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Record W2073972864 · doi:10.5539/mas.v3n5p46

The Testing Method of the Co-Movement of A+H Stock Prices

2009· article· en· W2073972864 on OpenAlexvenueno aff
Xiang Jing, Susheng Wang, Jiaji Hao

Bibliographic record

VenueModern Applied Science · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAugmented Dickey–Fuller testEconometricsStock (firearms)Granger causalityEmpirical researchEconomicsTest (biology)Financial economicsStatisticsMathematics

Abstract

fetched live from OpenAlex

In this paper, the technical methods utilized for testing the co-movement of A+H stock prices of dual listed companies were analyzed through co-integration test, the Granger causality test, variance decomposition and Geweke indicators. Currently, empirical research on A+H stock markets are mainly based on two perspectives: empirical analysis of international asset price equalization theory and empirical analysis of measurement technology. Commonly there are four base unit test methods: Dickey-Fuller test, Augmented Dickey-Fuller test, Phillips and Perron test and KPSS test. These indicators and methods are then analyzed and evaluated. Research has not yet found an ideal approach to link A+H stock markets, although the above mentioned methods complement each other. In short, these test theories for development are only used in practice. With expanding research, in the future, there will be more powerful theoretical tools for empirical analysis.

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)0.007
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.046
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.047
GPT teacher head0.262
Teacher spread0.214 · 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 designSimulation or modeling
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

Citations5
Published2009
Admission routes1
Has abstractyes

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