Dynamic Structure of the Global Financial System of Systems
Bibliographic record
Abstract
Purpose: This paper empirically investigates the structural evolution of global financial systems from the system of systems (SoS) view for eleven countries. The financial SoS consists of eleven countries each of which has its own financial system with relative autonomy. The paper aims to provide a prototype of the structural dynamics of the global financial SoS for the eleven financial entities during different phases of the financial markets. Methodology/Approach: The graph-theoretic approach of minimum spanning trees (MST) is applied on two levels to construct the component level of a subsystem within each country and the systemic level of global financial SoS. An SoS can be viewed as a network of networks (NoN) of financial transactions. The statistical approach of principal components analysis (PCA) is also applied to the systemic level of financial SoS among geographic countries to find the driving factor of variance. Originality/Value: This study provides an empirical quantitative measure of systemic risk and applies it to the global SoS to describe the interconnections and linkages. This paper examines the transmission of risks among the components. The structural dynamics of the SoS is expected to be a function of economic cycles including episodes of economic expansion and contraction. Findings: The average distance of component level MST is found to be lower during an economic contraction and higher during an economic expansion. The systemic level MST of global SoS can successfully reflect the geographic as well as the economic relationship between countries. The model verifies the intuition on natural clusters of Germany-France-Italy and the USA-Canada-UK as implied by the tight economic interconnections in each cluster. The result from PCA shows the USA, UK, and Australia experienced a counter movement compared to other European countries during the Euro debt crisis. The Japan financial system contraction and expansion can be explained by other countries indicating that it does not appear to be the driving factor of global SoS over the period of the data sample.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".