极值阿尔法与崩盘风险：通过LLM提取的披露网络区分结构性尾部与彩票式尾部
Extreme Value Alpha and Crash Risk: Separating Structural Tails from Lottery Tails with LLM-Extracted Disclosure Networks
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中文总结 AI 辅助
该研究利用LLM从10-K文件提取的公司披露网络，将股票收益上尾分为崩盘侧与结构性尾部，通过24家科技公司试点验证了崩盘侧信号的有效性，明确了判别器的适用边界。
中文摘要 AI 辅助
股票收益的上尾厚重性具有歧义性：它可以是彩票式尾部（投资者过度为之付费的瞬时跳跃风险，即MAX折价），也可以是结构性尾部（极端赢家出现前经济重构的统计影子）。仅靠收益无法区分二者，因此单纯的尾部热度并非阿尔法信号。本文的判别器是基于公司披露测量的网络：通过可审计的LLM流程从10-K文件中提取的、基于跨度的有向图，其结构变化可分解为边的生成、消亡和漂移。核心符号模式为：尾部热度伴随网络消亡对应崩盘侧；尾部热度伴随完整或正在形成的网络对应结构性尾部与历史赢家所在侧。2014-2025年24家科技公司的试点证据支持崩盘侧结论：上尾热度与消亡质量的交互项可预测负向远期异常收益（月度t值=-2.9；公司 vintage t值=-3.9；Wild聚类p值=0.04；对双向聚类和控制变量具有稳健性），且该组合先于英伟达2018年和2022年的回撤出现。此消亡侧信号可直接用作风险监测的危险标志。阿尔法侧在试点中呈正向方向但不显著，有待验证性测试。对50家随机标普500成分股的预注册复现研究未成功，明确了适用边界：在非连贯生态系统中，披露网络几乎消失（83%的公司 vintage 无消亡质量），因此该判别器仅存在于公司密集记录交易对手的场景中。验证性设计已完全预先设定，包含受管控的主要配对、存档的功效模拟、仅正标签的赢家、经选择校正的基准，以及带有密度门限的冻结生态连贯样本池；仅当门限通过时才会激活。若得到验证，尾部热度将成为区分崩盘风险与结构性赢家的条件信号。
英文摘要
A heavy upper tail in a stock's returns is ambiguous: it can be a lottery tail, transient jump risk that investors overpay for (the MAX discount), or a structural tail, the statistical shadow of an economic reconfiguration that precedes extreme winners. Returns alone cannot separate them, so tail heat alone is not an alpha signal. Our discriminator is the firm's disclosure-measured network: a directed, span-grounded graph from 10-K filings via an auditable LLM pipeline, whose rewiring decomposes into edge birth, death, and drift. The central sign pattern: tail heat with network death is the crash side; tail heat with an intact or forming network is where structural tails and historical winners live. Pilot evidence from 24 technology firms (2014-2025) supports the crash side: upper-tail heat interacted with death mass predicts negative forward abnormal returns (monthly t = -2.9; firm-vintage t = -3.9; wild-cluster p = 0.04; robust to two-way clustering and controls), and the same configuration preceded NVIDIA's 2018 and 2022 drawdowns. This death-side signal is immediately useful as a risk-monitoring danger flag. The alpha side is directionally positive but not significant in the pilot and awaits the confirmatory test. A pre-registered replication on 50 random S&P 500 firms failed, defining the boundary: outside coherent ecosystems the disclosure graph nearly vanishes (83% of firm-vintages have zero death mass), so the discriminator exists only where firms densely document counterparties. The confirmatory design is fully pre-specified, with a gatekept primary pair, archived power simulations, positive-only winner labels, selection-corrected benchmarks, and a frozen ecosystem-coherent universe with a density gate; it activates only if the gate passes. If confirmed, tail heat becomes a conditional signal separating crash risk from structural winners.
