Liquidity and the Threat of Fraudulent Assets
Bibliographic record
Abstract
We study an over-the-counter (OTC) market with bilateral meetings and bargaining where the usefulness of assets, as means of payment or collateral, is limited by the threat of fraudulent practices. We assume that agents can produce fraudulent assets at a positive cost, which generates endogenous upper bounds on the quantity of each asset that can be sold, or posted as collateral in the OTC market. Each endogenous, asset-specific, resalability constraint depends on the vulnerability of the asset to fraud, on the frequency of trade, and on the current and future prices of the asset. In equilibrium, the set of assets can be partitioned into three liquidity tiers, which differ in their resalability, their prices, their sensitivity to shocks, and their responses to policy interventions. The dependence of an asset's resalability on its price creates a pecuniary externality, which leads to the result that some policies commonly thought to improve liquidity can be welfare reducing.
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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 | 0.018 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".