New Funds, Familiar Fears: Are Exchange Traded Funds Making Markets Less Stable? Part II – Interaction Risks
Bibliographic record
Abstract
Exchange Traded Funds (ETFs) – tradeable investments that provide a return linked to an underlying index or basket of assets – are likely the most successful financial product since the 2008 crisis. Over the last decade they’ve experienced remarkable growth. Yet these products may also be making the financial system less stable and, like Wall Street innovations of the past, connecting banks and main street with dangerous implications. This final article – of a two-part study on ETF risks – posits that these products may be introducing two “interaction risks” into financial markets due to a complex operating and trading ecosystem. First, ETFs could create information cascades, facilitate investor herding, and financial contagion. Second, ETFs could be distorting the informational efficiency of underlying asset and securities prices, and disincentivizing active price discovery, in a way that masks market risk. This article builds on its predecessor, which showed how ETFs could create a fragile “illusion” of liquidity, since financial intermediaries, in a crisis, often act unpredictably and pursue discretionary incentives. The combined study compliments prior work on financial market systemic risk by analogizing ETF interaction risks to prior crises – particularly 2008. Given the comparisons, the ETF market’s continuing growth and interest by retail investors, institutions, and pensions, regulatory and academic attention should be increased to ensure risks are both understood and appropriately mitigated. This article introduces several areas where heightened focus is warranted.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".