condition of the financial institution for market order, and defined the supervision degree as a function of effective rules and realizable level. Secondly, it taked these indexes into Logistic Equation to calculate the fixed points, and simulated the complex changes of market order. Thirdly, it analyzed the characteristics of the stable numerical solution in the inter-temporal differential equation. Lastly, it explained the foresight, the exogenous, the carefulness, and the time lags of the financial supervision with chaos control theory. It deduces that when the supervision degree falls into [3, 3.554], the supervision can lead to a well-ordered market. Key words: nonlinear control, market order, financial supervision, transmission mechanism Resume: La regle lineaire etait un mode general dans l'analyse de l'ordre de Bourse et de la supervision financiere, mais elle a frequemment cause la deviation serieuse de l'ordre de Bourse du but de surveillance. Cet article a ete consacre pour resoudre la deviation avec le modele de chaos. D’abord, il a substitue la condition de fonctionnement de l'institution financiere a l'ordre de Bourse, et a defini le degre de supervision en fonction des regles efficaces et du niveau realisable. Deuxiemement, il a pris ces index dans l'equation logistique pour calculer les points fixes, et a simule les changements complexes de l'ordre de Bourse. Troisiemement, il a analyse les caracteristiques de la solution numerique stable dans l’equation differentielle intertemporelle. Finalement, il a explique la prevoyance, l'exogene, l'attention, et les laps de temps de la supervision financiere avec la theorie de controle de chaos. Il deduit que quand le degre de supervision tombe dans [3, 3.554], la supervision peut mener a un marche bien-commande. Mots-Cles: controle non-lineaire, ordre de Bourse, supervision financiere, mecanisme de transmission

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.021
GPT teacher head0.213
Teacher spread0.192 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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