Bibliographic record
Abstract
The aftermath of the 2008-09 U.S. financial crisis has been characterized by regulatory intervention of unprecedented scale. Although the necessity of a realignment of incentives and constraints of financial markets participants became a shared posterior after the near collapse of the U.S. financial system, considerable doubts have been subsequently raised on the welfare consequences of the Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010 and its various subcomponents, such as the Volcker Rule. The possibility of permanently inhibiting the market making capacity of large banks, with dire consequences in terms of under-provision of market liquidity, has been repeatedly raised. This paper presents systematic evidence from four different estimation strategies of the absence of breakpoints in market liquidity for fixed-income asset classes and across multiple liquidity measures, with special attention given to the corporate bond market. The analysis is performed without imposing restrictions on the exact dating of breaks (i.e. allowing for anticipatory response or lagging reactions to regulation) and focusing both on levels and dynamic latent factors. We report both single breakpoint and multiple breakpoint tests and analyze the liquidity of corporate bonds matched to their main underwriters making markets on those assets. Post-crisis U.S. regulatory intervention does not appear to have produced structural deteriorations in market liquidity.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".