Market Freeze and Recovery: Trading Dynamics under Optimal Intervention by a Market-Maker-of-Last-Resort
Bibliographic record
Abstract
We study the trading dynamics in a distressed asset market with search frictions. When trading of a financial asset ceases due to an adverse selection problem, a large player can resurrect the market by buying up bad assets which involves assuming financial losses. The player can, however, delay the intervention: a mere announcement today of intervening at a later point in time can cause markets to function again. This announcement effect gives rise to a trade-off between the size and the timing of the intervention. The optimal intervention involves balancing the financial losses from the intervention and the social cost of illiquid markets. If the losses are small and a market is deemed important, it is optimal to ensure that the market functions continuously. In this case, there is a fixed cost associated with intervention delay, making it optimal to intervene as early as possible at the minimum size. As losses increase and the importance of the market declines, the intervention is optimally delayed and it can be optimal to rely on the announcement effect by increasing the size of the intervention. Furthermore, our finding highlights the importance of search friction in the formation of market distress, the determination of policy announcement effect, and the optimal design of intervention. The views expressed in this paper are not necessarily the views of the Bank of Canada. 1 1
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".