BITCOIN FİYATININ ALTIN VE HAM PETROL FİYATLARI İLE İLİŞKİSİNİN ANALİZİ
Bibliographic record
Abstract
In recent years, the usability of cryptocurrencies in purchasing goods and services has been discussed in all developed and developing countries. At the same time, cryptocurrency is increasingly acting as an investment vehicle and speculative trading vehicle. An example of such use is the bitcoin cryptocurrency. Bitcoin was the first cryptocurrency to gain value without being an ordinary commodity that initially satisfies needs and carries no collateral in the form of existing currencies. One of the most discussed topics in the finance literature is the investigation of the factors affecting the price of bitcoin. The aim of this study is to investigate the relationship between Bitcoin, a crypto currency, and gold and crude oil prices. In the study, weekly data between January 2019 and August 2021 were examined. Vector Autoregressive Model (VAR) was used to examine the relationship between the variables and the direction of the relationship between the variables was determined by the Granger causality test. According to the results of the VAR model, gold and crude oil prices have significant effects on bitcoin prices, but bitcoin price does not have a significant effect on gold and crude oil prices. On the other hand, the results of the Granger causality test confirm the findings of the VAR analysis. Granger causality results reveal that both gold price and crude oil price are the Granger causes of bitcoin price.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".