A Bibliometric analysis of literature on hedge and safe haven assets
Bibliographic record
Abstract
ABSTRACT We conduct a meta‐literature review of safe haven and hedge assets covering 617 papers published in 170 sources from 1996 to 2022 based on the Scopus database. This review includes a qualitative analysis of the bibliometric content and a quantitative analysis of the citations to identify the primary research streams and offer future research directions. The analysis identifies four research streams in the hedge and safe haven literature: (1) Gold as a hedge and safe haven asset; (2) various models estimating the hedge and safe haven ability of potential assets such as precious metals, crude oil, and cryptocurrencies; (3) Bitcoin as a safe haven asset; and (4) the role of various safe haven assets, particularly Gold and Bitcoin, during the COVID‐19 crisis. The meta‐review also classifies the most influential authors focusing on hedging and safe haven research through co‐authorship and collaborative network analysis. Finally, future research directions are formulated with a wide set of potential research questions and areas. The outcomes of this meta‐review study are useful for researchers, financial analysts, and investors searching for the best safe haven assets during unfavorable market conditions.
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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 | 0.018 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.235 | 0.184 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".