The Signal Credibility Index for Prediction Markets: A Microstructure-Grounded Diagnostic with Weighted and Time-Varying Extensions
TLDR
This paper formalizes and validates the Signal Credibility Index (SCI) to diagnose information quality in prediction market price movements.
Key contributions
- Introduces a revised persistence component using PR(t,w) for logit prices.
- Develops a weighted Cobb-Douglas SCI form incorporating flow-based concentration.
- Presents a time-varying SCI specification for real-time market monitoring.
- Validates SCI via Monte Carlo simulations, including stress tests and manipulation.
Why it matters
The paper addresses the challenge of distinguishing genuine information from noise in prediction market price moves. By formalizing and validating the SCI, it provides a crucial diagnostic tool for market participants and researchers. This helps assess the credibility of signals, improving market transparency.
Original Abstract
Prediction-market price moves are widely treated as informationally equivalent: a price jump is read the same way regardless of whether it reflects durable Bayesian updating, transient liquidity pressure, strategic position adjustment, or genuine disagreement. This paper formalizes the Signal Credibility Index (SCI) introduced in Nechepurenko (2026) as a stand-alone diagnostic. We make four contributions: (i) a revised persistence component using the persistence ratio PR(t,w) on logit prices, well-defined on short rolling windows; (ii) a weighted Cobb-Douglas form SCI(ααα) with flow-based concentration HHI_flow; (iii) a time-varying specification SCI(t; w) for real-time monitoring; and (iv) Monte Carlo validation including an out-of-distribution stress test, coordinated multi-wallet manipulation, and a logistic-regression benchmark. The validation establishes discrimination among designed microstructure regimes, not external evidence of downstream coordination effects. We document two failure modes consistent with the index targeting coordination credibility rather than pure information content: a Type II error on informed-but-concentrated whale repricing, and a Type I error on coordinated multi-wallet manipulation.
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