Decrypting Cryptocurrencies: An Exploration of the Impact on Financial Stability
Bibliographic record
Abstract
This study aims to empirically examine the relationship between cryptocurrency and various facets of the financial system. It seeks to provide a comprehensive understanding of how cryptocurrencies interact with, and influence, the stock market, the U.S. dollar’s strength, inflation rates, and traditional banking operations. This is carried out using linear regression models, Granger causality tests, case studies, including the collapse of the Futures Exchange (FTX), and the successful integration of Binance. The study unveiled a strong positive correlation between cryptocurrency market capitalization and key financial indicators like the Dow Jones Industrial Average, Consumer Price Index, and traditional banking operations. This indicates the growing significance of cryptocurrencies within the global financial landscape. However, a mild association was found with the U.S. dollar, suggesting a limited influence of cryptocurrencies on traditional fiat currencies currently. Despite certain limitations such as reliance on secondary data, methodological choices, and geographic focus, this research provides valuable insights for policymakers, financial industry stakeholders, and academic researchers, underlining the necessity for continued study into the complex interplay between cryptocurrencies and financial stability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".