DESEMPEÑO DE LOS MERCADOS ACCIONARIOS DESARROLLADOS DURANTE LA CRISIS GRIEGA (Developed Stock Markets Performance During the Greek Crisis)
Bibliographic record
Abstract
En este articulo investigamos empiricamente el desempeno de los mercados accionarios desarrollados (de acuerdo a la clasificacion de MSCI) durante la reciente crisis griega, cuyo inicio data desde comienzos de Octubre de 2009. El desempeno se mide a traves del alfa de Jensen. Como modelo generador de retornos accionarios de equilibrio se usa un CAPM internacional con el World Index como portafolio de referencia para los inversionistas. Los resultados muestran que los mercados accionarios de Italia, Espana y Grecia .fueron los mas expuestos a destruir riqueza en el periodo de la crisis. Por el contrario, el mercado accionario de USA presenta un desempeno superior mientras que los mercados de Canada, del area Pacifico e Israel no presentan un desempeno ajustado por riesgo que sea estadisticamente diferente al mostrado por el World Index. Se concluye que existe la necesidad de que los paises de la Eurozona con un excesivo endeudamiento publico encuentren los espacios para una mayor disciplina entregando al sector privado los incentivos para generar una mayor productividad economica a objeto de superar los episodios de esta crisis que han sido transmitidos a los restantes mercados accionarios Europeos independiente de su tamano.This paper investigates empirically the performance of developed stock markets (according to the classification of MSCI) during the recent crisis in Greece, which started in early October 2009. Performance is measured by Jensen's alpha. An international version of the CAPM is used as the equilibrium model, while the World Index is used as the benchmark portfolio for investors. The results show that stock markets in Italy, Spain and Greece were the most exposed to value destruction during the crisis period. By contrast, the U.S. stock market had superior performance while the markets of Canada, the Pacific area and Israel do not present a risk-adjusted performance statistically different from that shown by the World Index. We conclude that eurozone countries with excessive public debt to GDP should be able to create the spaces for a greater fiscal discipline and provide incentives to private sector with the objective to generate greater economic productivity to overcome this kind of crisis which negatively have affected to other European stock markets regardless of their size.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".