CASSH: serà mais um bloco econÃmico?
Bibliographic record
Abstract
This working paper analyzes the financial integration level of the recent CASSH\nacronym formed by the countries Australia, Canada, Hong Kong, Singapore and\nSwitzerland, which have strong economies and homogeneous profiles in a social,\ndemographic and financial context, being classified by UN as countries with very high\nhuman development. By analyzing the presence of common trends and cycles\nassociated to the market indices most representative of CASSH stock exchanges,\nduring the period between January 1998 and November 2010, using the\nmethodological technique developed by Vahid and Engle (1993), it is evident that,\nduring global economic stability periods the stocks of these economies are more\ninfluenced by trends than by cycles, being determined more by economic\nfundamentals, while in the crisis periods there is a higher influence of cycles,\nassuming the financial risk factors greater relevance in the composition of the indices\nreturns. It is possible to identify that financial markets analyzed have distinct longterm\nscenarios governed by four common trends, two leading to a positive trajectory,\none to a pessimistic scenario and the other negative initially, but after the 2007 crisis,\nit recovers following a positive trajectory. It is notorious that they react differently to\nshort-term shocks and with different intensities, mainly due to the behavior of\nCanadian cycle that correlates negatively with the others and with the common cycle.\nThrough Granger causality test, the common trend pessimist can only be provided by\nthe Swiss index, while the index of Hong Kong appears as the only one able to\npredict the common cycle.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".