Relación riesgo-rendimiento en las acciones del sector industrial de la BMV en los actuales cambios geopolíticos. | Risk-return relationship in the shares of the industrial sector of the BMV in the current geopolitical changes
Bibliographic record
Abstract
La teoria financiera asume una correlacion positiva en la relacion riesgo-rendimiento. En 2017 EUA modifica su interaccion con el mundo en todos los ambitos y se evalua la participacion del sector industrial de Mexico-EUA-Canada. En esta investigacion se analiza la relacion riesgo-rendimiento de los precios de las acciones del sector industrial de la Bolsa Mexicana de Valores durante 2006-2016 versus el 2017 considerado ano de cambios geopoliticos, se estudio 14,300 correlaciones semanales de relacion riesgo-rendimiento. Se encontro que existen companias con correlacion negativa de relacion riesgo-rendimiento y los resultados del analisis estadistico de varianza (ANOVA) demostraron que solo en algunos meses del 2017 se modifico la relacion riesgo-rendimiento y que cada subsector del sector industrial mantiene diferentes valores de riesgo-rendimiento. Abstract Given that during the year 2017 the United States (USA) modified its interaction with the world, it is considered relevant and relevant to the participation of the industrial sector of Mexico-US-Canada. This article is based on an investigation carried out by the authors in which 14,300 weekly risk-return correlations will be analyzed, in order to determine their relationship with the prices of the industrial sector shares of the Mexican Stock Exchange. The results of said statistical analysis of variance (ANOVA) showed that only in some months of 2017 the risk-performance ratio was modified and that each sub-sector of the industrial sector with different values. Likewise, if the financial theory assumes a positive relationship in the risk-return relationship, it has been detected that there are relationships with negative correlation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".