The Long-Term Relationship between Nasdaq Index and Gold Yield: Analysis Based on ARMAX Model
Bibliographic record
Abstract
This article explores the relationship between the gold yield and the Nasdaq index through a detailed understanding of the gold yield and the changes in the Nasdaq index in the past ten years. Will the gold yield be affected by the Nasdaq index, whether there is a correlation between the two, and whether the gold price can be predicted based on the Nasdaq index. In this paper, the ARMA model is used to rank the gold return rate, and four models are used, namely the gold return rate, the gold return rate plus the Nasdaq index, the gold return rate plus the Nasdaq index lagging 1 period and lagging 2 Period to analyze and finally explore the relationship between the two. This paper mainly finds that the return rate of gold is positively correlated with the Nasdaq index, but the prediction efficiency of the model is low. Gold is an effective return to crisis industries. When the stock market is not friendly, gold can play a very good hedging role. This article uses the influential and extensive data of the Nasdaq index to let everyone know how to analyze the yield of gold through existing data. In short, it is when to buy and sell gold. This paper suggests that although the model shows that the prediction effect of the Nasdaq index on the gold return rate is not significant, the two show a positive correlation, and it has a significant effect in the lag period 1 and 2. It is recommended to use the Nasdaq index Investors who watch gold yields give priority to watching lag 1.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".