Comparative Analysis of Gold, Art, and Wheat as Inflation Hedges
Bibliographic record
Abstract
This study confirms gold’s role as a reliable inflation hedge while introducing new insights into lesser-explored assets like art and wheat. Using advanced methodologies such as the ARDL framework and LSTM deep learning, it conducts a detailed analysis of inflation-hedging dynamics, exploring non-linear relationships and unexpected inflation impacts across various asset classes. The findings reveal complex dynamics. Gold demonstrates strong long-term inflation hedging potential. The negative coefficient for the US dollar index suggests that gold acts as a hedge against currency depreciation. Furthermore, a positive relationship between gold returns and inflation during high inflation periods highlights its effectiveness in protecting purchasing power. Art presents a more intricate picture. Long-term analysis suggests a weak mean-reverting tendency, but a negative relationship with inflation, potentially linked to economic downturns. Interestingly, unexpected inflation positively correlates with art returns in the long run, hinting at its potential inflation-hedging abilities. No statistically significant connection between wheat prices and overall inflation was observed; the short-run analysis reveals a dynamic interplay between inflation, real GDP growth, and wheat prices at different time points.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".