The Dow Jones Precious MetalsIndex and Global Markets
Bibliographic record
Abstract
The study investigated the relationship between the Dow Jones Precious Metal Index (DJGSP) and market indexes of the largest economies of the world, which included the U.S., the U.K., Germany, Sweden, Spain, Brazil, Hong Kong, Australia, Norway, and Canada for the five-year period from April 2007 to April 2012. The multiple correlation coefficient and coefficient of determination (CoD) were calculated to study this relationship. The multiple correlation coefficient measured the relationship between the DJGSP and market indexes, while the CoD indicated the percentage of the variation in the DJGSP that can be explained and accounted for by the market indexes in the regression equation. The multiple regression analysis was performed to study the effect of 10 market indexes on the movement of DJGSP. Results implied that market indexes of 7 out of 10 economies, when used together, better predicted the movements in the DJGSP. It was also found that, when individual market indexes were regressed with DJGSP, the DJGSP was highly correlated with Brazil’s market index and was least correlated with the U.S. market index. As the precious metals index has a very high regression coefficient relative to equity indexes of different economics, the precious metals should not be used to diversify global equity portfolios. TOPICS: Commodities , statistical methods , global , portfolio construction
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".