Per-Market Information Leakage and Order-Flow Skill: Two Methodological Lenses on Informed Trading in Decentralized Prediction Markets

Paper: arXiv 2605.02287

Authors: Maksym Nechepurenko

Abstract

April 2026 saw notable methodological convergence in the academic study of informed trading on decentralized prediction markets. Three approaches surfaced almost simultaneously: Mitts and Ofir (2026) apply a composite screen to over 210,000 wallet-market pairs; Gomez-Cram et al. (2026) apply an event-level sign-randomization test to Polymarket’s complete transaction history, classifying 3.14% of accounts as “skilled winners” and separately flagging 1,950 accounts as “insiders” via a lifecycle heuristic; Nechepurenko (2026) develops the Information Leakage Score (ILS) framework, which quantifies per-market information front-loading at an article-derived public-event timestamp. This paper provides a methodological comparison. The central claim is that these are three distinct layers of detection, not competing methods on a single layer. Sign-randomization is best understood as an account-level test of persistent directional skill conditional on opportunity selection – not a direct test of insider trading, and not a per-market measure. The heuristic insider flag is separate from the skill classifier, applies to a population the classifier excludes by design, and has unknown precision. The Polymarket sample pools politics, sports, crypto, and other categories with different information technologies, so a platform-wide “skilled winner” classification is mechanism-ambiguous. The January 2026 U.S.-Venezuela operation cluster, where the DOJ indictment of Master Sergeant Gannon Van Dyke provides a rare external enforcement benchmark, illustrates how the layers stack: lifecycle heuristics identify suspicious accounts; legal investigation addresses non-public-information possession; per-market scoring would quantify how much information was leaked into each contract. A combined pipeline gains in precision because each layer filters a different dimension.

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper provides a high-level, practical methodological framework for market surveillance, relying on empirical datasets and real-world legal benchmarks rather than novel theoretical derivations. While it employs statistical concepts like sign-randomization and proper-scoring rules, the focus is squarely on implementation, taxonomy, and the pragmatic integration of detection signals for compliance purposes.

Research Flowchart

  flowchart TD
    A[Research Goal: Decode Informed Trading in Decentralized Markets] --> B[Data Inputs: Global Market/Wallet History & DOJ Benchmarks]
    B --> C[Methodological Layers]
    C --> D[1. Event-level Sign-Randomization (Skill)]
    C --> D2[2. Lifecycle Heuristics (Suspicious Accounts)]
    C --> D3[3. Info Leakage Score/ILS (Per-market Front-loading)]
    D & D2 & D3 --> E[Findings: Multi-layered Pipeline for Precision]
    E --> F[Conclusion: Layers are complementary, not competing]

Cited References (43)

1. Prediction Markets (2004)
2. The Promise of Prediction Markets (2008)
3. The Wisdom of Crowds (2004)
4. Combinatorial Information Market Design (2003)
5. Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation (2007)
6. Distilling the Wisdom of Crowds: Prediction Markets vs.\ Prediction Polls (2017)
7. On the Impossibility of Informationally Efficient Markets (1980)
8. Trading volume statistics (2024)

2 of 43 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.