Information Leakage at Population Scale: An Evaluation of the Polymarket Insider-Relevant Subpopulation, 2020-2026

Paper: arXiv 2605.00459

Authors: Maksym Nechepurenko

Abstract

We carry the deadline-resolved Information Leakage Score (ILS-dl) framework of Nechepurenko (2026a, 2026b) from a single-case proof of concept to a population-scale evaluation across 12,708 Polymarket markets, October 2020 to April 2026. We frame the paper as a scope-discovery study: scaling reveals that the framework’s effective domain is materially narrower than initial framing suggested, and the principal obstacle is not score computation but resolution semantics. We report four findings. First, only 88 of 12,708 candidate markets (0.7%) yield computable ILS-dl values; only 1 of 32 markets in the ForesightFlow Insider Cases (FFIC) inventory is in scope, and 14 of 32 FFIC markets are flagged unclassifiable due to genuine resolution-criterion ambiguity. Second, only 12 of the 88 computed markets (13.6%) satisfy anchor-sensitivity, and an independent-second-pass T_event validation reaches 57.8% exact-date agreement, below the 90% ex-ante criterion. Third, raw ILS-dl medians are negative across all six (sub-bucket by period) cells, but a hazard-decay baseline correction we introduce yields a heterogeneous result: regulatory_formal post-2024 shifts to near-zero (-0.21 to -0.02), while regulatory_announcement post-2024 retains a 95% bootstrap CI entirely below zero. Fourth, the constant-hazard exponential of Nechepurenko (2026b) is rejected in favor of Weibull on the pooled post-2024 cell, but a per-subcategory check confirms the preference reflects category mixture rather than within-cell duration dependence. The implication is that detection of informed flow requires methodological refinement on the resolution-typology and score-baseline axes, not only on the score-computation axis where prior work concentrated.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 6.0/10 (capped from 8.0: no out-of-sample test; see penalty flags)
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper combines sophisticated statistical techniques, such as Weibull hazard-decay modeling, Murphy decomposition, and parametric-bootstrap testing, with an exhaustive empirical study involving 12,708 markets and external dataset releases. The focus on structural scope discovery and rigorous data cleaning places it firmly at the intersection of high theoretical complexity and heavy implementation.

Research Flowchart

  flowchart TD
    A[<b>Goal:</b> Scale ILS-dl Framework<br/>to Pop-Scale Evaluation] --> B(<b>Inputs:</b> 12,708 Markets<br/>(Oct 2020–Apr 2026))
    B --> C[<b>Methodology:</b> Resolution Semantics<br/>& Hazard-Decay Baseline]
    C --> D{<b>Computational Filter:</b><br/>Computability & Anchor-Sensitivity}
    D --> E[<b>Finding 1:</b> Limited Scope<br/>(Only 88/12,708 computable)]
    D --> F[<b>Finding 3/4:</b> Hazard Correction<br/>& Distribution Rejection]
    F --> G[<b>Outcome:</b> Methodological Shift:<br/>Resolution > Computation]

Cited References (11)

1. ForesightFlow : An Information Leakage Score Framework for Prediction Markets (2026)
2. Empirical Evaluation of Deadline-Resolved Information Leakage on Documented Polymarket Insider Cases — → 2605.02286
3. From Iran to Taylor Swift : Informed Trading in Prediction Markets (2026)
4. Prediction Market Accuracy: Crowd Wisdom or Informed Minority? (2026)
5. Strategic Bidding Wars in On-Chain Auctions (2025)
6. Federal Court Orders Default Judgment Against Insider Trader on Polymarket Maduro Contract (2026)
7. ForesightFlow Insider Cases (FFIC) Inventory (2026)
8. Polymarket Resolution Typology Dataset (2026)

1 of 11 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.