ForesightFlow: An Information Leakage Score Framework for Prediction Markets

Paper: arXiv 2605.00493

Authors: Maksym Nechepurenko

Abstract

ForesightFlow is an Information Leakage Score (ILS) framework for detecting informed trading on decentralized prediction markets. For an event-resolved binary market, the score quantifies the fraction of the terminal information move priced in before the public news event. Three operational scope conditions (edge effect, non-trivial total move, anchor sensitivity) are stated as preconditions for interpretation. The score admits a Murphy-decomposition reading that connects label generation to the proper-scoring-rule literature. A pilot empirical evaluation surfaces three findings. First, a resolution-anchored proxy for the public-event timestamp does not separate event-resolved markets from a matched control population (Mann-Whitney p = 1e-6, separation reversed), demonstrating that proxy quality is itself a binding constraint. Second, the article-derived timestamp on a single high-stakes case shifts the score by 0.444 in magnitude relative to the proxy and lies on the opposite side of zero. Third, an audit of the publicly documented Polymarket insider record reveals that documented cases are systematically deadline-resolved, falling outside the original ILS scope (0 of 24 FFIC inventory markets satisfied original scope conditions). This last finding motivates a deadline-ILS extension introduced in Section 7, anchored at the public-event timestamp rather than the news timestamp, and equipped with a per-category exponential hazard baseline for the time-to-event distribution. The extension closes the gap between the methodology and the population in which insider trading has been empirically documented. An end-to-end evaluation of the extension on the 2026 U.S.-Iran conflict cluster is reported in a companion paper. We release the FFIC inventory, the resolution-typology classification of the 911,237-market corpus, and all code at github.com/ForesightFlow.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 9.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper bridges information-theoretic frameworks and Brier score decomposition with massive-scale empirical validation on a corpus of nearly one million markets. It combines rigorous statistical derivation with significant data engineering, including custom microstructure adaptations and the release of an extensive forensic dataset.

Research Flowchart

  flowchart TD
    A[<b>Goal:</b> Quantify Informed Trading via Information Leakage Score (ILS)] --> B[<b>Methodology:</b> Define ILS with 3 Scope Conditions & Murphy-decomposition]
    B --> C[<b>Computational Process:</b> Apply ILS to Prediction Market Data]
    C --> D[<b>Input/Data:</b> 911,237-market corpus (FFIC Inventory)]
    D --> E[<b>Pilot Findings:</b> Proxy limitations & Scope mismatch (0/24 documented cases)]
    E --> F[<b>Outcome:</b> Deadline-ILS Extension & Public Codebase]

Cited References (39)

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