Price as Focal Point: Prediction Markets,Conditional Reflexivity, and the Politics of Common Knowledge

Paper: arXiv 2604.24147

Authors: Maksym Nechepurenko

Abstract

Prediction markets are widely treated as forecasting devices that reveal collective expectations about uncertain futures. This article argues that under specifiable conditions they also function as coordination mechanisms: public probabilities that organize the behavior of voters, donors, journalists, traders, and institutions in ways that can be self-fulfilling or self-defeating. Most existing work asks whether prediction markets forecast accurately; this paper asks whether accurate forecasting is even the right criterion for a market that has become a public coordination device. Drawing on transaction-level evidence from the 2024 U.S. presidential election, we show that the social force of a market signal depends less on its size than on its persistence, the breadth of responding trader types, and cross-platform consensus. We introduce a Signal Credibility Index (SCI) – combining the variance ratio VR(6), a two-sidedness diagnostic, and a trader-concentration adjustment – as a microstructure-grounded criterion for predicting when price moves acquire behavioral traction. Applied to three major 2024 political shocks, the framework reveals that superficially similar events generated qualitatively distinct signal types with different implications for elite coordination. A cross-platform comparison establishes a systematic decoupling of social authority from epistemic robustness: the most visible market produced the least accurate forecasts. The framework carries direct implications for regulating prediction markets as democratic information infrastructure.

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 6.0/10 (capped from 6.5: no out-of-sample test; see penalty flags)
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: While the paper features basic formalization of reflexivity and a custom statistical index (SCI), the core value lies in its pragmatic approach to market microstructure and empirical application to recent election data. It remains largely conceptual rather than deeply quantitative, prioritizing the qualitative analysis of market signals over heavy mathematical derivations.

Research Flowchart

  flowchart TD
    A[<b>Research Goal:</b> How do prediction markets function as coordination mechanisms?] --> B[<b>Data Input:</b> 2024 US Election Transaction-level Data]
    B --> C[<b>Methodology:</b> Microstructure-grounded Analysis]
    C --> D[<b>Computational Process:</b> Signal Credibility Index (SCI)<br/>(VR6 + Two-sidedness + Concentration)]
    D --> E[<b>Analysis:</b> 3 Major Shocks & Cross-platform Comparison]
    E --> F[<b>Findings:</b> Decoupling of social authority from accuracy<br/>& Prediction markets as democratic infrastructure]

Cited References (19)

1. Prediction Market Accuracy in the Long Run (2008) — link
2. The Comovement of Voter Preferences: Insights from U.S. Presidential Election Prediction Markets beyond Polls (2025) — link
3. Prediction Markets? The Accuracy and Efficiency of $2.4 Billion in the 2024 Presidential Election (2025)
4. The Bandwagon Effect in an Online Voting Experiment with Real Political Organizations (2021) — link
5. Interpreting the Predictions of Prediction Markets (2006) — link
6. Price Discovery and Trading in Modern Prediction Markets (2026) — link
7. Prediction Markets as a Public Health Threat (2026) — link
8. Exploring Decentralized Prediction Markets: Accuracy, Skill, and Bias on Polymarket (2025) — link

0 of 19 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.