The Application of Gold Price, Interest Rates and Inflation Expectations in Capital Markets
Bibliographic record
Abstract
The aim of this research is to determine a forecasting model of the price of gold in relation to the rate of interest from 1971–2013 that would benefit wealth managers in their forward interpretation of capital market expectations. It is not a model for market makers, since the price-setting dominance of banks in the physical as well as derivative markets presents a problem for any economic agent participating in these markets. Nonetheless, the ability to understand the variability of gold, interest rates and prices would clearly enhance financial planning and investor performance. This research models a full population of the price of gold with the rate of interest, in order to assess what impact a change in the interest rate would have on a change in the gold price (and vice versa). In developing a model price of gold that is strongly correlated with the actual price, the outcome of the research expects to show that not only is the interest rate and the gold price manipulated in relation to each other, but would also affirm the Gibson’s Paradox, that real gold is inversely related with the real interest rate, so that real prices are positively related with the real interest rate.
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 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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".