Does the yield curve affect the systemic risk between the stocks of FinTech and traditional finance companies?
Bibliographic record
Abstract
• Impact of yield curve on systemic risk in US financial services sector. • QVAR used to estimate systemic risk. • Level and slope components negatively and significantly affect systemic risk. • Yield curve impact is strongest in normal market conditions. This study explores the effect of yield curve components (level, slope, and curvature) on the return connectedness (systemic risk) between US FinTech stocks and traditional US financial stocks. Quantile connectedness analysis reveals that total connectedness fluctuates over time, particularly reaching high levels during the COVID-19 lockdowns and the 2023 US bank panic, underscoring the substantial impact of global health crises and bank panics. Connectedness tends to be higher but less variable under extreme market conditions than during normal times. The level and slope components of the yield curve negatively and significantly affect total connectedness in both normal and extreme conditions. This suggests that favorable economic conditions reduce systemic risk; however, the strength of these effects varies depending on market conditions. Their impact is most substantial in normal market conditions, with a one-standard deviation rise in the level (slope) reducing systemic risk by 0.77% (1.22%). Conversely, a one-standard deviation increase in economic policy uncertainty most notably raises total connectedness by 2.01% in normal markets. In contrast, a similar increase in five-year expected inflation decreases total connectedness the most, by 2.46% in normal 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".