Comparing the Efficiency and Similarity Between WTI, Fiat Currencies and Foreign Exchange Rates
Bibliographic record
Abstract
The complex dynamics of financial asset prices play a pivotal role in the global economy and consequently in the life of the people. Thus, this research encompasses a systematic analysis of the price dynamics of the financial assets considering simultaneously four critical attributes of the financial market (disorder, predictability, efficiency and similarity/dissimilarity). We explore these essential attributes of the financial market using the permutation entropy ([Formula: see text]) and Fisher Information measure ([Formula: see text]), and cluster analysis. Primary, we use the values of the information theory quantifiers to construct the Shannon–Fisher causality plane (SFCP) allows us to quantify the disorder and assess the randomness exhibited by these financial price time series. Bearing in mind the complexity hierarchy, we apply the values of [Formula: see text] and [Formula: see text] to rank the efficiency of these financial assets. The overall results suggest that the fiat currencies of developed countries, such as the Canadian dollar (CAD), British pound (GBP), and Norwegian krone (NOK), display higher disorder, lower predictability, and higher efficiency than other financial assets such as Crude oil (WTI) and Foreign exchange rates. Also, the cluster analysis provided by the K-means and the Hierarchical cluster techniques grouped these financial assets into only three distinct groups. We conclude that an oligopolistic market structure drives the WTI. At the same time, the other financial assets are characterized by atomized markets.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".