The Impact of COVID-19 Pandemic on Gold Price: From Ultra-Short-, Short- and Medium-Term Perspectives Respectively
Bibliographic record
Abstract
As the coronavirus pandemic spreads, it is not just people's health is threatened, but the financial market as well. In particular, policies such as segregation, embargoes, etc. broke the relationship between supply and demand in the market and the depressed economy caused investors to lose confidence. The fear of infection during the COVID-19 pandemic led investors to favor the purchase of financial assets with safe-haven and hedging properties, such as gold. In this article, the daily, weekly, and monthly prices of gold per troy ounce in US Dollars from January 1, 2010, to February 24, 2022, were extracted and analyzed. Three ARIMA models were applied in the study to predict the gold prices based on the assumption of no pandemic exists. And comparing the fitted values with the actual ones. The study analyzes the impact of Covid-19 on gold price performance from three perspectives. An ultra-short-term analysis is from January 24 to February 6 based on the model built by the daily dataset. A short-term one from January 24 to March 8 according to the weekly constructed model. And a medium-term comparison from January 2020 to July 2020 with the monthly dataset. Additionally, the possible reasons to explain the result are provided to help different investors make decisions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".