Does the yield curve affect the systemic risk between the stocks of FinTech and traditional finance companies?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it