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Record W2980337735 · doi:10.5430/ijba.v10n6p14

An Analysis of Stock Return Transmission in North and Latin America

2019· article· en· W2980337735 on OpenAlexvenueaboutno aff
Chikashi Tsuji

Bibliographic record

VenueInternational Journal of Business Administration · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicStock Market Forecasting Methods
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceChuo University
KeywordsStock (firearms)Latin AmericansStock marketEconomicsVector autoregressionFinancial economicsEconometricsGeographyPolitical science

Abstract

fetched live from OpenAlex

This study empirically examines the return transmission effects between the four North and Latin American stock markets in the US, Canada, Brazil, and Mexico. More specifically, applying a standard vector autoregression (VAR) model, we obtain the following interesting findings. First, (1) the return transmission effects between the four North and Latin American stock markets became much tighter in our second subsample period. Second, (2) in particular, US and Mexican stock markets are strong return transmitters in the recent period. Furthermore, (3) both in our first and second subsample periods, Brazilian stock returns do not transmit to the other three stock returns, although the other three North and Latin American stock markets affect the Brazilian stock market.

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)0.001
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.405
Teacher spread0.338 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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