When Bigger is Better: The Impact of a Tiny Tick Size on Undercutting Behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Economically insignificant tick sizes encourage undercutting behavior, thus harming market quality. Theoretical work shows that increasing tick sizes in unconstrained markets reduces undercutting and improves market quality. Equity market pricing grids are generally too coarse to test this prediction. We examine a cryptocurrency market with infinitesimal tick sizes where undercutting limit orders acquire price priority without meaningful economic cost. We show that increasing tick sizes reduces undercutting behavior, increases liquidity provision and quoted depth, and reduces transaction costs for institutional and retail-sized trades while decreasing short-term volatility. Tiny tick sizes are suboptimal, supporting increased minimum trading increments in tick-unconstrained markets.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it