Do Syndicated Loans Influence Systemic Risk? An Empirical Analysis of the Canadian Syndicated Loan Market
Bibliographic record
Abstract
This study examines the impact of syndicated loans on individual and national diversity of loan portfolios of the six largest Canadian banks, as well as their marginal contribution to Canadian systemic risk. To test the marginal impact of Canadian syndicated loans on risk, we use a simulation of fictitious loan portfolios to create benchmarks based on syndicated loans along with Hirschman-Herfindahl index and Euclidian distance measures. Our results show that syndicated loans have a positive impact on industrial diversity for each bank's loan portfolio. Empirical results also suggest that lenders¡¯ involvement in industries in which they lack expertise tends to develop a more homogeneous and concentrated national loan portfolio. Finally, we conclude that the homogenization of loan portfolios and the concentration of the national loan portfolio have an ambiguous impact on Canadian systemic risk. Specifically, the impact depends on the systemic risk measure and an industry effect is present.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".