MétaCan
Menu
Back to cohort
Record W7019114647

Estrategia para fortalecer la negociación búrsatil del mercado MILA.

2021· article· es· W7019114647 on OpenAlexaboutno aff

Bibliographic record

VenueBiblioteca Digital Repositorio Institutional CESA (Colegio de Estudios Superiores de Administración) · 2021
Typearticle
Languagees
FieldBusiness, Management and Accounting
TopicBusiness, Education, Mathematics Research
Canadian institutionsnot available
FundersCommercializations Promotion Agency for R and D OutcomesHenan Institute of Science and Technology
KeywordsQuarter (Canadian coin)Economic model
DOInot available

Abstract

fetched live from OpenAlex

En los mercados financieros la colocación y negociación de activos de renta variable se considera imprescindible para su crecimiento y desarrollo. Ciertamente los activos financieros no son ajenos a variaciones de precio, debido a fenómenos de volatilidad y riesgo que afectan su rentabilidad, estos efectos son típicos de los mercados desarrollados y emergentes. Además de estos efectos, los activos son sensibles a los sucesos inesperados que impactan en las decisiones de inversionistas y especuladores que se integran a través de agentes financieros e intermediarios de capital. En el marco del desarrollo de los mercados financieros para América Latina, se encuentra que el Mercado Integrado Latinoamericano MILA, representa una alianza estratégica, la cual figura como un medio visible para los países de Chile, Colombia, México y Perú. No obstante, esta alianza no ha tenido la suficiente promoción y desarrollo para incentivar su modelo de negociación y atracción de inversión de capitales extranjeros. En consecuencia, se realiza un análisis de los fundamentales macroeconómicos de estos mercados en aras de obtener la sintomatología de los mismos y así proponer una estrategia de inversión basada en la teoría de portafolios de Harry
\nMarkowitz.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.009
Science and technology studies0.0020.001
Scholarly communication0.0170.008
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.032
GPT teacher head0.299
Teacher spread0.267 · 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.

Study designObservational
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

Explore more

Same venueBiblioteca Digital Repositorio Institutional CESA (Colegio de Estudios Superiores de Administración)Same topicBusiness, Education, Mathematics ResearchFrench-language works237,207