剔除来源变量与状态门控股票因子预测
文章 arXiv papers · 作者: Chorok Lee
总结
本审计考察如何从状态门控预测中移除预测变量。删除某个变量的滞后输入，并不等同于在完整的拟合、滤波和调参过程中排除该变量，因为它也可能影响估计得到的状态门。研究人员重建了从 HML 到 SMB 的预测关系，并将分析扩展至四个区域面板中的 30 个因子方向，采用合并预测和隐马尔可夫模型预测。他们还比较了共享状态门下的消融与完整排除来源变量，并通过模拟区分底层预测信息与模型拟合带来的成本。
报告的证据有好有坏，总体上无法得出定论：重建的关系呈现负的均方误差增量收益；而消融与完整排除在 240 项比较中的封顶损失点估计符号有 42 项相反。在同时置信程序下，2,400 个时间端点均无法确定方向。即使预言机信号有益，拟合后的 HMM 仍会出现损失。市场间相关性、回溯性数据版本、有限的推断分辨率，以及未经核实的外部敞口，都限制了确认性结论。
核心观点
- 来源变量既会影响预测系数，也会影响估计的状态门，因此只移除其滞后输入并未将其完整排除。
- 本审计比较了共享状态门消融与在拟合、滤波和调参全过程中排除来源变量。
- 报告的金融比较得出相互矛盾的点估计方向，且没有任何时间端点能够确定方向。
- 模拟显示，拟合成本可能超过预言机模型可利用的正向预测信息。
- 市场间相关性和回溯性数据版本限制了确认性结论的力度。
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# Source Exclusion in Regime-Gated Forecasting: A Cross-Market Audit of Equity-Factor Predictability # Source Exclusion in Regime-Gated Forecasting: A Cross-Market Audit of Equity-Factor Predictability A source variable can enter a forecast through both lag coefficients and an estimated regime gate. Removing its lag block therefore differs from excluding its information throughout fitting, filtering and tuning. We audit this distinction in equity-factor forecasting. A current-vintage reconstruction of the reported HML-to-SMB relation finds negative incremental MSE gains for pooled, Gaussian-HMM and Student-t-HMM forecasts in 2010-2024. An expansion evaluates all 30 factor directions in four regional panels, with 2025-August 2026 withheld from earlier repository forecast evaluation. Shared-gate ablation and complete exclusion give opposite capped-loss point-estimate signs in 42 of 240 unweighted temporal comparisons. None of 2,400 temporal endpoints resolves a direction under a simultaneous confidence-sequence construction. All 776 intervals within a declared score-resolution margin are explained by forecast proximity. Controlled simulations separate oracle information from fitting costs and show fitted HMM losses despite positive oracle information. A matched Gaussian-versus-Bernoulli channel intervention improves forecasts at one corruption level under known-AR residualization, without identifying the earlier pipeline failure or a financial mechanism. Application-budget and learned-pipeline diagnostics expose poor resolution of known positive targets. Independent implementations replay archived forecasts and recompute all 4,800 primary financial interval summaries. The evidence demonstrates sensitivity to source removal and limited inferential resolution. Correlated markets, retrospective data vintages and unverified outside exposure limit confirmation claims.
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