Bibliographic record
Abstract
Exchange Traded Funds (ETF) are likely the most successful financial products since the 2008 global financial crisis (GFC).Despite numerous benefits, ETF's success could be making some asset managers "too interconnected to fail."Interconnection is a core element of systemic risk, and it played a material role in the transmission of economic shocks in the GFC.This article is the first, in a growing body of literature on ETFs, to provide a comprehensive inquiry into their systemic importance through the lens of interconnectivity.The article provides three unique contributions.First, it shows how ETFs are creating deep and complex interconnections between numerous market participants and service providers, extending to retail and institutional investors, and corporate behaviors and decisions.Second, it illustrates how ETF interconnection creates direct and indirect systemic risk transmission pathways, with unique factors not present in other managed asset products, like the reliance on key market-incentivized intermediaries in a crisis, crowd behaviors from correlated investment exposures, information cascades, runs, fire sales, and non-linear impacts.Finally, it shows how the effective monitoring of ETF systemic risk requires a cross-market analysis to assess the collective behaviors of numerous participants in a complex and interconnected operating ecosystem, and how both activity and entity-level oversight is prudent in this market.While ETF firms are distinct from banks and insurance companies, there's merit in safeguarding large firm's economic resilience given their centrality in a highly interconnected ecosystem.As such, ETFs illustrate the importance of considering financial markets as a "system" when designing supervisory * BA (Honors, First Class), LLB (Distinction), LLM (Magna Cum Laude), SJD Candidate (Duke)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".