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AI 根据所提供的论文材料归类:摘要研究预测市场信息泄漏评分的适用性,未直接估计资产收益、定价或尾部风险,现有细分方向没有充分匹配依据。

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总体规模的信息泄漏评估:Polymarket 内幕相关市场,2020—2026

研究问题
将按截止与结算时间定义的信息泄漏评分推广至大量预测市场时,其适用范围、事件时间可靠性和评分基线会遇到哪些限制?
方法与思路
作者在 12,708 个市场检验评分可计算性、锚点敏感性和事件日期一致性,并引入风险率衰减基线,比较指数与 Weibull 模型。
材料陈述的贡献
摘要报告仅 88 个市场可计算评分,事件日期复核低于预设标准;基线修正产生类别差异,强调结算语义与评分基线的限制。
阅读关注点
应核对市场筛选与结算分类、事件日期标注及复核流程,以及基线修正和类别混合如何影响信息泄漏评分的解释。
阅读边界
这是 arXiv 预印本,同行评审与接收未核实;摘要所述评分不能直接证明内幕交易,且可计算样本极小,外推范围受限。

原文摘要

We carry the deadline-resolved Information Leakage Score (ILS-dl) framework of Nechepurenko (2026a, 2026b) from a single-case proof of concept to a population-scale evaluation across 12,708 Polymarket markets, October 2020 to April 2026. We frame the paper as a scope-discovery study: scaling reveals that the framework's effective domain is materially narrower than initial framing suggested, and the principal obstacle is not score computation but resolution semantics. We report four findings. First, only 88 of 12,708 candidate markets (0.7%) yield computable ILS-dl values; only 1 of 32 markets in the ForesightFlow Insider Cases (FFIC) inventory is in scope, and 14 of 32 FFIC markets are flagged unclassifiable due to genuine resolution-criterion ambiguity. Second, only 12 of the 88 computed markets (13.6%) satisfy anchor-sensitivity, and an independent-second-pass T_event validation reaches 57.8% exact-date agreement, below the 90% ex-ante criterion. Third, raw ILS-dl medians are negative across all six (sub-bucket by period) cells, but a hazard-decay baseline correction we introduce yields a heterogeneous result: regulatory_formal post-2024 shifts to near-zero (-0.21 to -0.02), while regulatory_announcement post-2024 retains a 95% bootstrap CI entirely below zero. Fourth, the constant-hazard exponential of Nechepurenko (2026b) is rejected in favor of Weibull on the pooled post-2024 cell, but a per-subcategory check confirms the preference reflects category mixture rather than within-cell duration dependence. The implication is that detection of informed flow requires methodological refinement on the resolution-typology and score-baseline axes, not only on the score-computation axis where prior work concentrated.

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