Philipp D. Dubach
arXiv 5 Oct 2026 · Finance — Trading
arXiv:2610.06412 · PDF · Extracted main text
Trade-sign errors can change measured trading costs even when classification accuracy is high. We validate the side of Polymarket's public trade prints against the taker leg of each print's on-chain settlement. On twelve selected days between April and August 2026, spanning both exchange generations, 92.1% to 100.0% of prints match a settled taker leg, with exact side and token agreement on all 24.5 million matched pairs. Mint-and-merge settlement makes that taker leg essential: pooling maker and taker legs changes the measured buy share. Signing the full cached tape before settlement selection yields 16.6 million prints signed by every rule; equal-day balanced accuracy is 0.942 for Lee-Ready, 0.768 for the tick test and 0.670 for retrospective bulk volume classification. On 16.4 million identical eligible fills, Lee-Ready raises effective spread by 0.429 cents per share under equal-fill weights; its realised-spread difference is -0.209 cents. Under share-volume weights, taker five-minute midpoint impact is 0.689 cents, while tick and bulk classifications give -0.374 and -0.119 cents. That aggregate sign reversal disappears when tied print rows are excluded. Distortion depends on error-weighted signed outcomes, sample selection and weighting. Receipt-time ordering and unobserved future-quote age limit these delivered-quote accounting quantities; they do not identify causal impact or private information. Supporting venue and collector analyses provide descriptive diagnostics.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | David Easley, Marcos M. López de Prado, and Maureen O'Hara (2016) Discerning information from trade data | 0.737 | 3 | 3 | 67% |
| 2 | Katrina Ellis, Roni Michaely, and Maureen O'Hara (2000) The accuracy of trade classification rules: Evidence from Nasdaq | 0.737 | 3 | 3 | 67% |
| 3 | Bidisha Chakrabarty, Roberto Pascual, and Andriy Shkilko (2015) Evaluating trade classification algorithms: Bulk volume classification versus the tick rule and the Lee–Ready algorithm | 0.644 | 2 | 2 | 100% |
| 4 | Philipp D. Dubach (2026) The anatomy of a decentralized prediction market: Microstructure evidence from the polymarket order book, 2026a self | 0.644 | 2 | 2 | 100% |
| 5 | Simon Jurkatis (2021) Inferring trade directions in fast markets | 0.644 | 2 | 2 | 100% |
| 6 | Elizabeth R. Odders-White (2000) On the occurrence and consequences of inaccurate trade classification | 0.644 | 2 | 2 | 100% |
| 7 | Boka Qin and Rui Yang (2026) Polymarket-v1 database, 2026 | 0.644 | 2 | 2 | 100% |
| 8 | Yiming Shen, Yuhan Jin, Shuohan Wu, Yanlin Wang, and Jiachi Chen (2026) The ghosts of Polymarket: When off-chain matches meet on-chain reverts, 2026 | 0.644 | 2 | 2 | 100% |
| 9 | Charles M. C. Lee and Mark J. Ready Inferring trade direction from intraday data | 0.511 | 2 | 2 | 50% |
| 10 | PMXT (2026) Polymarket orderbook archive (v2): data overview | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 43 scored citations.