The Small World of Canadian Capital Markets: Statistical Mechanics of Investment Bank Syndicate Networks, 1952–1989
Bibliographic record
Abstract
Abstract We investigate the structure of investment bank syndicate networks in Canada. We consider two banks to be connected if they have participated in an underwriting syndicate together, and construct networks of such connections using data drawn from the Record of New Issues (Financial Data Group). We show that these interfirm networks form “small worlds”, in which banks are both locally clustered and globally connected by short paths of intermediate banks, and are “scale free”, in which the connectivity of the network is highly skewed and with most banks tied to a small set of prominent banks. We examine changes over time in the network's small‐world and scale‐free properties, and demonstrate their theoretical and practical implications for the structure and operation of Canadian capital markets by linking these properties to the network's cliquey‐ness, resilience, and speed of information transmission. Résumé Cette étude porte sur la structure des réseaux que for‐ment les syndicats d'émission des banques d'investissement au Canada. Nous posons que deux banques sont liées si elles ont participé ensemble à un syndicat d'émission, et nous retraçons les réseaux de liens en utilisant des données extraites du Record of New Issues (Financial Data Group). Nous montrons que ces réseaux interorganisationnels (RIO)forment des petits mondes dans lesquels les banques sont à la fois localement regroupées et mondialement reliées par des courts chemins de banques intermédiaires. Les RIO sont également sans échelle (scale free): la connectivité dans le réseau est fortement inégale et la plupart des banques sont liées à un petit nombre de banques dominantes. Nous examinons l'évolution des propriétés de petit monde et d'absence d'échelle du réseau et mettons en Evidence leurs implications théoriques et pratiques pour la structure et le fonctionnement du marché canadien des capitaux en reliant ces propriétés aux caractères de clique, de résilience et de vitesse de transmission de l'information du réseau.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".