Why Can't I Trade? Exchange Discretion in Calling Halts*<sup>,</sup><sup>†</sup>
Bibliographic record
Abstract
ABSTRACT Stock exchanges are important intermediaries in how firm information enters price. Trading halts are a key tool, often exercised at the exchanges' discretion, to prevent extraordinary price volatility when new information arrives. We investigate how exchanges use discretion and whether the discretion alters the effectiveness of the halts. We provide evidence consistent with halts reflecting the preferences of listed firms rather than the stated exchange objectives (i.e., minimizing excess volatility and off‐equilibrium trades). Furthermore, when exchanges exercise more discretion (unexplained by firm and information characteristics), the halts are less effective. Specifically, halts with more discretion are less likely to resume trading with efficient prices and are more likely to have been called unnecessarily (i.e., little to no price movement during the halt). These findings are consistent with exchanges using halts to cater to listed firms rather than to meet exchange objectives such as minimizing excess volatility or avoiding trades at off‐equilibrium prices.
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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 | 0.006 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".