The Signal Credibility Index for Prediction Markets: A Microstructure-Grounded Diagnostic with Weighted and Time-Varying Extensions

Battle Count: 56

The paper is primarily about prediction market microstructure diagnostics rather than traditional quantitative trading. However, it has moderate relevance: (1) prediction markets are increasingly used as trading venues and information sources; (2) the SCI framework for distinguishing informed updating from liquidity pressure and manipulation is conceptually transferable to traditional market microstructure analysis; (3) the persistence ratio and flow-based concentration measures could inform signal quality assessment in algorithmic trading; (4) the multi-wallet clustering protocol is relevant for detecting manipulation in crypto/DeFi trading. The paper does not directly address portfolio optimization, alpha generation, or execution strategies. The coordination-credibility framing is more relevant to event-driven trading and political/economic forecasting than to traditional quantitative trading.

The core SCI computation is straightforward (three components multiplied together on logit-transformed prices). However, practical implementation requires: (1) on-chain ledger reconstruction for trader-level signed flows (non-trivial for Polymarket/Polygon), (2) aggressive volume classification via Lee-Ready tick rule when order books are unavailable, (3) multi-wallet clustering protocol with four steps (common funder, temporal co-movement, custodial filter, graph community detection), (4) proper logit transformation and numerical clipping, (5) rolling-window management for time-varying SCI, (6) bootstrap inference for confidence intervals. The data pipeline for blockchain-based prediction markets is the primary complexity driver. The algorithm itself (Algorithm 1) is well-specified and implementable in ~200-500 lines of Python.

The paper provides a GitHub repository (https://github.com/ForesightFlow/signal-credibility-index), a fixed random seed (20260429), full DGP specifications in Appendix A, and detailed algorithm pseudocode. All simulation parameters (Gamma shape-scale, Dirichlet concentration, AR(1) coefficients, trader pool sizes) are explicitly stated. However, the illustrative application to 2024 election shocks uses simulation-derived values rather than actual on-chain data, and the real-world validation dataset (labeled downstream coordination responses) does not yet exist. The multi-wallet clustering protocol is described but not fully implemented in the provided code.