发表机构
Fudan University（复旦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出注入真值协议与随机采纳对照，检验定量策略研究中准入阈值的有效性，发现其在弱信号下消除虚假发现但采纳率低，强信号下无增益，且绝对收益标准会误拒所有候选。
AI 中文摘要
策略研究混淆了两个问题：寻找一个有利可图的规则，以及确定该发现并非搜索运气所致。后者需要准入阈值——在采纳结论之前必须满足的统计标准——然而这些阈值是否有效，以及其代价如何，仍未得到检验。我们引入了一种注入真值协议，并设置随机采纳对照组，该对照组以与阈值相同的比率采纳；只有当阈值胜过该对照组时，它才携带信息，而不仅仅是提高门槛。在合成面板和真实校准面板中，阈值在弱信号区域消除了虚假发现，但将采纳率降至1%至7%，而在强信号时则毫无增益。最重要的是，基于绝对收益而非超额收益计算的标准会静默拒绝所有候选，包括真实信号。关键词：多重检验，回测过拟合，策略准入，注入真值验证，超额收益，虚假发现率。
英文摘要
Strategy research conflates two problems: finding a profitable rule, and establishing that the finding is not search luck. The latter calls for admission gates -- statistical criteria that must be satisfied before a conclusion is adopted -- yet whether gates work, and at what cost, remains untested. We introduce an injected-truth protocol with a random-admission control that adopts at the same rate as the gate; only if the gate beats this control does it carry information rather than merely raise a threshold. Across synthetic and real-calibrated panels, gates eliminate false discoveries in the weak-signal regime but cut adoption to 1--7%, and add nothing when signals are strong. Most importantly, criteria computed on absolute rather than excess returns silently reject every candidate, including true signals. Keywords: multiple testing, backtest overfitting, strategy admission, injected-truth validation, excess returns, false discovery rate
Comments12 pages, 3 figures, 7 tables. Code and data to reproduce every result: https://github.com/simplify23/quant-trading-agent
